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2130 locations

8 attributes

Intact Forest Landscapes (2013) (from SDG15: Life on Land)

Shared by GlobalForestWatch on September 15, 2015. updated 17 days ago.
<p>The <a href="http://intactforests.org/">Intact Forest Landscapes</a> (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat satellite imagery for the year 2000.</p><p>Changes in the extent of IFLs were identified within year 2000 IFL boundary using the global wall-to-wall Landsat image composite for year 2013 and the global forest cover loss dataset (Hansen et al., 2013). Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2013. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.</p><p>This data can be used to assess forest intactness, alteration, and degradation at global and regional scales.</p>
3 locations

71 attributes

RSPO Land Use Change Analysis (from Country Data)

Shared by GlobalForestWatch on July 28, 2017. updated 3 months ago.
<p>Land cover maps for 2005 were developed using the four vegetation classes used under the RSPO’s Remediation and Compensation Procedures (RACP): 1. Structurally complex forest with uneven or multi-layered canopy, 2. Structurally simplified or degraded forest with even or single-layered canopy, 3. Multi-species agroforestry and 4. Highly modified and/or degraded areas retaining little or no natural, structurally intact vegetation.</p><p>Indicative forest cover layer was derived initially from Hansen tree cover (minimum canopy 70% in Ghana, 80% in Thailand, 90% in Honduras) combined with Landsat data (minimum canopy 70% in Ghana, 80% in Thailand and 95% in Honduras), which in turn were refined further with reference to country-specific forest layers (USGS dense forest cover, Ghana; REDD/CCAD-GIZ, Honduras; SAVGIS 2000 forest cover, Thailand). Sub-classes of forest vegetation were then differentiated using Landsat based supervised classification with on-screen manual corrections. </p><p>Non-forest areas were derived by comparing Hansen tree cover loss (through 2004) with areas defined as open land in the 2005 Landsat data. Areas identified as plantation (GlobCover, Ghana; REDD/CCAD-GIZ, Honduras; ISCGM land cover, Thailand) were used to mask the tree cover layer to generate the final resulting land cover layer for 2005.</p>
265092 locations

18 attributes

Tree plantations (from Forest Cover)

Shared by GlobalForestWatch on January 24, 2016. updated 3 months ago.
<p>This data set was created by Transparent World, with the support of Global Forest Watch. Many studies depicting forest cover and forest change cannot distinguish between natural forests and plantations. This data set attempts to distinguish tree plantations from natural forest for seven key countries: Brazil, Cambodia, Colombia, Indonesia, Liberia, Malaysia, and Peru.</p><p>Given the variability of plantations and their spectral similarity to natural forests, this study used visual interpretations of satellite imagery, primarily <a href="http://landsat.usgs.gov/">Landsat</a>, supplemented by high resolution imagery (Google Maps, Bing Maps, or Digital Globe), where available, to locate plantations. Analysts hand-digitized plantation boundaries based on several key visual criteria, including texture, shape, color, and size.</p><p>Each polygon is labelled with the plantation type and when possible, the species. A “gr” in front of the species name indicates a group of species, such as pines or fruit, where the individual species was not identifiable. The percentage of plantation coverage indicates a rough estimate of the prevalence of plantation within a polygon (as in the case of a mosaic). Types are defined as follows:</p><ul><li><strong>Large industrial plantation:</strong> single plantation units larger than 100 hectares</li><li><strong>Mosaic of medium-sized plantations:</strong> mosaic of plantation units < 100 hectares embedded within patches of other land use</li><li><strong>Mosaic of small-sized plantations:</strong> mosaic of plantation units < 10 hectares embedded within patches of other land use.</li><li><strong>Clearing/ very young plantation:</strong> bare ground with contextual clues suggesting it will become a plantations (shape or pattern of clearing, proximity to other plantations, distinctive road network, etc)</li></ul>
-1 locations

0 attributes

Indonesia primary forest (2000) (from Country Data)

Shared by GlobalForestWatch on October 28, 2015. updated 3 months ago.
<p>This data set indicates the location of intact and degraded primary forests across Indonesia for the years 2000, 2005, 2010, and 2012. Primary forest consists of mature natural forest cover that has not been completely cleared in recent history (30 years or more) and exists in a contiguous block of 5 ha or more. </p><p>Primary forest cover was mapped using Landsat composites and multi-temporal metrics as input data to a two-step supervised classification. The first step was a per-pixel classification of areas with tree canopy cover of 30% and above for the 2000 reference year. A second per-pixel classification procedure was performed to separate primary forest from other tree cover for 2000; contiguous areas of 5 ha and greater were retained as primary forest. A limited editing of this classification was performed to remove older plantations and adjust other forest formations that could not be identified using the per-pixel classifier, but could be identified in photo-interpretive contexts. Primary forests were subsequently characterized into primary intact and primary degraded subclasses using the GIS-based buffering approach of the Intact Forest Landscapes (IFL). To create the IFL layer, buffers of roads, settlements and other signs of human landscape alteration were used to identify degraded areas within zones of primary forest cover. IFL mapping employed cloud-free Landsat mosaics to quantify changes in primary intact forest extent. The map of primary intact and primary degraded forest cover types corresponds to the Indonesia Ministry of Forestry’s primary and secondary forest cover types.</p>
-1 locations

0 attributes

Mangrove forests (from Forest Cover)

Shared by GlobalForestWatch on March 24, 2015. updated 3 months ago.
<p>To improve scientific understanding of the extent and distribution of mangrove forests of the world the status and distribution of global mangroves were mapped using recently available Global Land Survey (GLS) data and the Landsat archive.The project interpreted approximately 1000 Landsat scenes using hybrid supervised and unsupervised digital image classification techniques. Results were validated using existing GIS data and the published literature to map ‘true mangroves’.</p><p>The total area of mangroves in the year 2000 was 137,760 km2 in 118 countries and territories in the tropical and subtropical regions of the world. Approximately 75% of world's mangroves are found in just 15 countries, and only 6.9% are protected under the existing protected areas network (IUCN I-IV). Our study confirms earlier findings that the biogeographic distribution of mangroves is generally confined to the tropical and subtropical regions and the largest percentage of mangroves is found between 5° N and 5° S latitude.</p><p>The remaining area of mangrove forest in the world is less than previously thought; the estimate provided in this study is 12.3% smaller than the most recent estimate by the Food and Agriculture Organization (FAO) of the United Nations. This data set presents the most comprehensive, globally consistent and highest resolution (30 m) global mangrove database ever created</p>
-1 locations

0 attributes

Global land cover (2008) (from Forest Cover)

Shared by GlobalForestWatch on April 6, 2015. updated 3 months ago.
<p>GlobCover is a European Space Agency (ESA) initiative which began in 2005 in partnership with the Joint Research Center, European Environmental Agency, UN Food and Agricultural Organization, UN Environment Programme, Global Observation of Forest Cover and Land Cover Dynamics, and International Geosphere-Biosphere Programme. The aim of the project was to develop a service capable of delivering global composites and land cover maps using observations from the 300 meter MERIS sensor on board the ENVISAT satellite mission. ESA makes land cover maps available covering 2 periods: December 2004 - June 2006 and January - December 2009. GlobCover products come with a thematic legend compatible with the UN Land Cover Classification System (LCCS).</p><p>Data in this layer was generated using MERIS images which were classified using both a supervised (human-verified) and unsupervised (automated)classification algorithm applied at two different seasonal time steps to create land cover classes based on both spectral and temporal properties of land cover. The labeling procedure is automated and based on the GlobCover 2005 (v2.2) land cover map. Several decision rules have been defined with the help of international land cover experts to create unique labels for each class.</p>
-1 locations

0 attributes

Wetlands and water bodies (from Global Forest Watch Water)

Shared by GlobalForestWatch on January 25, 2016. updated 6 months ago.
<p>This data set estimates large-scale wetland distributions and important wetland complexes, including areas of marsh, fen, peatland, and water (<a href="http://www.sciencedirect.com/science/article/pii/S0022169404001404">Lehner and Döll 2004</a>). Large rivers are also included as wetlands (lotic wetlands); it is assumed that only a river with adjacent wetlands (floodplain) is wide enough to appear as a polygon on the coarse-scale source maps. Wetlands are a crucial part of natural infrastructure as they help protect water quality, hold excess flood water, stabilize shoreline, and help recharge groundwater (<a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.1995.tb03414.x/abstract">Beeson and Doyle 1995</a>, <a href="http://www.ingentaconnect.com/contentone/saf/njaf/2006/00000023/00000001/art00003?crawler=true">Stuart and Edwards 2006</a>). Limited by sources, the data set refers to lakes as permanent still-water bodies (lentic water bodies) without direct connection to the sea, including saline lakes and lagoons as lakes, while excluding intermittent or ephemeral water bodies. Lakes that are manmade are explicitly classified as reservoirs. The <a href="https://www.worldwildlife.org/pages/global-lakes-and-wetlands-database">Global Lakes and Wetlands Database</a> combines best available sources for lakes and wetlands on a global scale. This data set includes information on large lakes (area ≥ 50 km2) and reservoirs (storage capacity ≥ 0.5 km3), permanent open water bodies (surface area ≥ 0.1 km2), and maximum extent and types of wetlands.</p>
-1 locations

0 attributes

Tree cover (2000) (from Forest Cover)

Shared by GlobalForestWatch on January 5, 2016. updated 3 months ago.
<p>This data set, a collaboration between the <a href="http://glad.geog.umd.edu/">GLAD</a> (Global Land Analysis & Discovery) lab at the University of Maryland, Google, USGS, and NASA, displays tree cover over all global land (except for Antarctica and a number of Arctic islands) for the year 2000 at 30 × 30 meter resolution. “Percent tree cover” is defined as the density of tree canopy coverage of the land surface and is color-coded by density bracket (see legend).</p><p>Data in this layer were generated using multispectral satellite imagery from the <a href="http://landsat.usgs.gov/">Landsat 7</a> thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analyzed using Google Earth Engine, a cloud platform for earth observation and data analysis, to determine per pixel tree cover using a supervised learning algorithm.</p><p>The tree cover canopy density of the displayed data varies according to the selection - use the legend on the map to change the minimum tree cover canopy density threshold.</p>
2220 locations

10 attributes

Intact Forest Landscapes (2000) (from SDG15: Life on Land)

Shared by GlobalForestWatch on September 15, 2015. updated 6 months ago.
<p>The <a href="http://intactforests.org/">Intact Forest Landscapes</a> (IFL) data set identifies unbroken expanses of natural ecosystems within the zone of forest extent that show no signs of significant human activity and are large enough that all native biodiversity, including viable populations of wide-ranging species, could be maintained. To map IFL areas, a set of criteria was developed and designed to be globally applicable and easily replicable, the latter to allow for repeated assessments over time as well as verification. IFL areas were defined as unfragmented landscapes, at least 50,000 hectares in size, and with a minimum width of 10 kilometers. These were then mapped from Landsat satellite imagery for the year 2000.</p><p>Changes in the extent of IFLs were identified within year 2000 IFL boundary using the global wall-to-wall Landsat image composite for year 2013 and the global forest cover loss dataset (Hansen et al., 2013). Areas identified as “reduction in extent” met the IFL criteria in 2000, but no longer met the criteria in 2013. The main causes of change were clearing for agriculture and tree plantations, industrial activity such as logging and mining, fragmentation due to infrastructure and new roads, and fires assumed to be caused by humans.</p><p>This data can be used to assess forest intactness, alteration, and degradation at global and regional scales.</p>
-1 locations

0 attributes

Erosion (from Global Forest Watch Water)

Shared by GlobalForestWatch on January 26, 2016. updated 6 months ago.
<p>This layer shows the risk of erosion around the world, from low to high. Erosion and sedimentation by water involves the process of detachment, transport, and deposition of soil particles, driven by forces from raindrops and water flowing over the land surface. Because soil erosion is difficult to measure at large scales, soil erosion models are crucial estimation tools to extrapolate limited data to other localities and conditions. The Revised Universal Soil Loss Equation (RUSLE), which predicts annual soil loss from rainfall and runoff, is the most common model used at large spatial scales due to its relatively simple structure and empirical basis. The model takes into account rainfall erosivity, topography, soil erodibility, land cover and management, and conservation practices. Because the RUSLE model was developed based on agricultural plot scale and parameterized for environmental conditions in the USA, modifications of the methods and data inputs are necessary to make the equation applicable to the globe. We estimated erosion potential based on the RUSLE model, adjusted to extend its applicability to a global scale. Conservation practices and topography information were not included in this model to calculate global erosion potential, due to data limitation and their relatively minor contribution to the variation in soil erosion at the continental to global scale compared to other factors. The result of the global model was categorized into five quantiles, corresponding to low to high erosion risks.</p>