The Land Parcel Identification System (LPIS) is a reference database of the agriculture parcels used as a basis for area related payments to farmers in relation to the Common Agricultural Policy (CAP). These payments are (co)financed by the European Agricultural Guarantee Fund (‘EAGF’) and the European Agricultural Fund for Rural Development (‘EAFRD’). To ensure that payments are regular, the CAP relies on the Integrated Administration and Control System (IACS), a set of comprehensive administrative and on the spot checks on subsidy applications, which is managed by the Member States. The Land Parcel Identification System (LPIS) is a key component of the IACS. It is an IT system based on ortho imagery (aerial or satellite photographs) which records all agricultural parcels in the Member States. It serves two main purposes: to clearly locate all eligible agricultural land contained within reference parcels and to calculate their maximum eligible area (MEA). The LPIS is used for cross checking during the administrative control procedures and as a basis for on the spot checks by the paying agency. Description copied from catalog.inspire.geoportail.lu.

Reason for Selection Habitat near rivers and streams is strongly linked to water quality and instream flow (Naiman 1997). Intact vegetated buffers within the floodplain of rivers and streams provide aquatic habitat, improve water quality, reduce erosion and flooding, recharge groundwater, and more (WeConservePA 2014). Natural floodplain landcover is often described as “the first line of defense” for aquatic systems. Input Data2021 National Land Cover Database (NLCD)Southeast Blueprint 2024 extentEstimated Floodplain Map of the Conterminous U.S. from the Environmental Protection Agency’s (EPA) EnviroAtlas; see this factsheet for more information; download the data The EPA Estimated Floodplain Map of the Conterminous U.S. displays “...areas estimated to be inundated by a 100-year flood (also known as the 1% annual chance flood). These data are based on the Federal Emergency Management Agency (FEMA) 100-year flood inundation maps with the goal of creating a seamless floodplain map at 30-m resolution for the conterminous United States. This map identifies a given pixel’s membership in the 100-year floodplain and completes areas that FEMA has not yet mapped” (EPA 2018). National Hydrography Dataset Plus (NHDPlus) Version 2.1 medium resolution catchments (note: V2.1 is just the current sub-version of the dataset generally called NHDPlusV2); view the user guideCatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics. To learn more about catchments and how they’re defined, check out these resources:An article from USGS explaining the differences between various NHD productsThe glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key termsMapping StepsClip the 2021 NLCD to the EPA estimated floodplain layer.Reclassify the clipped NLCD to identify natural landcover using the following classes: open water, barren land, deciduous forest, evergreen forest, mixed forest, scrub/shrub, grassland/herbaceous, woody wetlands, and emergent wetlands.Calculate the percent of riparian natural landcover inside each NHDPlus catchment using ArcPy Spatial Analyst Zonal Statistics “MEAN” function.Take the resulting raster times 100 to convert from a decimal to whole number percent.Reclassify the above raster into the 1-5 classes seen in the final indicator values below.Clip the resulting raster back to the EPA estimated floodplain layer. It is necessary to do this again since the Zonal Statistics function outputs pixel values for the entire catchment. During this step, assign a value of 0 to areas outside the EPA floodplain. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows:5 = >90% natural landcover within the estimated floodplain, by catchment4 = >80-90%3 = >70-80%2 = >60-70%1 = ≤60% natural landcover within the estimated floodplain, by catchment0 = Not identified as a floodplainKnown IssuesSmall headwaters and creeks are not included in this indicator because the EPA estimated floodplain dataset does not include them.This indicator does not account for the accumulated impacts of upstream riparian buffers. Buffers at the headwaters are treated the same as those downstream.This indicator does not consider the river or stream size in relation to the estimated floodplain. Aquatic habitat needs may differ based on the river size class. For example, smaller headwater streams may need more natural landcover than larger rivers to maintain aquatic health. It also does not account for variation in buffer quality within the floodplain at a scale below the catchment. This means that within the estimated floodplain, loss of natural habitat adjacent to the river is treated the same as loss farther away.While this indicator generally includes the open water area of reservoirs, some open water portions of reservoirs (e.g., Kerr Lake in NC/VA) are missing from the estimated floodplain dataset.The catchment boundaries are inconsistent in how far they extend toward the ocean. As a result, this indicator does not consistently apply to estuaries, coastal areas, and barrier islands.In the area just south of Guadalupe Mountains National Park in West Texas, this indicator depicts the floodplain as a series of straight lines that poorly match the actual floodplain. This is due to an error in the EPA floodplain map used in this indicator.The catchment boundaries cross the United States/Mexico border, but the NLCD impervious data does not; as a result, the values along the United States/Mexico border are only based on the portion of the catchment where there are NLCD impervious values.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 CitedDewitz, J., 2023, National Land Cover Database (NLCD) 2021 Products: U.S. Geological Survey data release. [https://doi.org/10.5066/P9JZ7AO3]. EPA EnviroAtlas. 2018. Estimated Floodplain Map of the Conterminous U.S. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/Supplemental/EstimatedFloodplains.pdf]. Naiman, Robert J., and Henri Decamps. “The Ecology of Interfaces: Riparian Zones.” Annual Review of Ecology and Systematics 28 (1997): 621–58. [https://www.nativefishlab.net/library/textpdf/19487.pdf]. U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). 2012. National Hydrography Dataset Plus 2. [https://www.horizon-systems.com/nhdplus/]. WeConservePA. 2014. ConservationTools.org: The Science Behind the Need for Riparian Buffer Protection. [https://conservationtools.org/guides/131-the-science-behind-the-need-for-riparian-buffer-protection]. 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].

Version 2 minor revision 27 June 2024.\r \r This is the latest compilation of land use mapping information for Australia’s regions as at December 2023. The land use data are supported by a supplementary commodities dataset, containing extra information on the location of select predominantly agricultural commodities. These datasets replace the previous 2020 December updates. \r Version 2 fixes issues caused during the conversion of the state vector datasets to rasters, where single pixel horizontal lines were generated in local areas. This does not affect the date or scale of mapping.\r \r These data were compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) from vector land use datasets collected as part of state and territory mapping programs and other authoritative sources through the Australian Collaborative Land Use and Management Program (ACLUMP). These datasets are not recommended for change analysis or for national land use statistics—instead use the Land use of Australia 2010-11 to 2015-16.\r \r About the Catchment Scale Land Use of Australia – Update December 2023 spatial dataset:\r \r A seamless raster dataset that combines land use vector data for all state and territory jurisdictions, at a spatial resolution of 50 by 50 metres.\r Shows a single dominant land use for each location, based on the management objective of the land manager (as identified by state and territory agencies).\r Updates have been made to New South Wales, Northern Territory, Tasmania, Victoria, the capital city of Adelaide, parts of the Great Barrier Reef NRM regions, and national updates to select horticultural tree crops and protected cropping structures. There are also minor corrections to Western Australia, and more accurate representation of mining areas in South Australia. \r The date of mapping (2008 to 2023) and scale of mapping (1:5,000 to 1:250,000) vary and are provided as supporting datasets. \r Produced by combining land tenure and other types of land use information, fine-scale satellite data and information collected in the field. \r Refer to the metadata and ABARES website for additional information.\r \r About the Catchment Scale Land Use of Australia – Commodities – Update December 2023 spatial dataset:\r - Provides location, extent and year verified for 185 commodities, where mapped, as a vector dataset. \r - Commodity data are validated in the field and using other sources.\r - Generally, a single commodity is shown at a location reflecting the most recent date that location was verified.\r - The location of a commodity may change on a seasonal to annual basis, depending on factors such as climate, markets or farming systems.\r - Not nationally complete or comprehensive, and with various dates of capture (1967 to 2023) and input mapping products (2014 to 2023). \r - Refer to the metadata for additional information.\r \r Citation\r - Land use: ABARES 2024, Catchment Scale Land Use of Australia – Update December 2023 version 2, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, June, CC BY 4.0, DOI: 10.25814/2w2p-ph98\r - Commodities: ABARES 2024, Catchment Scale Land Use of Australia – Commodities – Update December 2023, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, February CC BY 4.0. DOI: 10.25814/zfjz-jt75

Recommended citation

Gütschow, J.; Busch, D.; Pflüger, M. (2024): The PRIMAP-hist national historical emissions time series v2.6.1 (1750-2023). zenodo. doi:10.5281/zenodo.15016289.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Content

• Use of the dataset and full description
• Abstract
• Support
• Sources
• Files included in the dataset
• Notes
• Data format description (columns)
• References
• Changelog

Abstract

The PRIMAP-hist dataset combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas, covering the years 1750 to 2023, and almost all UNFCCC (United Nations Framework Convention on Climate Change) member states as well as most non-UNFCCC territories. The data resolves the main IPCC (Intergovernmental Panel on Climate Change) 2006 categories. For CO2, CH4, and N2O subsector data for Energy, Industrial Processes and Product Use (IPPU), and Agriculture are available. The "country reported data priority" (CR) scenario of the PRIMAP-hist datset prioritizes data that individual countries report to the UNFCCC.

For developed countries, AnnexI in terms of the UNFCCC, this is the data submitted anually in the "National Inventory Submissions". Until 2023 data was submitted in the "Common Reporting Format" (CRF). Since 2024 the new "Common Reporting Tables" (CRT) are used. For developing countries, non-AnnexI in terms of the UNFCCC, we use the "Biannial Transparency Reports" (BTR) which mostly come with data also using the "Common Reporting Tables". We also use older data available through the UNFCCC DI portal (di.unfccc.int) and additional country submissions from "Biannial Update Reports" (BUR), "National Communications" (NC), and "National Inventory Reports" (NIR) read from pdf and where available xls(x) or csv files. For a list of these submissions please see below. For South Korea the 2023 official GHG inventory has not yet been submitted to the UNFCCC but is included in PRIMAP-hist. PRIMAP-hist also includes official data for Taiwan which is not recognized as a party to the UNFCCC. We have mostly replaced the official data that has not been submitted to the UNFCCC used in v2.6 as countries have now submitted their data in CRT format, but had to make some exceptions as the CRT data was not usable for all countries.

Gaps in the country reported data are filled using third party data such as CDIAC, EI (fossil CO2), Andrew cement emissions data (cement), FAOSTAT (agriculture), and EDGAR 2024 (all sectors for CO2, CH4, N2O, HFCs, PFCs, SF6, NF3, except energy CO2). Lower priority data are harmonized to higher priority data in the gap-filling process.

For the third party priority time series gaps in the third party data are filled from country reported data sources.

Data for earlier years which are not available in the above mentioned sources are sourced from EDGAR-HYDE, CEDS, and RCP (N2O only) historical emissions.

The v2.4 release of PRIMAP-hist reduced the time-lag from 2 to 1 years for the October release. Thus the present version 2.6.1 includes data for 2023. For energy CO2 growth rates from the EI Statistical Review of World Energy are used to extend the country reported (CR) or CDIAC (TP) data to 2023. For CO2 from cement production Andrew cement data are used. For other gases and sectors we use EDGAR 2024 data. In a few cases we have to rely on numerical methods to estimate emissions for 2023.

Version 2.6.1 of the PRIMAP-hist dataset does not include emissions from Land Use, Land-Use Change, and Forestry (LULUCF) in the main file. LULUCF data are included in the file with increased number of significant digits and have to be used with care as they are constructed from different sources using different methodologies and are not harmonized.

The PRIMAP-hist v2.6.1 dataset is an updated version of

Gütschow, J.; Pflüger, M.; Busch, D. (2024): The PRIMAP-hist national historical emissions time series v2.6 (1750-2023). zenodo. doi:10.5281/zenodo.13752654.

The Changelog indicates the most important changes. You can also check the issue tracker on github.com/JGuetschow/PRIMAP-hist for additional information on issues found after the release of the dataset. Detailed per country information is available from the detailed changelog which is available on the primap.org website and on zenodo.

Use of the dataset and full description

Before using the dataset, please read this document and the article describing the methodology, especially the section on uncertainties and the section on limitations of the method and use of the dataset.

Gütschow, J.; Jeffery, L.; Gieseke, R.; Gebel, R.; Stevens, D.; Krapp, M.; Rocha, M. (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, doi:10.5194/essd-8-571-2016

Please notify us (johannes.guetschow@climate-resource.com) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.

When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the PRIMAP-hist dataset. See the full citations in the References section further below.

Since version 2.3 we use the data formats developed for the PRIMAP2 climate policy analysis suite: PRIMAP2 on GitHub. The data are published both in the interchange format which consists of a csv file with the data and a yaml file with additional metadata and the native NetCDF based format. For a detailed description of the data format we refer to the PRIMAP2 documentation.

We have also included files with more than three significant digits. These files are mainly aimed at people doing policy analysis using the country reported data scenario (HISTCR). Using the high precision data they can avoid questions on discrepancies with the reported data. The uncertainties of emissions data do not justify the additional significant digits and they might give a false sense of accuracy, so please use this version of the dataset with extra care.

Support

If you encounter possible errors or other things that should be noted, please check our issue tracker at github.com/JGuetschow/PRIMAP-hist and report your findings there. Please use the tag "v2.6.1" in any issue you create regarding this dataset.

If you need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@climate-resource.com.

Climate Resource makes this data available CC BY 4.0 licence. Free support is limited to simple questions and non-commercial users. We also provide additional data, and data support services to clients wanting more frequent updates, additional metadata or to integrate these datasets into their workflows. Get in touch at contact@climate-resource.com if you are interested.

Sources

• Global CO2 emissions from cement production v250226 data, paper: Andrew
  (2025), Andrew (2019)
• EI Statistical Review of World Energy website: Energy Institute (2024)
• CDIAC data: Hefner and Marland (2023), data: Hefner (2024), paper: Gilfillan and Marland (2021)
• CEDS: data: Hoesly et al. (2020), paper: Hoesly et al. (2018)
• EDGAR 2024: data/website: European Commission, European Commision, JRC (2024), report: European Commission. Joint Research Centre & IEA. (2024)
• EDGAR-HYDE 1.4 data: Van Aardenne et al. (2001), Olivier and Berdowski (2001)
• FAOSTAT database data: Food and Agriculture Organization of the United Nations (2024)
• RCP historical data data, paper: Meinshausen et al. (2011)
• UNFCCC National Communications and National Inventory Reports for developing countries available from the UNFCCC DI portal <a

The Land use of Australia 2010–11 to 2020–21 data package consists of seamless continental rasters of land use at the national scale which provides the spatial representation of how Australia’s land resources are used. Data is for 2010–11, 2015–16 and 2020-21, and the associated changes between the years. Land use is specified according to the Australian Land Use and Management (ALUM) Classification version 8. The Land use of Australia 2010–11 to 2020–21 data package is a product of the Australian Collaborative Land Use and Management Program. Citation: ABARES 2024, Land use of Australia 2010–11 to 2020–21, Australian Bureau of Agricultural and Resource Economics and Sciences, Canberra, November, CC BY 4.0. DOI: 10.25814/w175-xh85

The first comprehensive mangrove cover dataset from 1990 to 2020, at five-year intervals, across all five mangrove areas in Pakistan, i.e. Indus Delta, Sandspit, Sonmiani, Kalmat Khor, and Jiwani. Using the Google Earth Engine (GEE) geospatial cloud computing platform, Random Forest (RF) classifier was applied on Landsat 30 m spatial resolution satellite images to classify three major land cover classes: ‘mangrove’, ‘water’ and ‘other’. High temporal and spectral resolutions of Landsat images, with a low saturation level of spectral bands with the integration of indices, are the main factors that ensured >90% overall accuracy of land cover maps. Overall, the findings of this paper revealed that, at the national scale, an estimated 477.22 sq. km was covered with mangrove in 1990, which increased to 1463.59 sq. km in 2020, a 3.74% annual rate of change. Mangrove fragmentation mapping results have also showed enhancement in mangrove tree canopy density.

The mangrove cover dataset of Pakistan on 5-year interval (1990-2020) at 30m spatial resolution data is available here:

Citation:

Use of these data requires citation of this dataset:

Gilani, Hammad, Naz, Hafiza Iqra, Arshad, Masood, Nazim, Kanwal, Akram, Usman, Abrar, Aneeqa, & Asif, Muhammad. (2024). Mangrove cover dataset of Pakistan on 5-year interval (1990-2020) at 30m spatial resolution (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10732690

Original research article:

Gilani, H., Naz, H.I., Arshad, M., Nazim, K., Akram, U., Abrar, A., & Asif, M. (2021). Evaluating mangrove conservation and sustainability through
spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan. Estuarine, Coastal and Shelf Science, 249: 107128. doi.org/10.1016/j.ecss.2020.107128

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

Southeast Blueprint 2024 extent
2021 National Land Cover Database (NLCD)
U.S. Census Bureau 2018 state boundaries (500k version): download the data
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]
Southeast Blueprint 2024 grasslands and savannas indicator

Mapping Steps

Reclassify the NLCD 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.
Convert 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 plantation in other subregions like the Coastal Plain.
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 to 3 (natural). NLCD often misclassifies natural grasslands and savannas as hay/pasture, which would otherwise lower their landscape condition score. 
From the Census state boundary file, export the SECAS states and dissolve into a single layer. This is used to identify the inland continental part of the Blueprint.
Convert that dissolved layer into a raster and use it to remove marine areas and areas outside the continental Southeast geography from the reclassified landcover layer.
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 2024. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 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. 

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].