Raster of Active Fires frequency per square kilometer for the period 01/01/2019 - 25/09/2019, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. <br> This raster layer was produced by GRID-Geneva. Data accessed in October 2019, at https://modis.gsfc.nasa.gov/data/dataprod/mod14.php. <br> <br> The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. <br> <br> Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.

Raster of Active Fires frequency per square kilometer, based on MODIS Collection 6 Active Fire Product MCD14ML. Only location points described as "presumed vegetation fire" in the attributes are included in the frequency calculation. This raster layer was produced by GRID-Geneva. Data accessed in December 2018, at <a href="https://modis.gsfc.nasa.gov/data/dataprod/mod14.php" target="_blank">https://modis.gsfc.nasa.gov/data/dataprod/mod14.php</a>. The MODIS active fire product detects fires in 1-km pixels that are burning at the time of overpass under relatively cloud-free conditions using a contextual algorithm. Please see the <a href="http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf" target= "_blank">MODIS Active Fire Product User's Guide</a> for detailed information about the MODIS active fire product suite. Giglio, L., Schroeder, W., and Justice, C. O., 2016, The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment, 178:31-41.

The forest integrrity index is derived by overlaying the human footprint (Venter et al. 2016) on the forest structural condition. The name is consistent with the concept of ecological integrity. Ecological integrity has been defined as, “the system’s capacity to maintain structure and ecosystem functions using processes and elements characteristic for its ecoregion.” (Parks Canada 2008). This capacity is a result of the climate, soil, topography, biota and other biophysical properties of the ecoregion and the extent to which these properties are not altered by modern human pressures. Consistent with this definition, the forest integrity index is based on on the structural complexity of a stand relative to the natural potential of the ecoregion and level of human pressure. Thus, forest of high integrity are relatively tall, high in canopy cover, older, and with relatively low human pressure. An increasing number of studies have shown that human pressure in various forms can have negative effects on native species.  Thus, high integrity forests may be uniquely important for conservation because they support species and processes that are require well-developed forests and are sensitive to human activities.  Such forests often also have high economic value and have likely been preferentially converted to more intense human land uses.  Thus, identifying remaining areas of high forest integrity is important for conservation planning.<br><br>Data is provided by Montana State University.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>

These layers show the average values by decade (60s, 70s, 80s, 90s, 00s, 10s) over the time interval from 1960 to 2018 of the Self-calibrating Palmer Drought Severity Index (scPDSI), based on the scPDSI dataset provided by the Climatic Research Unit (CRU) that cover the time interval from 1901 to 2018. Original data were subset and processed using QGIS at UNEP/GRID-Geneva.<br><br> The scPSDI indicates the degree of drought severity (negative values means higher severity) based on climatic and environmental parameters. The scPDSI metric was introduced by Wells et al. (2004), who give detailed information about its calculation. The scPDSI is a variant on the original PDSI of Palmer (1965), with the aim to make results from different climate regimes more comparable. As with the PDSI, the scPDSI is calculated from time series of precipitation and temperature, together with fixed parameters related to the soil/surface characteristics at each location. <br><br> The dataset has been updated each year using newer versions of CRU TS input data, currently to the end of 2018 using a preliminary version of CRU TS 4.03 (0.5° resolution). <br> Please read <a href="https://crudata.uea.ac.uk/cru/data/drought/scpdsi.global2018.readme.txt"target=_blank>this document</a> for more information. <br><br> (Dataset method) van der Schrier G, Barichivich J, Briffa KR and Jones PD (2013) A scPDSI-based global data set of dry and wet spells for 1901-2009. J. Geophys. Res. Atmos. 118, 4025-4048 (10.1002/jgrd.50355).<br> (1901-2018 update) Barichivich J, Osborn TJ, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2018"]. Bulletin of the American Meteorological Society under review.<br> (1901-2017 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2018) Drought [in "State of the Climate in 2017"]. Bulletin of the American Meteorological Society 99, S36-S37. (doi:10.1175/2018BAMSStateoftheClimate.1)<br> (1901-2016 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2017) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2016"]. Bulletin of the American Meteorological Society 98, S32-S33 (doi:10.1175/2017BAMSStateoftheClimate.1) (available here).<br> (1901-2015 update) Osborn TJ, Barichivich J, Harris I, van der Schrier G and Jones PD (2016) Monitoring global drought using the self-calibrating Palmer Drought Severity Index [in "State of the Climate in 2015"]. Bulletin of the American Meteorological Society 97, S32-S36 (available here). <br><br> Data were downloaded from <a href="https://crudata.uea.ac.uk/cru/data/drought/"target=_blank>CRUDATA/Drought</a>

The distribution of forest biomass vertically and horizontally is an important predictor of biodiversity, disturbance risk, carbon storage, and hydrological flows. Human activities may alter the influence of forest structure on biodiversity through hunting, introducing non-native species, and altering disturbance regimes. The authors introduce two new remotely sensed indices describing forest structure and human pressure in tropical forests. The Forest Structural Condition Index (SCI) uses best existing global forest data sets to represent a gradient from low to high forest structure development. Remotely sensed estimates of canopy height, tree cover, and time since disturbance comprise inputs of the index. The index distinguishes short, open-canopy, or recently disturbed stands such as those recently deforested from tall, closed-canopy, older stands typical of primary of late secondary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar and estimates of aboveground biomass derived from forest inventory plots. The Forest Integrity Index overlays an index of human pressure, the Human Footprint, on SCI to identify structurally complex forests with low human pressure that are likely to be most valuable for biodiversity and ecosystem services. The SCI and Forest Integrity Index are being used to assess progress for countries in reaching the 2020 forest fragmentation and connectivity targets under the Convention on Biodiversity. Broader potential applications include using the SCI and Forest Integrity as predictors of habitat quality, community richness, carbon storage, hydrological yield, and restoration of secondary forest.<br><br>This dataset is provided from the University of Montana through a partnerhsip with the NASA Biodiversity and Ecological Forecasting Program.<br/><br>License information: <a href "https://creativecommons.org/licenses/by/4.0/" target="_blank">CC-4.0 Attribution</a>.<br/>