Timor-Leste: Copernicus Vegetation Index Anomaly (FAPAR Anomaly)

Miscellaneous events are often the result of the cumulative impact of a combination of abiotic, insect and disease agents or events.  For example, Aspen decline where repeated infestations of Forest Tent Caterpillar, are combined with several seasons of prolonged drought. The Government of Ontario tracks forest damage events to help proactively manage the detrimental effects to our forests.   We monitor the threat and spread of invasive forest pest insect species in Ontario. The data is also important to the Forest Management Planning process in calculating timber volume loss within affected areas. Additional Documentation Forest Misc Damage Event - Documentation (Word) Forest Misc. Damage Event - Data Description (PDF) Status On going: data is being continually updated Maintenance and Update Frequency As needed: data is updated as deemed necessary Contact Calvin Julie, calvin.julie@ontario.ca Recommendations The boundaries for these events were coarsely mapped. This must be taken into account when comparing information from this data class to other NRVIS layers that have a higher spatial accuracy (e.g.:Forest Cover). Otherwise, misleading results will be derived whenever this information is subjected to analysis. This NRVIS data class accommodates history for events spanning several years which includes overlaps. Users should request and/or sort out individual events through the 'Year of Event' ,'Event Name' and 'Forest Damage Ranking' attributes in order to prevent clutter.

Site Regions and Site Districts of Ontario represent an early ELC system originally developed by Angus Hills (1959 and later revisions). This dataset was revised by the ELC Working Group in 2000 to better reflect new information and new technology, while maintaining Hills's original concepts. For further information on the rationale for the revisions, refer to Ecoregions of Ontario Modifications to Angus Hills Site Regions and Districts, Revisions and Rationale (Crins and Uhlig 2000), or the Ecological Land Classification Primer (Crins 2007). Additional Documentation Site Region - Data Description (PDF) Site Region - Documentation (Word) Status Completed: production of the data has been completed Maintenance and Update Frequency Not planned: there are no plans to update the data Contact Peter Uhlig, Peter.Uhlig@ontario.ca

This data product contains raster data depicting the spatial distribution of forest ownership types in the conterminous United States circa 2009. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local, as well as three types of private ownership: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, unincorporated partnerships and associations, and Native American tribal lands). The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.

Woodland areas in the Commonwealth of Kentucky derived from 2016 USDA-NASS data for use on the KyTopo map series. Cropland Data Layer by USDA-NASS, 2016 (https://datagateway.nrcs.usda.gov/). The different classifications of tree cover were pooled together to create a woodlands polygon. Classes include: Evergreen Forest, Mixed forest, Forest, Christmas trees, Other Tree Crops, Wetlands and Woody Wetlands. The polygon was then smoothed and reworked using algorithms to conform to the KyTopo maps. Online Linkage: https://ky.box.com/v/kymartian-KyTopo-Woodlands/

CWHR Predicted Habitat Models represent areas of predicted suitable habitat for each species within its range. These models are built from the following principal inputs: 1) a statewide, best-available vegetation map (FVEG); 2) GIS data representing a species’ range; 3) the CWHR database of habitat suitability values for over 700 terrestrial vertebrate species. Habitat suitability ranks of Low (non-zero values less than 0.34), Medium (0.34-0.66), and High (greater than 0.66) are based on the maximum suitability value across the 3 species life requisites: reproduction, feeding, and cover. Note that previous versions of these Predicted Habitat Models used an average across the 3 life requisites in order to obtain an overall suitability score for each habitat type and stage class. Habitat suitability scores were developed based on habitat patch sizes greater than 40 acres in size and are best interpreted for habitat patches greater than 200 acres in size. The FVEG landcover dataset is an aggregation of multiple statewide landcover and regional vegetation mapping efforts, conducted at different points in time (approximately 1990 up to time of publishing) and at various resolutions, compiled by the California Department of Forestry and Fire Protection (CALFIRE). FVEG uses the most current and consistent data available for each region of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Crosswalks were used to attribute the various data sources according to the CWHR habitat-type classification system. Attributing FVEG with CWHR habitat types allows for the extraction of areas with non-zero suitability values for each species within the bounds of its range, creating a series of maps of predicted suitable habitat which are species-specific. Because FVEG is an amalgam of disparate landcover assessment efforts across the state, the predictive power for determining suitable habitat will vary between species, and possibly even regionally for species which are widely distributed. While these maps represent CDFW’s best estimate of the presence of suitable habitat for any given species in the CWHR system, these maps are also limited by several factors: 1) the accuracy and resolution of vegetation maps in a given region; 2) the dynamic nature of the landscape in which fire and other disturbance events alter conditions at a greater frequency than mapping efforts can track; 3) the currency of expert knowledge, particularly as species adapt to changing land and climate conditions and the shifting of other species’ ranges; 4) the frequency of species-specific surveys across a representative sample of a species’ entire range; 5) metapopulation dynamics, which describes the shifting of populations within their environment as result of numerous types of interactions and responses. CWHR GIS data representing predicted suitable habitat should not be used to indicate the presence or absence of a particular species at any specific site. CWHR predicted habitat models are named according to the 4-character alpha-numeric CWHR ID assigned to each species (5 characters in the case of subspecies or other sub-taxa). There is also a “CWHR Revision Tracking Table” containing a record for each species, its CWHR ID, scientific name, common name, and range and habitat model data revision history. CWHR species range models, predicted habitat models, and GIS data of the statewide distribution of all CWHR habitat types, along with the CWHR revision tracking table, are available for download at https://www.wildlife.ca.gov/Data/CWHR.

Seagrass beds are vital coastal ecosystems that provide habitat and nursery grounds for a diverse array of marine life, supporting rich biodiversity. They also play a crucial role in stabilizing sediments, improving water quality, and capturing carbon, making them essential for healthy oceans and climate regulation. This layer shows the extent of shallow-water seagrass beds derived from Sentinel-2 imagery from 2023-2024, based on analysis by Peng et al (Nature 2026).    Dataset Summary Phenomenon Mapped: Shallow-water seagrass extent Geographic Extent: Global Layer Projection: WGS 1984 Web Mercator Cell Size: 10-meters Pixel Depth: 1-bit integer Update Frequency: None Source: Peng et al 2026 Data Vintage: June 2026 Publication Date: July 2026 Analysis: Supports analysis What can you do with this layer? This tile imagery service is designed for fast data visualization and analysis. Identify pixels by clicking on the map to reveal the pre-configured pop-ups. It can be used to model areas suitable for seagrass habitat. An additional 2019-2020 analysis is available from the source.  Citation: Peng, J. (2026). Global 10-m shallow-water seagrass extent mapping code. Zenodo. https://doi.org/10.5281/zenodo.18212209 Revisions None Questions? Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.