LANDFIRE (LF) National (2001) was the original product suite of the LANDFIRE program, which included Existing Vegetation Cover (EVC), Height (EVH), and Type (EVT). Subsequent refinements after feedback from data users resulted in updated products, referred to as LF 2001, that now served as LANDFIRE's baseline datasets and are the basis for all subsequent LANDFIRE updates. These updates account for disturbances and vegetation transition changes that may not represent current vegetation conditions. Therefore, in 2016 LANDFIRE initiated the Remap prototype to determine how to undertake a national-scale remap of the LANDFIRE primary vegetation datasets. EVC, EVH, and EVT were produced (circa 2015) via modeling for ecologically variable prototyping areas in the Pacific Northwest (NW) and Grand Canyon (GC). An error analysis within the GC suggested an overall accuracy of 52% (N = 800) for EVT, and a goodness of fit of 51% (N = 38) for percent cover (continuous EVC) and 53% (N = 38) for height (continuous EVH). The prototyping effort included a new 81-class map using the National Vegetation Classification (NVC) within the NW. This paper presents a narrative of the innovative methodologies in image processing and mapping used to create the new LANDFIRE vegetation products. Fire 2019, 2, 35 2 of 26 LANDFIRE (LF) 2001 was a joint 5-year project between the U.S. Department of Agriculture (USDA) Forest Service and U.S. Department of the Interior (DOI) to provide vegetation and fuel datasets for the conterminous United States circa 2001 [6]. A foundation for this suite of data products was the LANDFIRE Reference Database (LFRDB), which was developed to hold ground-referenced plot data information about vegetation types and structure metrics (i.e., height and cover). Data sources included Forest Inventory and Analysis (FIA), U.S. Geological Survey (USGS) National Gap Analysis Program (GAP), the Nature Conservancy, and other federal, state, and local datasets [6].Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) are the foundational geospatial datasets used by LANDFIRE. Descriptions of the Landsat bands and indices used by LANDFIRE are shown in Table 1a,b, respectively. TM and ETM+ images were not available cost-free during the production timeframe of LF 2001; however, LANDFIRE was a member of the Multi-Resolution Land Characteristics (MRLC) Consortium [8], which gave the program access to any previously purchased Landsat scenes. Landsat scenes not in the MRLC archive were purchased to meet the goal of three cloud-free images per Landsat World Wide Referencing System 2 (WRS2) Path/Row for the conterminous United States, Alaska, and Hawaii [6]. Additional remotely sensed data sources including a digital elevation model (DEM), slope, elevation, and biophysical gradients (e.g., temperature and precipitation) were also acquired or produced as needed.Classification and regression tree (CART) models were developed to determine vegetation types, while regression tree models were used to classify vegetation structur...
Aims: Natural resource management and biodiversity conservation rely on inventories of vegetation that span multiple management or political jurisdictions. However, while remote sensing data and analytical tools have enabled production of maps at increasing spatial resolution and reliability, there are limited examples where national or continental-scaled maps are produced to represent vegetation at high thematic detail. We illustrate two examples that have bridged the gap between traditional land cover mapping and modern vegetation classification. Study area: Our two case studies include national (USA) and continental (North and South America) vegetation and land cover mapping. These studies span conditions from subpolar to tropical latitudes of the Americas. Methods: Both case studies used a supervised modeling approach with the International Vegetation Classification (IVC) to produce maps that provide for greater thematic detail. Georeferenced locations for these vegetation types are used by machine learning algorithms to train a predictive model and generate a distribution map. Results: The USALANDFIRE (Landscape Fire and Resource Management Planning Tools Project) case study illustrates how a history of vegetation-based classification and availability of key inputs can come together to generate standard map products covering more than 9.8 million km2 that are unsurpassed anywhere in the world in terms of spatial and thematic resolution. That being said, it also remains clear that mapping at the thematic resolution of the IVC Group and finer resolution require very large and spatially balanced inputs of georeferenced samples. Even with extensive prior data collection efforts, these remain a key limitation. The NatureServe effort for the Americas - encompassing 22% of the global land surface - demonstrates methods and outputs suitable for worldwide application at continental scales. Conclusions: Continued collection of input data used in the case studies could enable mapping at these spatial and thematic resolutions around the globe. Abbreviations: CART = Classification and Regression Tree; CONUS = Conterminous United States; DSWE = Dynamic Surface Water Extent; EPA = United States Environmental Protection Agency; FGDC = Federal Geographic Data Committee; IVC = International Vegetation Classification; LANDFIRE = Landscape Fire and Resource Management Planning Tools Project; LFRDB = LANDFIRE Reference Database; LiDAR = Light Detection and Ranging; NDVI = Normalized Difference Vegetation Index; NLCD = National Land Cover Database; USNVC = United States National Vegetation Classification; USA = United States of America; WWF = World Wildlife Fund or Worldwide Fund for Nature.
Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1&2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have > 17% of their extent in Status 1&2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.
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