Landscape Condition (Southeast Blueprint Indicator)

Reason for Selection A high degree of naturalness across the landscape benefits species diversity as well as ecosystem services such as pollinator habitat, increased water infiltration, and reduced soil erosion. Though much of the Southeast has experienced human alteration at some point, natural landcover across the wider landscape provides many benefits. It allows species to disperse during different life stages, better adapt to a changing climate by accessing refugia, and freely move between different habitats. Natural landscapes can also complement existing protected areas and help increase resilience to extreme weather events such as flooding and hurricanes (Kremen and Merenlender 2018). This indicator is loosely based on an approach for evaluating land use intensity as part of the “landscape integrity” metric developed by the University of Florida Center for Landscape Conservation Planning for the [Florida Critical Lands and Waters Identification Project] (CLIP) (Oetting et al. 2016). Input Data * [National Land Cover Database] (NLCD): [2021 Land Cover], [2021 U.S. Forest Service (USFS) Tree Canopy Cover] * Elevation-based mask used to separate the marine environment from terrestrial areas: available in the [Southeast Blueprint 2025 Ancillary Data Download]; see Appendix II of [the Southeast Blueprint 2025 Development Process] for details on how this layer was developed * [Global-scale mining polygons (Version 2)]; [download the geopackage]; [see the layer in an online viewer]; [read a journal article] about the data development * 2020 [LANDFIRE Existing Vegetation Type] (EVT) [LF 2.2.0] * 2020 [LANDFIRE Biophysical Settings] (BPS) [LF 2.2.0] * [Grassland and savanna extent] (Southeast Blueprint indicator) - intermediary code output * Southeast Blueprint reservoirs mask: available in the [Southeast Blueprint 2025 Ancillary Data Download]; see Appendix II of [the Southeast Blueprint 2025 Development Process] for details on how this layer was developed Mapping Steps * Reclassify the 2021 NLCD landcover into 3 alteration classes where 3 is natural, 2 is altered, and 1 is heavily altered. Assign a value of 1 to all pixels with a landcover class of “Developed, High Intensity” or “Developed, Medium Intensity”. Assign a value of 2 to all pixels with a landcover class of “Developed, Open Space”, “Developed, Low Intensity”, “Hay/Pasture”, or “Cultivated Crops”. Assign a value of 3 to everything else. * Use LANDFIRE BPS to identify historic grasslands in the western part of the region that wouldn’t typically have trees by selecting the following values in the BPS_NAME field: ‘Southern Blackland Tallgrass Prairie’, ‘Central Mixedgrass Prairie’, ‘Chihuahuan Loamy Plains Desert Grassland’, ‘Southeastern Great Plains Tallgrass Prairie’, ‘Western Great Plains Sand Prairie’, and ‘Western Great Plains Shortgrass Prairie’. Then use the 2021 NLCD USFS Tree Canopy Cover layer to identify current treeless areas by selecting all canopy cover values <1. Combine these layers to identify parts of historic treeless grasslands that now have trees, then convert those pixels from 3 (natural) into 2 (altered). * Convert the mining polygons to raster. Use the resulting raster to convert pixels within mine footprints classified as 3 (natural) into 2 (altered). * Extract the “Quarries-Strip Mines-Gravel Pits-Well and Wind Pads” class from the LANDFIRE EVT data. Reproject and align with NLCD. There are groups of location errors in this class in specific parts of South Alabama and West Mississippi. To remove these location errors, remove quarry pixels classified in NLCD as evergreen, mixed, or deciduous forest (41, 42, 43). * Use this improved depiction of “Quarries-Strip Mines-Gravel Pits-Well and Wind Pads” to convert pixels from 3 (natural) to 2 (altered). We use the mining polygons and LANDFIRE quarries class to attempt to distinguish altered areas of bare earth or rock (such as quarries, industrial areas, mines, and oil and gas well drilling pads) from natural areas of bare earth or rock (such as rock outcroppings and beaches). * Extract the “Northeastern North American Temperate Forest Plantation” class from the LANDFIRE EVT data. Reproject, align with NLCD, and covert these areas in these pixels that are classified as 3 (natural) into 2 (altered). This class only occurs in the Appalachians and Interior Plateau subregions and addresses a past known issue of the Blueprint overprioritizing pine plantations on the Cumberland Plateau. It does not impact pine plantations in other subregions like the Coastal Plain. * Reclassify the reservoirs layer to change NoData values to 1 so that all reservoirs receive a value of 0, and all non-reservoirs receive a value of 1. The reservoir layer was previously clipped to the Southeast Blueprint extent, so this change ensures that the circular focal window analysis in one of the following steps can look beyond the Southeast Blueprint boundary to properly calculate scores. * Use the reservoirs layer to covert reservoir pixels from 3 (natural) to 2 (altered). * Create a raster with the known and likely grassland classes (values 5, 6, and 7) from the grasslands and savannas indicator. Use this raster to convert grassland areas classified as 2 (altered) based on NLCD landcover to 3 (natural). NLCD often misclassifies natural grasslands and savannas as hay/pasture, which would otherwise lower their landscape condition score. * Use the elevation-based mask raster to remove marine areas from the reclassified landcover layer by assigning them a value of NoData. * Many species and ecological processes operate at multiple scales. To account for this, estimate the average amount of alteration using a circular moving window (or neighborhood) analysis at 4 different scales: approximately 0.22 acres (single pixel), approximately 10 acres, approximately 100 acres, and approximately 1,000 acres. Then average the values across all scales. This results in continuous values ranging from 1 to 3. * Bin the continuous values into the following categories seen in the final indicator values below: 1 (heavily altered): 1 to <1.5; 2 (altered): 1.5 to <2; 3 (partly natural): 2 to <2.5; 4 (mostly natural): 2.5 to <2.9; 5 (natural): 2.9 to <2.99; 6 (very natural): 2.99 to 3. These breaks align with different levels of alteration. For example, an average value of 2.99 reflects a very natural landscape where only 1% of the area is altered and everything else is natural. That amounts to one altered pixel (which scores a 2) for every 99 natural pixels (which score a 3). An average value of 2 represents a partly natural landscape with an equal number of heavily altered and natural areas. For example, a landscape with an equal number of altered pixels (scoring a 2), heavily altered pixels (scoring a 1), and natural pixels (scoring a 3) would have a value of 2. * As a final step, clip to the spatial extent of Southeast Blueprint 2025. Note: For more details on the mapping steps, code used to create this layer is available [in the Southeast Blueprint 2025 Data Download] under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = Very natural landscape 5 = Natural landscape 4 = Mostly natural landscape 3 = Partly natural landscape 2 = Altered landscape 1 = Heavily altered landscape Known Issues * This indicator underestimates landscape condition in many areas composed of native grasses and forbs on private lands. NLCD landcover often classifies these areas as “Hay/Pasture”. Most of the areas classified as “Hay/Pasture” are partly altered areas, but some are functioning as natural grasslands either with or without grazing. The indicator does use known and likely grassland data to correct for this issue, but the known grassland data is missing many grassland areas in the Southeast and the likely grassland prediction miss many areas on private lands. * This indicator may overestimate landscape condition around places like quarries, mines, wind pads, and other industrial sites. The indicator considers these areas to have the same level of alteration as pine plantations, crops, pastures, low-density development, and open space in developed areas. Treating this range of landcover types as equally altered does not reflect their varying levels of human modification compared to natural areas. Future improvements may consider expanding alteration classes to better capture these nuances. * This indicator does not account for variation in habitat condition due to invasive species. * In arid parts of Southwest TX, this indicator underprioritizes some naturally open areas. The combination of LANDFIRE EVT and NLCD landcover does a fairly good job of distinguishing natural from altered open areas but occasionally misclassifies a small set of natural areas that should score higher. * This indicator overestimates landscape condition in some areas in the Everglades Headwaters National Wildlife Refuge proclamation boundaries. Many, but not all, pastures in that area function similarly to native grasslands. * This indicator may overestimate landscape condition in places with thin linear alterations (e.g., railroads, thin bridges, pipelines). NLCD can sometimes classify those pixels as natural landcover categories—in part due to the small area they cover within a 30 m pixel. * Because the reservoir layer used in this indicator was previously clipped to the Blueprint extent, and the landscape condition moving window analysis looks beyond the Blueprint extent, this indicator will overestimate condition along the edges of the Blueprint in places where reservoirs are present within approximately 1 mi outside the Blueprint boundary. * Because this indicator is intended to represent terrestrial and freshwater ecosystems, we attempted to remove the marine environment so that the ocean does not inflate landscape condition scores in coastal areas. For example, we did not want nearby “unaltered” ocean pixels to raise the score of an otherwise heavily developed barrier island. However, in aquatic systems, it is difficult to determine where freshwater ends and saltwater begins, as that transition is highly dynamic and influenced by many factors including topography, instream flow, tides, storms, and more. To define the extent of this indicator, we used an elevation-based approach that includes all areas greater than -1 m in elevation (i.e., all areas above 1 m below sea level), then backfilled to capture low-lying coastal areas that are disconnected from the ocean. We intentionally erred on the side of including more terrestrial and freshwater areas so as not to exclude small islands and coastal freshwater lakes (like Lake Mattamuskeet in NC). This is an imperfect methodology but produced better results than some of the other alternatives we explored. Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email [hilary_morris@fws.gov]). Literature Cited Dewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. [[https://doi.org/10.5066/P9JZ7AO3]]. Kremen, Claire, and Adina M. 2018. Merenlender. Landscapes that work for biodiversity and people. Science 362.6412. Eaau6020. [[https://www.science.org/doi/10.1126/science.aau6020]]. Landscape Fire and Resource Management Planning Tools (LANDFIRE), Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. Published 2023-05-01. LANDFIRE 2022 Existing Vegetation Type (EVT) CONUS. LF 2022. Raster digital data. Sioux Falls, SD. [[https://landfire.gov/evt.php]]. Maus, V., Giljum, S., Gutschlhofer, J. et al. A global-scale data set of mining areas. Sci Data 7, 289 (2020). [[https://doi.org/10.1038/s41597-020-00624-w]]. Oetting J, Hoctor T, and Volk M. 2016. Critical Lands and Waters Identification Project (CLIP): Version 4.0 Technical Report. Accessed Nov 10, 2022. [[https://www.fnai.org/PDFs/CLIP_v4_technical_report.pdf]]. U.S. Census Bureau. Feature Catalog for the 2018 United States 1:500,000 Cartographic Boundary Files (Shapefile). 2019-05. Current State and Equivalent (national). [[https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html]]. Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123. [[https://doi.org/10.1016/j.isprsjprs.2018.09.006]].