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			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. 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In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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				"URL_UPDATE"
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			"issued": "2020-07-02T08:58:25.000Z",
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			"title": "Current Forest fragmentation",
			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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				"fronts",
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				"change",
				"fragmentation",
				"URL_UPDATE"
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			"issued": "2020-07-02T08:58:25.000Z",
			"modified": "2026-10-23T07:29:46.000Z",
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			"title": "Primary Humid Forest",
			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
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				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=ef56ea768f354941ae20e74a4458e37d&sublayer=5",
			"landingPage": "https://globil.panda.org/maps/panda::forest-2017",
			"title": "Forest (2017)",
			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
			"keyword": [
				"deforestation",
				"fronts",
				"forest",
				"change",
				"fragmentation",
				"URL_UPDATE"
			],
			"issued": "2020-07-02T08:58:25.000Z",
			"modified": "2026-10-23T07:29:46.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-180.0000,-59.0565,-179.9979,74.6429",
			"license": "Publication must acknowledge WWF",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::forest-2017"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://wwf-sight-maps.org/arcgis/rest/services/wwf_data_transfer/Global_Forest_Analysis/MapServer/5"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/csv?layers=5"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/shapefile?layers=5"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/geojson?layers=5"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/kml?layers=5"
				}
			],
			"theme": [
				"geospatial"
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		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=ef56ea768f354941ae20e74a4458e37d&sublayer=6",
			"landingPage": "https://globil.panda.org/maps/panda::forest-2000",
			"title": "Forest (2000)",
			"description": "<div>To advance our understanding of forest cover changes, given the discrepancies, this work provides</div><div>an original analysis by assessing five available remote sensing datasets (ALOS PALSAR forest and non-</div><div>forest data, ESA CCI Land Cover, MODIS IGBP, Hansen/GFW on global tree cover loss, and Terra-I) to</div><div>estimate the likely extent of current forests (circa 2018) and forest cover loss from 2001-2018, for</div><div>which data was available. This assumes that no single approach or data source can capture major</div><div>trends everywhere; therefore, an all-available data approach is needed to overcome shortcomings of</div><div>individual datasets. The main shortcomings of this approach, however, are that it does not account for forest gains, tends to</div><div>underestimate the conversion in dry forests ecosystems and lacks explicit assessment of</div><div>uncertainties across the different datasets.</div><div><br /></div><div>“Forest cover loss” in the all-available data analysis consists of observations (pixels) changing from</div><div>forest to non-forest at any time during 2000 to 2018. The spatial resolution chosen was 250m given</div><div>the original resolutions of the datasets incorporated and on the understanding that forest areas</div><div>should be a minimum of 250 x250m (6.25 ha) to contain the functional attributes of a forest (e.g.</div><div>species distribution, ecology, ecosystem services), rather than depicting individual trees or groups of</div><div>trees.</div><div><br /></div><div><div>According to our analysis, about 20% of total forest cover loss takes place in core forest, which we</div><div>label “primary forest loss”, while the remaining 80% results from the conversion of edge and</div><div>patched forests, which is labelled as “secondary forest loss”. Two thirds of total forest cover loss in</div><div>the period from 2000-2018 occurred in the tropics and subtropics, followed by boreal and temperate</div><div>forests. A portion of the loss in temperate and boreal forests will not be permanent and might refer</div><div>to other types of natural forest disturbances produced by insects, fire, and severe weather, as well</div><div>as by felling of plantations or semi-natural forests as part of forest management.</div><div>Much tropical forest cover loss is in South America and Asia, while subtropical forest cover loss is</div><div>mainly in South America and Africa. When looking at countries by income levels, as defined by the</div><div>World Bank, much of deforestation takes place in upper middle and lower middle-income countries.<br /></div><div>To the risk of simplifying, this suggests an increasing pressure on forests in the transition that occurs</div><div>when countries increase economic development. In the tropics, upper-middle income countries</div><div>dominate forest cover loss in South America, due to the influence of Brazil, and lower middle-</div><div>income countries in Asia, due to the influence of Indonesia. Forest cover loss in the subtropics occurs</div><div>mainly in Brazil and Argentina in South America, many lower-middle income countries in South</div><div>America, and lower-income countries in sub-Saharan Africa. Most temperate and boreal forest cover</div><div>loss, likely not all permanent, occurs in high-income countries (Russia), and North America (United</div><div>States and Canada) </div><div><br /></div><div>Unfortunately, this data does not identify changes over time or land use interactions among</div><div>countries. Reduced forest cover loss in some mainly high-income countries, except North America, is</div><div>associated with forest cover loss, particularly in lower- and upper-middle countries in the tropics. </div><div>Interactions are informed by the “forest transition” effect. Forest transition dynamics occur when</div><div>net forest restoration replaces net forest cover loss in some specific place.  The countries that</div><div>underwent a forest transition that reduced forest loss and encouraged regrowth may have placed</div><div>additional pressure on forests outside their borders, thus displacing deforestation. The debate on</div><div>forest transitions and leakage is quite controversial given its policy implications.</div><div>Recent analysis, based on a land-balance model that quantifies deforestation due to global trade at</div><div>country level in the tropics and sub-tropics, linked to a country-to-country trade model, found that</div><div>from 2005-2013, 62% of forest loss was caused by commercial agriculture, pasture and plantations.</div><div>About 26% of total deforestation was attributed to international demand, 87% of which was</div><div>exported to countries with decreasing deforestation or increasing forest cover in Europe and Asia</div><div>(i.e. China, India).  Some of this displacement pressure may be reduced by land intensification.</div></div><div><br /></div><div><div><b> Global patterns of forest fragmentation</b></div><div><br /></div><div><br /></div><div>In this analysis we consider forest degradation alongside forest cover loss. Degradation is a multi-</div><div>factorial phenomenon that includes amongst others loss of native species, appearance of invasive</div><div>species, pollution damage, structural changes, selective timber removal and many more. Here we</div><div>use fragmentation as a proxy that can be detected through remote sensing; this is a critical aspect of</div><div>forest degradation but does not capture all aspects. The change in spatial pattern and structure by</div><div>fragmentation of forest into smaller patches or “islands” damages forest ecosystem services such as</div><div>carbon storage and climate mitigation, regulation, water provision, and habitat for biodiversity. </div><div>These impacts are created by changes at forest edges, which include increased exposure to different</div><div>climate, fire, wind, mortality, and human access.  The increasing isolation of forest patches</div><div>contributes to long-term changes in biodiversity, including species richness and productivity,</div><div>creating fundamental changes in forest ecosystems.</div><div>We evaluated the fragmentation of forests using morphological spatial pattern analysis (MSPA)</div><div>assessed on the two all-available data global forest cover maps corresponding to 2000 and 2018, to</div><div>determine forest cover transitions between different type of fragmentation classes (i.e. stable core,</div><div>inner edges, outer edges, and patches). Changes between fragmentation classes over time are</div><div>defined as primary and secondary degradation based on their initial state, in contrast to forests</div><div>which remain in the same fragmentation class as stable core, inner edge, outer edge, and patch. In</div><div>this definition, primary degradation is a result of the fragmentation of core forests into forest with</div><div>more edges, reducing the area of continuous forest extent, and resulting in greater losses of carbon</div><div>and associated ecosystem services such as biodiversity present in intact forests.  Secondary</div><div>degradation is the conversion of edge forests into more fragmented classes, occurring in secondary</div><div>forests which may already be degraded and are more accessible and easier to deforest</div></div><div><br /></div><div><br /></div>",
			"keyword": [
				"deforestation",
				"fronts",
				"forest",
				"change",
				"fragmentation",
				"URL_UPDATE"
			],
			"issued": "2020-07-02T08:58:25.000Z",
			"modified": "2026-10-23T07:29:46.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-180.0000,-59.0565,-179.9979,74.6429",
			"license": "Publication must acknowledge WWF",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::forest-2000"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://wwf-sight-maps.org/arcgis/rest/services/wwf_data_transfer/Global_Forest_Analysis/MapServer/6"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/csv?layers=6"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/shapefile?layers=6"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/geojson?layers=6"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ef56ea768f354941ae20e74a4458e37d/kml?layers=6"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=c69f5c140667471681eb48d673653ac2&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::managed-forest-concessions-wri-1",
			"title": "Managed Forest Concessions (WRI)",
			"description": "<p><span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>Logging concession refers to an area allocated by a government for logging in a public forest. Logging concessions are distinct from wood fiber concessions, where plantation forests are established for the production of pulp and paper products. Concession is used as a general term for licenses, permits, or other contracts that confer rights to private companies to manage and extract timber from public forests; terminology varies at the national level, however, and includes \"forest permits,\" \"tenures,\" \"licenses,\" and other terms.The logging concession data on GFW, while displayed as a single layer, is assembled on a country-by-country basis from multiple sources.Logging concession data displayed on the GFW website vary from country to country by date and data sources. Data may come from government agencies, NGOs, or other organizations.</span></p><div><p><br />&nbsp;</p><div>&nbsp;</div></div>",
			"keyword": [
				"Concessions",
				"concessions",
				"global",
				"WRI"
			],
			"issued": "2020-12-21T13:59:13.000Z",
			"modified": "2026-06-08T13:20:53.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-133.0816,-7.8411,140.9983,60.0001",
			"license": "<span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>This layer is a compilation of concession data from various countries and sources. The quality of these data can vary depending on the source. This layer may not be comprehensive of all existing concessions in a country, and the location of certain concessions can be inaccurate.</span>",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::managed-forest-concessions-wri-1"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://wwf-sight-maps.org/arcgis/rest/services/wwf_data_transfer/Managed_Forest_Concessions__WRI_/MapServer/0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/c69f5c140667471681eb48d673653ac2/csv?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/c69f5c140667471681eb48d673653ac2/shapefile?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/c69f5c140667471681eb48d673653ac2/geojson?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/c69f5c140667471681eb48d673653ac2/kml?layers=0"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=233c13d2c8df42cda98862ea5b2a26ce&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::mozambique-mangroves-1995-2018",
			"title": "mozambique mangroves 1995-2018",
			"description": "<p style='background-color:rgb(255, 255, 255); border:0px solid currentcolor; box-sizing:border-box; color:rgb(74, 74, 74); font-family:&quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px 0px 1.5rem; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>WWF Germany used Google Earth engine to derive mangrove extent in Mozambique from 1995 - 2018 in 5 year intervals.&nbsp;</p><p style='background-color:rgb(255, 255, 255); border:0px solid currentcolor; box-sizing:border-box; color:rgb(74, 74, 74); font-family:&quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px 0px 1.5rem; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>These are categorised into 11 types, each type is a (Multipart) Polylgon:</p><p style='background-color:rgb(255, 255, 255); border:0px solid currentcolor; box-sizing:border-box; color:rgb(74, 74, 74); font-family:&quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px 0px 1.5rem; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>Dynamic, Stable Mangrove,&nbsp;</p><p style='background-color:rgb(255, 255, 255); border:0px solid currentcolor; box-sizing:border-box; color:rgb(74, 74, 74); font-family:&quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px 0px 1.5rem; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>Gain 2000, Gain 2005, Gain 2010, Gain 2015</p><p style='background-color:rgb(255, 255, 255); border:0px solid currentcolor; box-sizing:border-box; color:rgb(74, 74, 74); font-family:&quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px; font-style:normal; font-variant-caps:normal; font-variant-ligatures:normal; font-weight:400; letter-spacing:normal; margin:0px 0px 1.5rem; text-align:start; text-decoration-color:initial; text-decoration-style:initial; text-indent:0px; text-transform:none; word-spacing:0px;'>Loss 2018, Loss 2000, Loss 2005, Loss 2010, Loss 2015</p>",
			"keyword": [
				"Mangroves",
				"Mozambique",
				"Mapping",
				"East Africa"
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			"issued": "2020-11-13T09:18:55.000Z",
			"modified": "2020-11-13T09:28:50.811Z",
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				"name": "World Wide Fund for Nature"
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					"title": "GeoJSON",
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					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/233c13d2c8df42cda98862ea5b2a26ce/kml?layers=0"
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		{
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			"identifier": "https://www.arcgis.com/home/item.html?id=350391d0933c407b917fb6aa22fb3acf",
			"landingPage": "https://globil.panda.org/maps/panda::tiger-conservation-landscape-data",
			"title": "Tiger Conservation Landscape data",
			"description": "<p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, wide­spread poaching of tigers and wildlife continues to cre­ate empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s grow­ing middle-class has intensified threats to tigers across the range.</p><p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.</p>Learn more about the analysis and results<ul><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-user-s-guide' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The User's Guide</a> that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.</li><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-technical-assessment' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015</a></li><li><a href='http://www.jstor.org/stable/10.1641/B570608' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The fate of wild tigers. BioScience</a>, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.</li></ul>",
			"keyword": [
				"TCL",
				"Tigers",
				"WCS",
				"WWF",
				"GLOBIL",
				"Species",
				"Conservation",
				"Big Cats"
			],
			"issued": "2016-02-03T10:41:47.000Z",
			"modified": "2025-12-29T15:48:11.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
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			"accessLevel": "public",
			"spatial": "40.5262,-8.8496,141.5412,63.3540",
			"license": "https://creativecommons.org/licenses/by-nc-sa/4.0",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::tiger-conservation-landscape-data"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/TCL_data/FeatureServer"
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			],
			"theme": [
				"geospatial"
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		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=350391d0933c407b917fb6aa22fb3acf&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::historic-tiger-range",
			"title": "Historic tiger range",
			"description": "<p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, wide­spread poaching of tigers and wildlife continues to cre­ate empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s grow­ing middle-class has intensified threats to tigers across the range.</p><p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.</p>Learn more about the analysis and results<ul><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-user-s-guide' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The User's Guide</a> that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.</li><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-technical-assessment' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015</a></li><li><a href='http://www.jstor.org/stable/10.1641/B570608' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The fate of wild tigers. BioScience</a>, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.</li></ul>",
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				"TCL",
				"Tigers",
				"WCS",
				"WWF",
				"GLOBIL",
				"Species",
				"Conservation",
				"Big Cats"
			],
			"issued": "2016-02-03T10:41:47.000Z",
			"modified": "2016-04-05T04:20:56.792Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
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			"spatial": "40.5262,-8.8496,141.5412,63.3540",
			"license": "https://creativecommons.org/licenses/by-nc-sa/4.0",
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					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::historic-tiger-range"
				},
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					"title": "Shapefile",
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					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/shapefile?layers=0"
				},
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					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geojson?layers=0"
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					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/kml?layers=0"
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					"title": "File Geodatabase",
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					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/filegdb?layers=0"
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					"title": "Excel",
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				{
					"@type": "dcat:Distribution",
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					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/sqlite?layers=0"
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				"geospatial"
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		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=350391d0933c407b917fb6aa22fb3acf&sublayer=1",
			"landingPage": "https://globil.panda.org/datasets/panda::restoration-landscapes-geo",
			"title": "restoration landscapes geo",
			"description": "<p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, wide­spread poaching of tigers and wildlife continues to cre­ate empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s grow­ing middle-class has intensified threats to tigers across the range.</p><p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.</p>Learn more about the analysis and results<ul><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-user-s-guide' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The User's Guide</a> that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.</li><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-technical-assessment' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015</a></li><li><a href='http://www.jstor.org/stable/10.1641/B570608' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The fate of wild tigers. BioScience</a>, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.</li></ul>",
			"keyword": [
				"TCL",
				"Tigers",
				"WCS",
				"WWF",
				"GLOBIL",
				"Species",
				"Conservation",
				"Big Cats"
			],
			"issued": "2016-02-03T10:41:47.000Z",
			"modified": "2016-02-03T10:55:07.948Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
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				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "73.1183,10.8349,106.3341,30.4854",
			"license": "https://creativecommons.org/licenses/by-nc-sa/4.0",
			"distribution": [
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					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::restoration-landscapes-geo"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/TCL_data/FeatureServer/1"
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					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/shapefile?layers=1"
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					"@type": "dcat:Distribution",
					"title": "GeoJSON",
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					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geojson?layers=1"
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					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/kml?layers=1"
				},
				{
					"@type": "dcat:Distribution",
					"title": "File Geodatabase",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/filegdb?layers=1"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Feature Collection",
					"format": "TXT",
					"mediaType": "text/plain",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/featureCollection?layers=1"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Excel",
					"format": "XLSX",
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					"format": "GPKG",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geoPackage?layers=1"
				},
				{
					"@type": "dcat:Distribution",
					"title": "SQLite Geodatabase",
					"format": "GDB",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/sqlite?layers=1"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=350391d0933c407b917fb6aa22fb3acf&sublayer=2",
			"landingPage": "https://globil.panda.org/datasets/panda::survey-landscapes-geo",
			"title": "survey landscapes geo",
			"description": "<p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, wide­spread poaching of tigers and wildlife continues to cre­ate empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s grow­ing middle-class has intensified threats to tigers across the range.</p><p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.</p>Learn more about the analysis and results<ul><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-user-s-guide' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The User's Guide</a> that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.</li><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-technical-assessment' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015</a></li><li><a href='http://www.jstor.org/stable/10.1641/B570608' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The fate of wild tigers. BioScience</a>, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.</li></ul>",
			"keyword": [
				"TCL",
				"Tigers",
				"WCS",
				"WWF",
				"GLOBIL",
				"Species",
				"Conservation",
				"Big Cats"
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			"issued": "2016-02-03T10:41:47.000Z",
			"modified": "2016-02-03T10:55:14.199Z",
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				"name": "World Wide Fund for Nature"
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			"license": "https://creativecommons.org/licenses/by-nc-sa/4.0",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::survey-landscapes-geo"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/TCL_data/FeatureServer/2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/csv?layers=2"
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					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/shapefile?layers=2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geojson?layers=2"
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				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/kml?layers=2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "File Geodatabase",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/filegdb?layers=2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Feature Collection",
					"format": "TXT",
					"mediaType": "text/plain",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/featureCollection?layers=2"
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				{
					"@type": "dcat:Distribution",
					"title": "Excel",
					"format": "XLSX",
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				{
					"@type": "dcat:Distribution",
					"title": "GeoPackage",
					"format": "GPKG",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geoPackage?layers=2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "SQLite Geodatabase",
					"format": "GDB",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/sqlite?layers=2"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=350391d0933c407b917fb6aa22fb3acf&sublayer=3",
			"landingPage": "https://globil.panda.org/datasets/panda::tcl-classes-and-priorities-geo",
			"title": "tcl classes-and-priorities geo",
			"description": "<p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>A world without tigers is hard to imagine, but red flags are being hoisted across the tiger’s range. In Indochina, wide­spread poaching of tigers and wildlife continues to cre­ate empty forests, and the development of the proposed transnational economic corridors in the region will further fragment Indochina’s remaining forests and create dispersal barriers. In Sumatra and Malaysia, vast oil palm and acacia plantations are predicted to result in complete conversion of some of the richest lowland rain forests on Earth, habitats that were populated by tigers only a few years ago. The increasing demand for tiger parts for folk medicines in China and Southeast Asia and for costume adornment among Tibet’s grow­ing middle-class has intensified threats to tigers across the range.</p><p style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-stretch: inherit; line-height: 24px; font-family: OpenSansRegular, Helvetica, Arial, sans-serif; font-size: 15px; margin-top: 0px; margin-bottom: 1.25em; padding: 0px; vertical-align: baseline; background-color: rgb(255, 255, 255);'>But large mammals, including tigers, have coexisted for centuries with dense human populations. The release of the 1997 Tiger Conservation Unit Analysis identified where tigers can live in the future. During the decade since, experiences from implementing field conservation projects have confirmed that the future of wildlife conservation in Asia depends on judicious land use planning—zoning—of human use areas, core wildlife habitat, buffer zones, and corridors in large conservation landscapes to restore the harmony that once existed in the wild land-village interface of rural Asia.</p>Learn more about the analysis and results<ul><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-user-s-guide' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The User's Guide</a> that highlights the remaining tiger lands—the large landscapes of habitat, often anchored by protected areas that are global priorities for conservation.</li><li><a href='http://www.worldwildlife.org/publications/tiger-conservation-landscape-data-and-report-the-technical-assessment' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The Technical Assessment: Setting Priorities for the Conservation and Recovery of Wild Tigers: 2005-2015</a></li><li><a href='http://www.jstor.org/stable/10.1641/B570608' style='box-sizing: border-box; word-wrap: break-word; border: 0px; font-style: inherit; font-variant: inherit; font-weight: inherit; font-stretch: inherit; line-height: inherit; font-family: inherit; margin: 0px; padding: 0px; vertical-align: baseline; color: rgb(0, 110, 165); text-decoration: none; transition: all 0.25s ease-in-out;'>The fate of wild tigers. BioScience</a>, Dinerstein, E., Loucks, C.J., Wikramanayake, E., Ginsberg, J., Sanderson, E., Seidensticker, J., Forrest, J.L., Bryja, G., Heydlauff, A., Klenzendorf, S., Mills, J, O'Brien, T., Shrestha, M, Simons, R., Songer, M. 2007.</li></ul>",
			"keyword": [
				"TCL",
				"Tigers",
				"WCS",
				"WWF",
				"GLOBIL",
				"Species",
				"Conservation",
				"Big Cats"
			],
			"issued": "2016-02-03T10:41:47.000Z",
			"modified": "2019-01-29T10:50:52.496Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "73.2014,-5.9411,140.6644,51.2798",
			"license": "https://creativecommons.org/licenses/by-nc-sa/4.0",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::tcl-classes-and-priorities-geo"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/TCL_data/FeatureServer/3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/csv?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/shapefile?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geojson?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/kml?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "File Geodatabase",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/filegdb?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Feature Collection",
					"format": "TXT",
					"mediaType": "text/plain",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/featureCollection?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Excel",
					"format": "XLSX",
					"mediaType": "application/vnd.ms-excel",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/excel?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoPackage",
					"format": "GPKG",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/geoPackage?layers=3"
				},
				{
					"@type": "dcat:Distribution",
					"title": "SQLite Geodatabase",
					"format": "GDB",
					"mediaType": "application/geopackage+sqlite3",
					"accessURL": "https://globil.panda.org/api/download/v1/items/350391d0933c407b917fb6aa22fb3acf/sqlite?layers=3"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=5ebad80efb914448ac2a7c7abcd98a31&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::nature-based-climate-solutions-nbs-carbon-projects-africa-karnik-et-al-v1-2024",
			"title": "Nature-based climate solutions (NbS) Carbon Projects Africa (Karnik et al. v1 2024)",
			"description": "<p><strong>Carbon Projects database for Africa</strong> (other areas available also)<br />visualized by Registry name (N) and Project Type (T)</p><p>Nature-based climate solutions (NBS) have become an important component of &nbsp;strategies aiming to reduce atmospheric CO2 and mitigate climate change impacts. Carbon offsets have emerged as one of the most widely implemented NBS strategies, however, these projects have also been criticized for exaggerating offsets. Verifying the efficacy of NBS-derived carbon offset is complicated by a lack of readily available geospatial boundary data. Herein, we detail methods and present a database of nature-based offset project boundaries. This database provides the locations of 575 NBS projects distributed across 55 countries. Geospatial boundaries were aggregated using a combination of scraping data from carbon project registries &nbsp;(n=433, 75.3%) as well as manual georeferencing and digitization (n=127, 22.1%). Database entries include three varieties of carbon projects: avoided deforestation, afforestation, reforestation and re-vegetation, and improved forest management. An accuracy assessment of the georeferencing and digitizing process indicated a high degree of accuracy (intersection over union score of 0.98 +/- 0.015).</p><p>Karnik, A., Kilbride, J., Goodbody, T., Rachel, R., &amp; Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. <a target='_blank' href='https://doi.org/10.5281/zenodo.11459391' rel='nofollow ugc noopener noreferrer'>https://doi.org/10.5281/zenodo.11459391</a> Version v1 Jun 3, 2024</p><p>Associated Publication: Karnik, A., Kilbride, J.B., Goodbody, T.R.H. et al. An open-access database of nature-based carbon offset project boundaries. Sci Data 12, 581 (2025). <a target='_blank' href='https://doi.org/10.1038/s41597-025-04868-2' rel='nofollow ugc noopener noreferrer'>https://doi.org/10.1038/s41597-025-04868-2</a></p>",
			"keyword": [
				"Carbon",
				"Africa",
				"NbS"
			],
			"issued": "2025-07-29T07:50:50.000Z",
			"modified": "2025-07-29T07:54:03.882Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-16.4525,-33.9871,50.0177,13.1838",
			"license": "<p><span>CC-by-4.0&nbsp;</span><br />Karnik, A., Kilbride, J., Goodbody, T., Rachel, R., &amp; Ayrey, E. (2024). A global database of nature-based carbon offset project boundaries [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11459391</p>",
			"distribution": [
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					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::nature-based-climate-solutions-nbs-carbon-projects-africa-karnik-et-al-v1-2024"
				},
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					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/Nature_based_climate_solutions_NbS_Carbon_Projects_Africa/FeatureServer/0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5ebad80efb914448ac2a7c7abcd98a31/csv?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5ebad80efb914448ac2a7c7abcd98a31/shapefile?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5ebad80efb914448ac2a7c7abcd98a31/geojson?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5ebad80efb914448ac2a7c7abcd98a31/kml?layers=0"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=ea4335e5b1844a7b93589c528944b70a&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::mai-po-wetland-habitats",
			"title": "Mai Po Wetland Habitats",
			"description": "<p><span style='font-family:&quot;Calibri&quot;,sans-serif;'>The </span><span style='font-family:&quot;Calibri&quot;,sans-serif;'>Mai Po Inner Deep Bay Ramsar site in Hong Kong\nis an important staging and wintering site along the East Asia Flyway and supports large\nnumbers of migratory birds, including 22 globally threatened species. </span><span style='font-family:&quot;Calibri&quot;,sans-serif;'>Thus, rising\nsea levels, stronger storm surges and shrinking mudflats will combine to\ndecrease the availability of habitats for the waterbirds in Mai Po in the\nfuture, and adaptation plans to counter the threats from climate change and\noffshore subsidence are needed now to ensure </span><span style='font-family:&quot;Calibri&quot;,sans-serif;'>persistence\nof migrations and flyway functionality. Here, we modelled the impacts of\nclimate change and decreasing sedimentation rates on the important bird\nhabitats in the Mai Po Inner Deep Bay Ramsar site to support a climate\nadaptation strategy that will continue to provide and support migratory birds. </span></p><p><span style='font-family:Calibri, sans-serif;'>The Mai Po Inner Deep Bay Ramsar site is located\nin the North West New Territories of Hong Kong</span><span style='font-family:Calibri, sans-serif;'>,\nalong the southern edges of the Inner Deep Bay of the Pearl River estuary\n(Figure 1a). Mai Po is comprised of tidal mudflats, coastal mangroves, marshes,\nand fishponds, and now represents Hong Kong’s largest remaining\nwetland. Mai Po is about 1,500 ha in extent, with a 380 ha nature reserve zoned\nwithin it.</span></p><p><span style='font-family: Calibri, sans-serif;'>Mai Po supports over </span><span style='font-family: Calibri, sans-serif;'>50,000 to\n80,000 wintering waterbirds and another 20,000-30,000 </span><span style='font-family: Calibri, sans-serif; color: rgb(40, 37, 37);'>shorebirds use it as a staging area during their annual migration in\nspring and autumn. Regular visitors to Mai Po include the E</span><span style='font-family: Calibri, sans-serif;'>ndangered\nBlack-faced Spoonbill (<i>Platalea minor</i>)\nand </span><span style='font-family: Calibri, sans-serif;'>Nordmann’s Greenshank (<i>Tringa guttifer</i>), and, occasionally, the Critically Endangered\nSpoon-billed sandpiper (<i>Calidris pygmaea</i>).</span><span style='font-family: Calibri, sans-serif;'>\nMai Po played a significant role in the population recovery of the Black-faced\nspoonbill, and now regularly supports over 10% of the global population.</span></p><div style=''>\n\n\n\n<div style=''>\n\n<div><span style=''></span>\n\n<p><br /></p>\n\n</div>\n\n</div>\n\n</div><span style='font-family:&quot;Calibri&quot;,sans-serif;'></span>",
			"keyword": [
				"Ramsar",
				"wetland",
				"landcover",
				"land use",
				"sea level rise",
				"climate change",
				"opendata"
			],
			"issued": "2020-07-13T08:50:45.000Z",
			"modified": "2020-07-13T08:50:52.985Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "113.9931,22.4477,114.1091,22.5374",
			"license": "https://creativecommons.org/licenses/by/4.0",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::mai-po-wetland-habitats"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/Mai_Po_Habitats/FeatureServer/0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ea4335e5b1844a7b93589c528944b70a/csv?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ea4335e5b1844a7b93589c528944b70a/shapefile?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ea4335e5b1844a7b93589c528944b70a/geojson?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/ea4335e5b1844a7b93589c528944b70a/kml?layers=0"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=95aaabba77d5446c8d400e8f9305745d",
			"landingPage": "https://globil.panda.org/documents/panda::what-can-i-do-with-globil",
			"title": "What can I do with GLOBIL",
			"description": "{{description}}",
			"keyword": [
				"GLOBIL",
				"GLOBIL HUB",
				"Tutorial"
			],
			"issued": "2023-03-01T16:05:06.000Z",
			"modified": "2023-03-01T16:08:11.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "hifrank",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/documents/panda::what-can-i-do-with-globil"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=6ac47d334bff46e8a1e759f4968c5cff",
			"landingPage": "https://globil.panda.org/apps/panda::mapping-hcvs-in-the-congo-basin",
			"title": "Mapping HCVs in the Congo Basin",
			"description": "the results from this analysis were published in Environmental Research Letters: https://iopscience.iop.org/article/10.1088/1748-9326/ab9fae<div><br /></div>",
			"keyword": [
				"DRC",
				"Congo Basin",
				"FSC",
				"HCV"
			],
			"issued": "2017-04-13T15:27:47.000Z",
			"modified": "2021-07-05T13:32:07.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/apps/panda::mapping-hcvs-in-the-congo-basin"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://panda.maps.arcgis.com/apps/webappviewer/index.html?id=6ac47d334bff46e8a1e759f4968c5cff"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=46b29ddaf2a54730844000ce8c431f83",
			"landingPage": "https://globil.panda.org/datasets/panda::deforestation-fronts-2020-1",
			"title": "Deforestation Fronts 2020",
			"description": "<p style='background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'>WWF developed a global analysis of the world's most important deforestation areas or deforestation fronts in 2015. <span style='background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'>This assessment was revised in 2020 as part of the <b>WWF</b> </span><span style='background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'><a href='https://wwfint.awsassets.panda.org/downloads/deforestation_fronts___drivers_and_responses_in_a_changing_world___full_report_1.pdf' rel='nofollow ugc' target='_blank'><b><font color='#87cefa'>Deforestation Fronts</font></b></a></span><span style='background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'> Report.</span></p><p style='background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'>An Emerging Hotspot Analysis was used to derive deforestation fronts using the ArcGIS Emerging Hot Spot Analysis tool. This analysis was undertaken in 10km² hexagons, within country boundaries, based on the remote sensing data series from Terra-i data for Latin America, Africa, Asia and Oceania for the period from <b>2004 </b>to <b>2017</b> for which validated data was available. Locations with the highest incidence of deforestation were selected in the tropical and subtropical biomes in each country. Following the hotspot analysis, a visual interpretation of the spatial clustering of deforestation hotspots in the selected biomes by country was conducted in order to delineate the boundaries of deforestation fronts, which comprise all countries in which deforestation hotspots were detected. Deforestation fronts are places that contain an important area of remaining forests where there is a relatively larger spatial concentration or clustering of deforestation hotspots (measured in 10km2 hexagons). As a result of this exercise, based on the Terra-i data, 30 countries were retained in the analysis.</p>",
			"keyword": [
				"deforestation",
				"fronts",
				"forest",
				"shapefile",
				"hotspots",
				"hotspot",
				"trends",
				"Forest Cover Change",
				"WWF"
			],
			"issued": "2020-08-14T14:11:57.000Z",
			"modified": "2021-05-21T16:56:12.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "https://creativecommons.org/licenses/by-sa/4.0",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::deforestation-fronts-2020-1"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=245d9031685b4e8ab5aea705edf58397&sublayer=2",
			"landingPage": "https://globil.panda.org/maps/245d9031685b4e8ab5aea705edf58397",
			"title": "{{name}}",
			"description": "{{description}}",
			"keyword": [
				"forest",
				"degradation",
				"forest cover",
				"global",
				"forestsforward"
			],
			"issued": "2020-12-21T09:28:04.000Z",
			"modified": "{{modified:toISO}}",
			"publisher": {
				"name": "{{source}}"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "{{orgContactEmail}}"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "<div><span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>[CC BY-SA-4.0](https://creativecommons.org/licenses/by/4.0/)</span><br /></div><div><br /></div><div><span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>please cite as: Pacheco, P., Mo, K., Dudley, N., Shapiro, A., Aguilar-Amuchastegui, N., Ling, P.Y., Anderson, C. and Marx, A. 2021. Supplement to </span><span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>Deforestation fronts: Drivers and responses in a changing world</span><span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>. WWF, Gland, Switzerland. </span></div>",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/245d9031685b4e8ab5aea705edf58397"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=5354632068a54fdfbee5964de655bbc7&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::drc-protected-areas-parap",
			"title": "DRC Protected Areas (PARAP)",
			"description": "<div>These protected areas are the result of a 4 year project to update and assess the protected areas program in DRC (PARAP).</div><div><br /></div>The Program of Support to the Protected Area System (Programme d’Appui au<div>Réseau des Aires Protégées - PARAP) is a joint initiative of the Congolese Institute</div><div>for the Conservation of Nature (Institut Congolais pour la Conservation de la Nature</div><div>- ICCN) and the World Wide Fund for Nature (WWF). The primary objective of</div><div>PARAP is to assist the DRC in achieving its objective of conserving its biodiversity</div><div>and preserving the essential services provided by its ecosystems through the</div><div>development of an effectively and equitably managed system of protected areas that</div><div>covers at least 15 % of the national territory and contributes to national strategies for</div><div>biodiversity conservation, development and poverty reduction.</div><div>The first phase of PARAP ran from 2011 to 2016 and benefited from the support</div><div>of multiple financial partners. The major donors that were vital to sustaining the</div><div>program include the Federal Ministry for the Environment, Nature Conservation</div><div>and Nuclear Safety (BMU) and the Reconstruction Credit Institute (KfW) of the</div><div>Federal Republic of Germany, as well as the WWF network. The United States Agency</div><div>for International Development (USAID), the Norwegian Agency for Development</div><div>Cooperation (NORAD), the Global Environment Facility (GEF) and the World Bank</div><div>(WB) also contributed significantly to the realization of particular activities. Finally,</div><div>targeted assistance was provided by the German Development Agency (GIZ) and the</div><div>Goldhammer Foundation.</div><div><br /></div><div>From 2011 to 2016, the program operated out of ICCN's Technical and Scientific</div><div>Department (Département technique et scientifique - DTS), where its offices were</div><div>housed. Led by a WWF Technical Advisor, in coordination with the head of the</div><div>DTS and its directors, the first phase of the program mobilized approximately 25</div><div>permanent staff. The program team consisted primarily of thematic experts in fauna,</div><div>flora and socio-economics. These experts guided much of the program’s fieldwork and</div><div>were complimented by additional logistical and administrative staff. Numerous ICCN</div><div>officers and agents, from both the Directorate General (DG) and individual protected</div><div>areas, participated in PARAP activities and were the beneficiaries of trainings aimed</div><div>at strengthening their relevant capacities. Specific technical support was developed</div><div>to strengthen the capacities of ICCN's Directorate of Planning, Monitoring and</div><div>Evaluation (Direction de la planification, suivi et évaluation - DPSE).</div><div>To ensure a sound scientific and technical approach and encourage the emergence</div><div>of a new generation of young Congolese professionals, the program established</div><div>partnerships with universities and national and international research organizations.</div><div>Notably, the first phase of the program benefited from partnerships with the</div><div>International Union for Conservation of Nature (IUCN), the Center for Functional and</div><div>Evolutionary Ecology (Centre d’Écologie Fonctionnelle et Évolutive - CEFE) of the</div><div>National Center for Scientific Research (Centre National de la Recherche Scientifique</div><div>- CNRS), Griffith University (Australia) and the Center for Biodiversity Monitoring</div><div>(Centre de Surveillance de la Biodiversité - CSB) at the University of Kisangani</div><div>(UNIKIS).</div>",
			"keyword": [
				"WWF",
				"PARAP",
				"ProtectedAreas",
				"PA"
			],
			"issued": "2020-12-15T10:58:39.000Z",
			"modified": "2020-12-15T13:36:23.256Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "12.3684,-11.8772,30.2431,5.2879",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::drc-protected-areas-parap"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services1.arcgis.com/RTK5Unh1Z71JKIiR/arcgis/rest/services/DRC_PAs/FeatureServer/0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5354632068a54fdfbee5964de655bbc7/csv?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5354632068a54fdfbee5964de655bbc7/shapefile?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5354632068a54fdfbee5964de655bbc7/geojson?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/5354632068a54fdfbee5964de655bbc7/kml?layers=0"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=7c0ae4fcef1f44c2bdfeaa64803647c2",
			"landingPage": "https://globil.panda.org/maps/7c0ae4fcef1f44c2bdfeaa64803647c2",
			"title": "{{name}}",
			"description": "{{description}}",
			"keyword": [
				"Allen",
				"Coral",
				"Atlas",
				"EastAfrica",
				"marine",
				"benthic"
			],
			"issued": "2020-11-18T08:28:09.000Z",
			"modified": "{{modified:toISO}}",
			"publisher": {
				"name": "{{source}}"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/7c0ae4fcef1f44c2bdfeaa64803647c2"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=07db8149947143efb04416ab38123f81",
			"landingPage": "https://globil.panda.org/documents/panda::atlas-of-deforestation-and-industrial-plantations-in-borneo-version-2",
			"title": "Atlas of deforestation and industrial plantations in Borneo - Version 2",
			"description": "<p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>Conservationists lament the loss of rainforests in Borneo, and charge plantation companies for their destruction. Those on the plantation side argue that planting is done on already deforested land. </p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>This <a href='http://www.nature.com/articles/srep32017' style='color: rgb(0, 121, 193); text-decoration-line: none; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif !important;' target='_blank'>study</a> reveals that the plantation industry is the principle driver of the loss of old-growth forest in Malaysian Borneo. In Indonesian Borneo (Kalimantan), considerably more oil palm has been developed on already deforested lands. This is what the conservation community has promoted for decades. Development on degraded lands is a cornerstone of sustainable palm oil development, compatible with certification schemes and with recent zero-deforestation pledges. Seen from that perspective, Indonesia seems to have done much better than Malaysia, than has been widely assumed – though since 2005 the sharp increase in oil-palm driven deforestation gives little room for complacency. Despite planting on degraded lands, deforestation remains very high in Borneo, and does not appear to be slowing. </p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>This atlas helps distinguish plantation companies who practiced deforestation from those who avoided it, for more effective oversight of certification bodies and governments, and to allow companies that are doing the right thing, avoid the bad reputation of those who actually deserve it.</p><p style='margin-top: 0px; margin-bottom: 0px; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'></p><ul><li>View the extent of the forest area in 1973, before extractive industries began<br /></li><li>View forty-two years (1973-2015) of forest degradation by the logging industry, and conversion to industrial oil palm and pulpwood plantations<br /></li><li>Track the history of the type of vegetation present before plantations became established<br /></li><li>Determine the area of forest currently remaining in concessions of oil palm and pulpwood <br /></li><li>Determine the area developed by companies in concessions<br /></li><li>Determine the total area of forest that has been cleared since 1973 in concessions<br /></li><li>Determine the area of forest that has been actively cleared by companies to develop plantations in concessions (company-driven deforestation)<br /></li><li>Determine the area of non-forest that has been developed into plantation (avoided deforestation)<br /></li></ul><p style='margin-top: 0px; margin-bottom: 0px; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'></p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>For more information on results, methods, definitions and caveats, readers should consult this scientific publication: <a href='http://www.nature.com/articles/srep32017' style='color: rgb(0, 94, 149); font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif !important;' target='_blank'>Gaveau et al. (2016) Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Scientific Reports (2016)</a></p>",
			"keyword": [
				"WRI",
				"CIFOR",
				"Borneo",
				"Deforestation"
			],
			"issued": "2018-01-19T05:22:02.000Z",
			"modified": "2018-01-19T05:26:56.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/documents/panda::atlas-of-deforestation-and-industrial-plantations-in-borneo-version-2"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://panda.maps.arcgis.com/home/item.html?id=42f16bb693634f98b27d8ec130ad2b54"
				}
			],
			"theme": ""
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=18bac6f8ff2a4ed0afb62ca7a8b8ba13&sublayer=0",
			"landingPage": "https://globil.panda.org/datasets/panda::borneo-woodfiber-concession-2016-wri",
			"title": "Borneo WoodFiber Concession 2016 WRI",
			"description": "<p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>A pulpwood concession is a right granted by governments to plantation companies for the establishment and management of industrial, monoculture timber estates (mainly Acacia, or Eucalyptus in Borneo) for the production of pulp and paper.</p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>For Kalimantan, concessions were digitized and adapted by Greenpeace from: MoFor (2014) IUPHHK-HTI, Ministerial Decree SK 5984/Menhut-VI/BRPUK/2014, Ministry of Forestry, Indonesia, November 2014, <a href='http://www.dephut.go.id/index.php/news/details/9671' style='color: rgb(0, 121, 193); text-decoration-line: none; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, Helvetica, Arial, sans-serif !important;' target='_blank'>http://www.dephut.go.id/index.php/news/details/9671</a>. Pulpwood plantations are regulated and issued by one ministry: the Ministry of Environment and Forestry.</p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>For Sarawak, concessions were obtained from the Bruno Manser Fund report (1)  in pdf format, scanned and digitized in ArcGIS. Faeh D (2011) Development of Global Timber Tycoons In Sarawak, East Malaysia. History and Company Profile. Basel, Switzerland: Bruno Manser Fund.</p><p style='margin-top: 0px; margin-bottom: 1.55rem; font-family: &quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size: 17px; background-color: rgb(255, 255, 255);'>For Sabah, concession data could not be obtained.</p>",
			"keyword": [
				"WRI",
				"CIFOR",
				"Borneo",
				"Plantation",
				"Wood"
			],
			"issued": "2018-01-19T05:18:28.000Z",
			"modified": "2016-11-16T11:00:56.791Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "108.9471,-4.1818,118.7530,4.9263",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/datasets/panda::borneo-woodfiber-concession-2016-wri"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "https://services.arcgis.com/P8Cok4qAP1sTVE59/arcgis/rest/services/REGBorneo_WoodFiberConcession_2016_WRI/FeatureServer/0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "CSV",
					"format": "CSV",
					"mediaType": "text/csv",
					"accessURL": "https://globil.panda.org/api/download/v1/items/18bac6f8ff2a4ed0afb62ca7a8b8ba13/csv?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "Shapefile",
					"format": "ZIP",
					"mediaType": "application/zip",
					"accessURL": "https://globil.panda.org/api/download/v1/items/18bac6f8ff2a4ed0afb62ca7a8b8ba13/shapefile?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "GeoJSON",
					"format": "GeoJSON",
					"mediaType": "application/vnd.geo+json",
					"accessURL": "https://globil.panda.org/api/download/v1/items/18bac6f8ff2a4ed0afb62ca7a8b8ba13/geojson?layers=0"
				},
				{
					"@type": "dcat:Distribution",
					"title": "KML",
					"format": "KML",
					"mediaType": "application/vnd.google-earth.kml+xml",
					"accessURL": "https://globil.panda.org/api/download/v1/items/18bac6f8ff2a4ed0afb62ca7a8b8ba13/kml?layers=0"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=0780255be5d04ae2b7439260c94b46a1",
			"landingPage": "https://globil.panda.org/maps/panda::modis-fire-detections-last-24-hours",
			"title": "MODIS Fire Detections last 24 hours",
			"description": "<span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>MODIS Global Fires is a product of The University of Maryland's </span><a href='http://maps.geog.umd.edu/firms/' style='color: rgb(33, 117, 155); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>Fire Information for Resource Management System (FIRMS)</a><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>. FIRMS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World. </span><br /><br /><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>MODIS stands for MODerate Resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width. </span><br /><br /><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for FIRMS to update the website. Occasionally, hardware errors mean that it takes longer the 2-4 hours to process the data. For information on the system status of MODIS Rapid Response, </span><a href='http://rapidfire.sci.gsfc.nasa.gov/status/' style='color: rgb(33, 117, 155); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>click here</a><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>. </span><br /><br /><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>We gather this data from the </span><a href='https://earthdata.nasa.gov/data/near-real-time-data/firms/active-fire-data' style='color: rgb(33, 117, 155); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);' target='_blank'>EOSDIS download site</a><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>. These shapefiles from NASA are parsed using the </span><a href='http://www.esriurl.com/LiveFeed' style='color: rgb(33, 117, 155); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>Aggregated Live Feeds methodology</a><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'> to take the returned information and serve the data through ArcGIS Server as a map service. </span><br /><br /><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>For performance reasons these layers do not draw when zoomed out beyond 1:20000000. </span><br /><br /><span style='color: rgb(77, 77, 77); font-family: 'Lucida Grande', 'Lucida Sans Unicode', Arial, Helvetica, sans-serif; font-size: 14px; line-height: 22px; background-color: rgb(255, 255, 255);'>Attribute Information:</span><ul><li>Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). See <a href='http://maps.geog.umd.edu/firms/faq.htm#fireonground' style='color: rgb(33, 117, 155);'>What does a hotspot/fire detection mean on the ground?</a></li><li><a href='http://maps.geog.umd.edu/firms/faq.htm#temperature' style='color: rgb(33, 117, 155);'>Brightness</a>: The brightness temperature, measured (in Kelvin) using the MODIS channels 21/22 and channel 31.</li><li><a href='http://maps.geog.umd.edu/firms/faq.htm#scantrackmeaning' style='color: rgb(33, 117, 155);'>Scan and Track</a>: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See <a href='http://maps.geog.umd.edu/firms/faq.htm#scantrackmeaning' style='color: rgb(33, 117, 155);'>What does scan and track mean?</a></li><li>Date: Acquisition date of the hotspot/active fire pixel.</li><li>Time: Time of the overpass of the satellite (in UTC).</li><li><a href='http://maps.geog.umd.edu/firms/faq.htm#launchdate' style='color: rgb(33, 117, 155);'>Satellite</a>: Whether the detection was picked up by the Terra or Aqua satellite.</li><li><a href='http://maps.geog.umd.edu/firms/faq.htm#confidence' style='color: rgb(33, 117, 155);'>Confidence</a>: The detection confidence is a quality flag of the individual hotspot/active fire pixel.</li><li>Version: Version refers to the processing collection and source of data. The number before the decimal refers to the <a href='http://maps.geog.umd.edu/firms/faq.htm#collections' style='color: rgb(33, 117, 155);'>collection</a>(e.g. MODIS <a href='http://maps.geog.umd.edu/firms/faq.htm#collection5' style='color: rgb(33, 117, 155);'>Collection 5</a>). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by<a href='http://rapidfire.sci.gsfc.nasa.gov/' style='color: rgb(33, 117, 155);'>MODIS Rapid Response</a> will have the source code “CollectionNumber.0”. Data sourced from <a href='http://maps.geog.umd.edu/firms/faq.htm#MRRMODAPS' style='color: rgb(33, 117, 155);'>MODAPS</a> (with a 2 month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS. See <a href='http://maps.geog.umd.edu/firms/faq.htm#MRRMODAPS' style='color: rgb(33, 117, 155);'>What is the difference between data sourced from MODIS Rapid Response and MODAPS Collection 5?</a></li><li>Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.</li><li>FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (<a href='http://maps.geog.umd.edu/firms/publications.htm#wooster' style='color: rgb(33, 117, 155);'>Wooster et. al. (2005)</a>)</li></ul>",
			"keyword": [
				"Esri",
				"MODIS",
				"EOSDIS",
				"NASA",
				"EOS",
				"Earth Science",
				"Fire",
				"Global Fire",
				"Thermal",
				"FIRMS",
				"Terra",
				"Aqua",
				"hotspot"
			],
			"issued": "2016-02-04T09:17:55.000Z",
			"modified": "2017-06-12T11:07:18.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-172.3070,-49.2670,179.8900,55.6770",
			"license": "Data must be used according to NASA constraints",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::modis-fire-detections-last-24-hours"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "http://firms.modaps.eosdis.nasa.gov/wms/"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=8edef040ad724663abc551da46297fab",
			"landingPage": "https://globil.panda.org/maps/panda::africa-inundated-area-2013-2015",
			"title": "Africa inundated area 2013-2015",
			"description": "<span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>Flood maps of previous year provides valuable information as it indicates historical flood extents. For this project we can at least show flood extents from 2013 onwards and aim, if time permits, to extend the inundated layers back to 2000.</span><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'><br /></span></div><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>for more information visit: </span><font color='#000000' face='Palatino, Times, georgia, serif' size='3'>http://floodobservatory.colorado.edu/WebMapServerDataLinks.html</font></div>",
			"keyword": [
				"Africa",
				"2015",
				"inundated",
				"flooding",
				"hydrology",
				"Dartmouth Flood Observatory",
				"WWF",
				"GLOBIL",
				"monitoring"
			],
			"issued": "2016-12-22T12:53:11.000Z",
			"modified": "2017-02-28T22:21:40.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-18.2579,-34.9143,60.0000,40.0000",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::africa-inundated-area-2013-2015"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "http://floodobservatory.colorado.edu/geoserver/DFO_shapes/wms"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=421314874e254e2b95620505e05bb69b",
			"landingPage": "https://globil.panda.org/maps/panda::africa-daily-water-extent",
			"title": "Africa daily water extent",
			"description": "<span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>The current daily water extent layer is a 3-day moving water extent product, processed by NASA Goddard's Office of Applied Science. Three days of data are included to overcome missing data due to clouds and at the same time give an almost near real time representation of the surface water extent.</span><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'><br /></span></div><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>for more information go to: </span><font color='#000000' face='Palatino, Times, georgia, serif' size='3'>http://floodobservatory.colorado.edu/WebMapServerDataLinks.html</font></div>",
			"keyword": [
				"water",
				"Africa",
				"flooding",
				"daily",
				"Dartmouth",
				"WWF",
				"GLOBIL",
				"monitoring"
			],
			"issued": "2016-12-22T12:40:35.000Z",
			"modified": "2017-02-28T22:11:50.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-18.2771,-34.9360,59.9107,40.0469",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::africa-daily-water-extent"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "http://floodobservatory.colorado.edu/geoserver/DFO_3day_current_AF/wms"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=a6a1f2386b534419bae3cfa7283082f3",
			"landingPage": "https://globil.panda.org/maps/panda::africa-accumulated-water-extent-january-until-today",
			"title": "Africa accumulated water extent - January until today",
			"description": "<span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>This data product shows the water extent of the current year from January onwards, and will be monthly updated. This displayed aims to highlight the extent of flood events, which happened earlier during a calendar year.</span><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'><br /></span></div><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>for more information visit: </span><font color='#000000' face='Palatino, Times, georgia, serif' size='3'>http://floodobservatory.colorado.edu/WebMapServerDataLinks.html</font></div>",
			"keyword": [
				"WWF",
				"Dartmouth Flood Observatory",
				"flooding",
				"water",
				"hydrology",
				"Africa",
				"GLOBIL",
				"monitoring"
			],
			"issued": "2016-12-22T12:48:52.000Z",
			"modified": "2017-02-28T22:08:15.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-18.2992,-34.9373,60.0494,40.0469",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::africa-accumulated-water-extent-january-until-today"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "http://floodobservatory.colorado.edu/geoserver/DFO_Jan_till_current_AF/wms"
				}
			],
			"theme": [
				"geospatial"
			]
		},
		{
			"@type": "dcat:Dataset",
			"identifier": "https://www.arcgis.com/home/item.html?id=04c014bcde8f4b188433966bfdfc1c92",
			"landingPage": "https://globil.panda.org/maps/panda::africa-2-week-accumulated-water-extent",
			"title": "Africa 2 week accumulated water extent",
			"description": "<span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>The current 2-week water extent layer is a 14-day moving water extent product to specify the severity of long lasting floods. Large floods travel downstream leaving devastated areas behind. The 2-week product better maps the total extent of a flood.</span><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'><br /></span></div><div><span style='color: rgb(0, 0, 0); font-family: Palatino, Times, georgia, serif; font-size: medium; background-color: rgb(255, 255, 255);'>for more information visit: </span><font color='#000000' face='Palatino, Times, georgia, serif' size='3'>http://floodobservatory.colorado.edu/WebMapServerDataLinks.html</font></div>",
			"keyword": [
				"WWF",
				"Dartmouth",
				"Flood Observatory",
				"Flooding",
				"Africa",
				"water",
				"hydrology",
				"GLOBIL",
				"monitoring"
			],
			"issued": "2016-12-22T12:44:06.000Z",
			"modified": "2017-02-28T22:07:12.000Z",
			"publisher": {
				"name": "World Wide Fund for Nature"
			},
			"contactPoint": {
				"@type": "vcard:Contact",
				"fn": "WWF_Globil",
				"hasEmail": "mailto:globil@wwf.de"
			},
			"accessLevel": "public",
			"spatial": "-39.9448,-34.9382,60.0500,40.0469",
			"license": "",
			"distribution": [
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS Hub Dataset",
					"format": "Web Page",
					"mediaType": "text/html",
					"accessURL": "https://globil.panda.org/maps/panda::africa-2-week-accumulated-water-extent"
				},
				{
					"@type": "dcat:Distribution",
					"title": "ArcGIS GeoService",
					"format": "ArcGIS GeoServices REST API",
					"mediaType": "application/json",
					"accessURL": "http://floodobservatory.colorado.edu/geoserver/DFO_2wk_current_AF/wms"
				}
			],
			"theme": [
				"geospatial"
			]
		}
	]
}