Lakes & Reservoirs (Southeast Blueprint Indicator)

Reason for Selection Freshwater lake biomes, whether naturally occurring or created by artificial structures like dams, are prominent ecosystems on the landscape. They provide important fish habitat as well as drinking water for people (Keith et al. 2022, USGS 2025). The natural landcover surrounding lakes and reservoirs is strongly linked to good drinking water and safe places to recreate (Caldwell et al. 2014). There are many ways to assess the water quality of lakes (e.g., Secchi methods, nutrients loads, etc.); however, those data are not often spatially consistent or available for the full extent of the Southeast Conservation Blueprint. Land use practices upstream of lakes and reservoirs are correlated with (because they strongly influence) the nutrient, sediment, and contaminant loads entering a lake, and land use data are available at a broad scale. This indicator promotes consistency with the Midwest Glacial Lakes Fish Habitat Partnership’s watershed disturbance thresholds (Midwest Glacial Lakes Partnership 2019), and with the Midwest Landscape Initiative’s Midwest Conservation Blueprint. It also fills a key gap in the Southeast Blueprint by comparing the overall condition of different lakes and reservoirs. Prior to version 2025, the Blueprint did not attempt to prioritize across lakes and reservoirs and excluded them from the analysis due to a lack of indicator data. Input Data * 2023 [LANDFIRE Existing Vegetation Type] (EVT) CONUS; [access the data] * [2021 National Land Cover Database] (NLCD): Land cover * [LAGOS-US LOCUS v1.0] ([download geopackage]) * [Global-scale mining polygons (Version 2)]; [download the geopackage]; [see the layer in an online viewer]; [read a journal article] about the data development * [Southeast Blueprint 2025 extent] Mapping Steps * Reclassify the 2021 NLCD to begin separating the disturbed and developed classes from the natural classes. This will be further refined with additional datasets below. Assign the following disturbed classes a value of 0: Developed Open Space, Developed Low Intensity, Developed Medium Intensity, Developed High Intensity, Pasture/Hay, Cultivated Crops. All other classes are considered natural; assign them a value of 1. Also convert all NLCD pixels with a value of 0 to NoData because they are outside the continental United States geography. * Use a conditional statement to extract silviculture (i.e. intensive forest management for timber production) classes from the 2023 LANDFIRE EVT raster. Assign the following two silviculture classes a value of 0 (disturbed): “Northeastern North American Temperate Forest Plantation class” (9312) and “Southeastern North American Temperate Forest Plantation” (9322). Assign all other classes a value of 1. * Using the ArcPy Spatial Analyst Times function, combine the two rasters created above. In the resulting raster, silviculture areas from LANDFIRE receive a value of 0 (disturbed). Otherwise, a pixel retains the disturbed (0) or natural (1) values determined by the NLCD landcover. * Select U.S. areas from the global mining boundaries dataset, then add and calculate a field that will be used to convert this polygon dataset to a raster. Convert to a raster, assigning mine footprints a value of 0. Reclassify the raster output to change the NoData values to 1 so that mines receive a value of 0 and all other areas receive a value of 1. * Using the ArcPy Spatial Analyst Times function, combine the mining raster created above with the silviculture/landcover disturbance dataset to add mines to the disturbed class. * Use a conditional statement to extract quarries, strip mines, and gravel pit classes from the 2023 LANDFIRE EVT raster. This creates a raster where the “Quarries-Strip Mines-Gravel Pits-Well and Wind Pads” have a value of 0 and everything else is 1. 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). * There are groups of location errors in this quarry LANDFIRE class in specific parts of South Alabama and West Mississippi. To remove these location errors, assign a value of 1 (natural) to quarry pixels classified in NLCD as evergreen, mixed, or deciduous forest (41, 42, 43). Essentially, use NLCD to override the LANDFIRE classification in places where NLCD considers a quarry pixel to actually be forested. Otherwise, retain the disturbed value from the LANDFIRE quarry class. * Using the ArcPy Spatial Analyst Times function, combine the corrected “Quarries-Strip Mines-Gravel Pits-Well and Wind Pads” raster created above with the silviculture/landcover/mining disturbance dataset to add quarries, strip mines, gravel wells, and wind pads to the disturbed class. * Using the lakes layer from the LAGOS dataset, remove lakes and reservoirs from the natural/disturbed landcover raster created above. Especially for lakes or reservoirs with very small lakesheds, we don’t want the open water of a lake or reservoir itself (which would otherwise be considered natural) to influence the percent disturbance calculation. Convert the LAGOS lakes layer from polygon to raster and assign all lakes a value of 1. Reclassify the output to convert NoData areas to 0. * Use a conditional statement to assign a value of 9 in the combined natural/disturbed raster to any pixel identified as a lake or reservoir in the LAGOS dataset. Otherwise, retain the disturbed (0) or natural (1) value assigned above. This distinguishes lakes and reservoirs (9) from the areas outside the contiguous United States (NoData). Export this as a .tif for further analysis in QGIS. * In QGIS, use the fix geometries tool to repair the geometry of the “ws” layer from the LAGOS download (gis_locus_v1.0.gpkg). The ws layer contains the lakeshed polygons. * In QGIS, use the Zonal Histogram tool to calculate the area of disturbance (0) and natural (1) in each lakeshed. * Convert the zonal histogram output geopackage layer to a geodatabase layer, to allow it to work better in ArcGIS. * Use the CalculateField command to create a field to hold the percent disturbance value for each lakeshed. Calculate the percent of each lakeshed that is disturbed by dividing the disturbed area of each lakeshed by the total area of each lakeshed and multiplying by 100. Note: this causes a divide by zero error in lakesheds contained entirely in Canada, which are not covered by the CONUS landcover used to create the disturbance layer. Remove those lakesheds from the analysis and rerun the calculation. * Add a new field and populate it with the legend values for this indicator. This classification method for lake water quality is based on the Midwest Glacial Lakes Partnership thresholds for watershed landcover composition, as seen in their [Conservation Planner] online tool. Lakesheds with a percent disturbance >60% receive a value of 1, those with a percent disturbance >25% and ≤60% receive a value of 2, and those with a percent disturbance ≤25% receive a value of 3. * Convert the LAGOS geopackage lake layer to a geodatabase. * Using the Join Field command, join to the geodatabase lakes layer to the table containing the percent disturbance for the lakeshed. * Convert the lakes layer to a raster using the PolygonToRaster command, retaining the field holding the numeric values for this indicator. * Reclassify the above raster to change NoData values to 0. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools. * Convert to NoData all areas not covered by the NLCD because their disturbed condition or natural condition was not assessed. * Apply the legend values as seen below. * 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: 3 = Lake or reservoir with low disturbance (0-25%) in upstream watershed 2 = Lake or reservoir with medium disturbance (>25-60%) in upstream watershed 1 = Lake or reservoir with high disturbance (>60%) in upstream watershed 0 = Not identified as a lake or reservoir Things to Keep in Mind * We investigated a number of other datasets that provide similar information, but did not use them in this version of the indicator: * The approach developed by Meyer et al. (2024) categorizes lakes into eutrophic, mixotrophic, and dystrophic by matching up Landsat reflectance with limnological observations. It then applies those results to an entire lake using the HydroLAKES dataset. However, HydroLAKES excludes lakes less than 10 hectares in size, and many lakes in North Florida are absent in this dataset. The Meyer et al. (2024) approach also relies on point-in-time observations of surface reflectance, which for some lakes can vary enormously by season, environmental conditions, and year. We determined upstream watershed condition to provide a more stable, robust assessment of the likelihood of nutrient and sediment loading. * The SPARROW watershed model does not cover the entire Southeast. Known Issues * This indicator underprioritizes the nearshore areas of lakes and reservoirs (i.e. the littoral zone), which have unique importance for wildlife and plants. We attempted to do a shoreline analysis to highlight these areas but ran out of time due to complexities in the process. We will revisit this in future Blueprint updates to improve this indicator. * Some lakesheds along the U.S. border with Canada and Mexico extend beyond the boundaries of NLCD (i.e., outside the continental United States). Because we do not have landcover information for those portions of the lakeshed, the percent disturbance calculation used in this indicator is only based on the portion of the lakeshed covered by the NLCD. This might cause some lakeshed disturbance scores to be lower or higher than conditions on the ground depending on the condition of those excluded areas. 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 Caldwell, Peter; Muldoon, Corinne; Ford-Miniat, Chelcy; Cohen, Erika; Krieger, Suzanne; Sun, Ge; McNulty, Steven; Bolstad, Paul V. 2014. Quantifying the role of National Forest System lands in providing surface drinking water supply for the Southern United States. Gen. Tech. Rep. SRS-197. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station. 135 p. [[https://research.fs.usda.gov/treesearch/47706]]. Keith, D. A., J. R. Ferrer-Paris, E. Nicholson, M. Bishop, B. A, Polidoro, E. Ramirez-Llodra, M. G. Tozer, J. L. Nel, R. Mac Nally, E. J. Gregr, K. E. Watermeyer, F. Essl, D. Faber-Langendoen, J. Franklin, C. E. R. Lehmann, A. Etter, D. J. Roux, J. S. Stark, J. A. Rowland, N. A. Brummitt, U. C. Fernandez-Arcaya, I. M. Suthers, S. K. Wiser, I. Donohue, L. J. Jackson, R. T. Pennington, N. Pettorelli, A. Andrade, A. Lindgaard, T. Tahvanainen, A. Terauds, M. A. Chadwick, N. J. Murray, J. Moat, P. Pliscoff, I. Zager, and R. T. Kingsford (2022) A function-based typology for Earth’s ecosystems Nature 610, 513–518. DOI:10.1038/s41586-022-05318-4. [[https://www.nature.com/articles/s41586-022-05318-4]]. LANDFIRE, Earth Resources Observation and Science Center (EROS), U.S. Geological Survey. Published December 18, 2024. Existing Vegetation Type (EVT) CONUS. LANDFIRE 2023 Update, LF 2023, raster digital data. Sioux Falls, SD. [[https://www.landfire.gov]]. Meyer, M. F., Topp, S. N., King, T. V., Ladwig, R., Pilla, R. M., Dugan, H. A., Eggleston, J. R., Hampton, S. E., Leech, D. M., Oleksy, I. A., Ross, J. C., Ross, M. R. V., Woolway, R. I., Yang, X., Brousil, M. R., Fickas, K. C., Padowski, J. C., Pollard, A. I., Ren, J., & Zwart, J. A. (2024). National-scale remotely sensed lake trophic state from 1984 through 2020. Scientific data, 11(1), 77. [[https://doi.org/10.1038/s41597-024-02921-0]]. Midwest Glacial Lakes Partnership. 2019. Midwest Glacial Lakes Partnership Conservation Planner. User Guide. Accessed May 20, 2025. [[https://midwestglaciallakes.org/wp-content/uploads/2019/05/MGLP-Conservation-Planner-User-Guide-5_7_191.pdf]]. Smith, N.J., K.E. Webster, L.K. Rodriguez, K.S. Cheruvelil, and P.A. Soranno. 2021. LAGOS-US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S. ver 1. Environmental Data Initiative. Accessed December 13, 2021. [[https://doi.org/10.6073/pasta/e5c2fb8d77467d3f03de4667ac2173ca]]. U.S. Geological Survey (USGS). 2025. Water Science School: Lakes and Reservoirs. Accessed 2025-05-08. [[https://www.usgs.gov/special-topics/water-science-school/science/lakes-and-reservoirs]].