Land Cover Austin, TX This Enviroatlas Map Shows Land Cover for the Austin, TX Area at High Spatial Resolution (1-Meter Pixels)

• Austin, TX • New Haven, CT • Baltimore, MD • New York, NY • Birmingham, AL • Paterson, NJ • Brownsville, TX • Philadelphia, PA • Chicago, IL • Phoenix, AZ • Cleveland, OH • Pittsburgh, PA • Des Moines, IA • Portland, ME • Durham, NC • Portland, OR • Fresno, CA • Salt Lake City, UT • Green Bay, WI • Sonoma County, CA • Los Angeles, CA • St Louis, MO • Memphis, TN • Tampa, FL • Milwaukee, WI • Virginia Beach, VA • Minneapolis, MN • Washington, DC • New Bedford, MA • Woodbine, IA Meter Scale Urban Land Cover Austin, TX This EnviroAtlas map shows land cover for the Austin, TX area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, and Agriculture.1

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource Figure 2: MULC for Austin, managers, public health officials, citizens, teachers, and TX. students. Potential applications of these data include the How can I use this information? assessment of wildlife corridors and riparian buffers, This data layer can be used alone or combined visually and stormwater management, urban heat island mitigation, access analytically with other spatial data layers. MULC and its to recreation and green space, urban forestry, and urban derivatives support numerous lines of investigation into landscape ecology. human and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat 6% island and stormwater runoff effects? Do urban streams have 3% Water healthy vegetated buffers? 17% Impervious More than 85 EnviroAtlas data layers incorporate MULC in 5% 32% Soil/Barren their computation, including: Tree/Forest • Total carbon stored in above ground tree biomass (metric tons) 37% Grass/Herbaceous • 3 Reduction in annual stormwater runoff (m /yr) Agriculture • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge Figure 1: Areal percentage of MULC classes for Austin, TX • Reduction in median load of nitrites and nitrates, soluble phosphorous, and total suspended solids (kg/yr) from filtration by trees

CONTINUED ON BACK 1 How were the data for this map created? Manual editing was used to reduce confusion between The MULC data for Austin, Texas were generated from classes, as needed. Data were organized and manipulated in a digital image processing and supervised classification of GIS. A description of the classification techniques and aerial photography, LiDAR data, and relevant ancillary workflow is given in the metadata. datasets. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural What are the limitations of these data? Imagery Program (NAIP)3 and includes four spectral bands A land cover map is a model of the landscape, and by (blue, green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in May 2010 and LiDAR from August these maps are the best estimation of the truth based on the 2007. best available data. A formal accuracy assessment of 655 photo-interpreted reference points yielded an overall User’s A supervised classification software was used to identify five Accuracy of about 87 percent for the Austin MULC. Full common land cover classes: Impervious Surface, Soil/Barren, accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, and Water. Ancillary data information for the source data sets can be found on their were used to map Agriculture. respective web sites. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Austin MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Austin, TX area was developed by Charles Rudder, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Austin, TX MULC with 40% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover Baltimore, MD This EnviroAtlas map shows land cover for the Baltimore, MD area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Tree, Grass and Herbaceous, Agriculture, Woody Wetland and Emergent Wetland.1

41% 0% Agriculure • Total carbon stored in above ground tree biomass (metric tons) Woody Wetland • Reduction in annual stormwater runoff (m3/yr) Emergent Wetland • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) Figure 1: Areal percentage of MULC classes for Baltimore, MD. • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, and total suspended solids (kg/yr) from filtration by trees

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between The MULC data for Baltimore, MD were developed by the classes, as needed. Data were organized and manipulated in a Chesapeake Conservancy and converted into EnviroAtlas GIS. A description of the classification techniques and MULC by the EPA.3 The data were generated from digital workflow is given in the metadata. image processing and supervised feature extraction of aerial photography, LiDAR data, and relevant ancillary datasets. The What are the limitations of these data? aerial photography is from the United States Department of A land cover map is a model of the landscape, and by Agriculture (USDA) National Agricultural Imagery Program definition an imperfect representation of reality. However, (NAIP)4 and includes four spectral bands (blue, green, red, and these maps are the best estimation of the truth based on the near infrared). This MULC used NAIP imagery collected in best available data. A formal accuracy assessment of 687 2013 and LiDAR mostly from 2011 and 2015. photo-interpreted reference points yielded an overall User’s Accuracy of about 90 percent for the Baltimore MULC. Full A rule-based feature extraction software was used to identify accuracy results are reported in the metadata. Accuracy five common land cover classes: Impervious information for the source data sets can be found on their Surface, Soil/Barren, Grass/Herbaceous, Tree, and Water. respective web sites. Most errors are not evident when Ancillary data were primarily used to map Wetlands (Woody viewing at map scales smaller than approximately 1:8000. and Emergent)5 and Agriculture. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Baltimore MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Baltimore, MD area were created by the Chesapeake Conservancy, Gwen Oster, EPA ORAU Research Participant, and Drew Pilant, EPA.

Figure 3: Baltimore MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. Chesapeake Conservancy, 2018. Land Cover Data Project. Accessed March 2018. 4. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 5. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover Birmingham, AL This EnviroAtlas map shows land cover for the Birmingham, AL area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Woody Wetland and Emergent Wetland.1

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between The MULC data for Birmingham, AL were generated from classes, as needed. Data were organized and manipulated in a digital image processing and supervised classification of GIS. A description of the classification techniques and aerial photography, LiDAR data, and relevant ancillary workflow is given in the metadata. datasets. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural What are the limitations of these data? Imagery Program (NAIP)3 and includes four spectral bands A land cover map is a model of the landscape, and by (blue, green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in summer 2011 and LiDAR collected in these maps are the best estimation of the truth based on the 2010, 2011, and 2013. best available data. A formal accuracy assessment of 595 photo-interpreted reference points yielded an overall User’s A supervised classification software was used to identify five Accuracy of about 83 percent for the Birmingham MULC. common land cover classes: Impervious Surface, Soil/Barren, Full accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, and Water. Ancillary data information for the source data sets can be found on their were primarily used to map Wetlands (Woody and respective web sites. Most errors are not evident when Emergent).4 viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Birmingham, AL MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Birmingham, AL area was developed by Keith Endres, EPA, Daniel Rosenbaum, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Birmingham MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover Brownsville, Texas This EnviroAtlas map shows land cover for the Brownsville, TX area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Shrub, Agriculture, Orchard, Woody Wetland and Emergent Wetland.1

Why is high resolution land cover important? Land cover data present a “birds-eye” view that can help identify important features, patterns and relationships in the landscape. The National Land Cover Dataset (NLCD)2 provides land cover for the entire contiguous U.S. at 30- meter pixel resolution. However, for some analyses, the density and heterogeneity of an urban landscape requires higher resolution land cover data. Meter Scale Urban Land Cover (MULC) data were developed to fulfill that need. In comparison, there are 900 MULC pixels for every one NLCD pixel. Figure 2: MULC for Brownsville, TX. Anticipated users of MULC include city and regional How can I use this information? planners, water authorities, wildlife and natural resource This data layer can be used alone or combined visually and managers, public health officials, citizens, teachers, and analytically with other spatial data layers. MULC and its students. Potential applications of these data include the derivatives support numerous lines of investigation into assessment of wildlife corridors and riparian buffers, human and ecosystem health in the urban landscape, such as: stormwater management, urban heat island mitigation, access Where are the green and gray spaces? Which streets can to recreation and green space, urban forestry, and urban benefit from more trees? What areas are subject to urban heat landscape ecology. island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? Water 4% More than 85 EnviroAtlas data layers incorporate MULC in 0.4% 11% 1% Impervious their computation, including: Soil and Barren • Width of road and stream vegetated buffers (m) 17% 8% • Total carbon stored in above ground tree biomass Tree/ Forest (metric tons) Shrub • Reduction in annual stormwater runoff (m3/yr) 11% • Value of asthma exacerbation cases avoided due to Grass/ Herbaceous sulfur dioxide removed ($/yr) Agriculture • Estimated percent of tree cover within 26 m of a 11% busy road edge 33% Orchard 4% • Reduction in median load of nitrites and nitrates, Woody Wetland soluble phosphorous, and total suspended solids Emergent Wetland (kg/yr) from filtration by trees

Figure 1: Areal percentage of MULC classes for Brownsville, TX

CONTINUED ON BACK

1 How were the data for this map created? Manual editing was used to correct misclassifications as needed. Data were organized and manipulated in a GIS. A The MULC data for Brownsville, Texas were generated from description of the classification techniques and workflow is digital image processing and supervised classification of given in the metadata. aerial photography, LiDAR data, and relevant ancillary datasets. The aerial photography is from the United States What are the limitations of these data? Department of Agriculture (USDA) National Agricultural A land cover map is a model of the landscape, and by Imagery Program (NAIP)3 and includes four spectral bands definition an imperfect representation of reality. However, (blue, green, red, and near infrared). This MULC used NAIP these maps are the best estimation of the truth based on the imagery collected in 2014. LiDAR data were collected in best available data. A formal accuracy assessment of the 2006 and 2011 by the International Boundary and Water Brownsville MULC using 699 photo-interpreted reference Commission. points yielded an overall accuracy of 82 percent. Full accuracy A supervised classification was used to identify five common results are reported in the metadata. Accuracy information for land cover classes: Impervious Surface, Soil/Barren, the source data sets can be found on their respective web sites. Grass/Herbaceous, Tree/Forest, and Water. The Shrub class was differentiated from other vegetation classes using LiDAR How can I access these data? height data. Ancillary data were used to map four additional EnviroAtlas data can be accessed by: (1) viewing them in the classes: Wetlands (Woody and Emergent),4 Agriculture (row interactive map, (2) using the web services, or (3) crops), and Orchard. downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Brownsville MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Brownsville, Texas area were developed by Chelsea Fizer, EPA ORAU Research Participant, Daniel Rosenbaum, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Brownsville MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover Chicago, IL This EnviroAtlas map shows land cover for the Chicago, IL area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland, and Emergent Wetland.1

Anticipated users of MULC include city and regional Figure 2: MULC for Chicago, IL. planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and How can I use this information? students. Potential applications of these data include the This data layer can be used alone or combined visually and assessment of wildlife corridors and riparian buffers, analytically with other spatial data layers. MULC and its stormwater management, urban heat island mitigation, access derivatives support numerous lines of investigation into to recreation and green space, urban forestry, and urban human and ecosystem health in the urban landscape, such as: landscape ecology. Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat island and stormwater runoff effects? Do urban streams have 2% 2% healthy vegetated buffers? Water 4% More than 85 EnviroAtlas data layers incorporate MULC in Impervious their computation, including: 15% 1% Soil/Barren • Total carbon stored in above ground tree biomass 33% Tree/Forest (metric tons) • Reduction in annual stormwater runoff (m3/yr) 18% Grass/Herbaceous • Value of asthma exacerbation cases avoided due to Agriculture sulfur dioxide removed ($/yr) 26% Woody Wetland • Estimated percent of tree cover within 26 m of a busy Emergent Wetland road edge • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids Figure 1: Areal percentage of MULC classes for Chicago, IL. (kg/yr) from filtration by trees

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Chicago, IL were generated from digital description of the classification techniques and workflow is image processing and object-based image analysis (OBIA) of given in the metadata. aerial photography, LiDAR, and relevant ancillary datasets. The aerial photography is from the United States Department What are the limitations of these data? of Agriculture (USDA) National Agricultural Imagery A land cover map is a model of the landscape, and by Program (NAIP)3 and includes four spectral bands (blue, definition an imperfect representation of reality. However, green, red, and near infrared). This MULC used NAIP these maps are the best estimation of the truth based on the imagery collected in summer/fall 2010, 2012, and 2013 and best available data. A formal accuracy assessment of 600 LiDAR data from 2006, 2007, 2008, 2010, 2013 and 2014. photo-interpreted reference points yielded an overall User’s A rule-based feature extraction software was used to identify Accuracy of about 81 percent for the Chicago MULC. Full five common land cover classes: Impervious Surface, accuracy results are reported in the metadata. Accuracy Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. information for the source data sets can be found on their Ancillary data were used to map three additional classes: respective web sites and metadata. Most errors are not evident Wetlands (Woody and Emergent)4 and Agriculture. when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Austin MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments The majority of the EnviroAtlas MULC data for the Chicago, IL area were developed by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O’Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Daniel Rosenbaum, EPA Student Services Contractor, and Drew Figure 3: Chicago MULC with 50% transparency displayed on top of aerial Pilant, EPA added wetlands and agriculture classes and imagery. The fine spatial detail shows individual buildings, trees, and roads. assessed accuracy.

Selected Publications

1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover Cleveland, OH This EnviroAtlas map shows land cover for the Cleveland, Ohio area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Woody Wetland, and Emergent Wetland.1

Why is high resolution land cover important? Land cover data present a “birds-eye” view that can help identify important features, patterns, and relationships in the landscape. The National Land Cover Dataset (NLCD)2 provides land cover for the entire contiguous U.S. at 30-meter pixel resolution. However, for some analyses, the density and heterogeneity of an urban landscape requires higher resolution land cover data. Meter Scale Urban Land Cover (MULC) data were developed to fulfill that need. In comparison, there are 900 MULC pixels for every one NLCD pixel.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource Figure 2: MULC for Cleveland, OH. managers, public health officials, citizens, teachers, and students. Potential applications of these data include the How can I use this information? assessment of wildlife corridors and riparian buffers, This data layer can be used alone or combined visually and stormwater management, urban heat island mitigation, access analytically with other spatial data layers. MULC and its to recreation and green space, urban forestry, and urban derivatives support numerous lines of investigation into human landscape ecology. and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat island and 1.4% 0.4% stormwater runoff effects? Do urban streams have healthy vegetated buffers? 6.4% Water Impervious More than 85 EnviroAtlas data layers incorporate MULC in 30.1% 20.5% Soil/Barren their computation, including: Tree/Forest • Total carbon stored in above-ground tree biomass (metric tons) 1.5% Grass/Herbaceous • Reduction in annual stormwater runoff (m3/yr) 39.7% Woody Wetland • Value of asthma exacerbation cases avoided due to Emergent Wetland sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge Figure 1: Areal percentage of MULC classes for Cleveland, OH. • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids (kg/yr) from filtration by trees.

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Cleveland, OH were generated from description of the classification techniques and workflow is digital image processing and supervised classification of aerial given in the metadata. photography, LiDAR, and relevant ancillary datasets. The aerial photography is from the United States Department of What are the limitations of these data? Agriculture (USDA) National Agricultural Imagery Program A land cover map is a model of the landscape, and by definition (NAIP). It includes four spectral bands (blue, green, red, and an imperfect representation of reality. However, these maps are near infrared). This MULC used NAIP imagery collected in fall the best estimation of the truth based on the best available data. 2011 and 2013 and LiDAR data from 2006. A formal accuracy assessment of 600 photo-interpreted A pixel-based classification software was used to identify five reference points yielded an overall User’s Accuracy of about common land cover classes: Impervious Surface, Soil/Barren, 86 percent for the Cleveland MULC. Full accuracy results are Grass/Herbaceous, Tree/Forest, and Water. National Wetlands reported in the metadata. Accuracy information for the source Inventory (NWI) data were used to map Woody and Emergent data sets can be found on their respective web sites and Wetlands. metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Cleveland MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Cleveland, OH area was developed by Daniel Rosenbaum, EPA Student Services Contractor, Drew Pilant, EPA, and the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O’Neil-Dunne. The SAL portion of the land cover was created as part of the United States Forest Service Urban Tree Figure 3: Cleveland MULC with 50% transparency displayed on top of aerial Canopy (UTC) assessment program for Cuyahoga County. imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover Des Moines, IA This EnviroAtlas map shows land cover for the Des Moines, IA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland, and Emergent Wetland.1

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource Figure 2: MULC for Des Moines, IA. managers, public health officials, citizens, teachers, and students. Potential applications of these data include the How can I use this information? assessment of wildlife corridors and riparian buffers, This data layer can be used alone or combined visually and stormwater management, urban heat island mitigation, access analytically with other spatial data layers. MULC and its to recreation and green space, urban forestry, and urban derivatives support numerous lines of investigation into landscape ecology. human and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat <1% island and stormwater runoff effects? Do urban streams have 3% Water healthy vegetated buffers? Impervious 4% More than 85 EnviroAtlas data layers incorporate MULC in Soil/Barren their computation, including: 18% 1% 28% Tree/Forest • Total carbon stored in above ground tree biomass (metric tons) Grass/Herbaceous • Reduction in annual stormwater runoff (m3/yr) 17% Agriculture • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) 30% Woody Wetland • Estimated percent of tree cover within 26 m of a busy Emergent road edge Wetland • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids (kg/yr) from filtration by trees. Figure 1: Areal percentage of MULC classes for Des Moines, IA.

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes as needed. Data were organized and manipulated in a GIS. A The MULC data for Des Moines, IA were generated from description of the classification techniques and workflow is digital image processing and ISODATA classification of given in the metadata. aerial photography, LiDAR data, and relevant ancillary datasets. The aerial photography is from the United States What are the limitations of these data? Department of Agriculture (USDA) National Agricultural A land cover map is a model of the landscape, and by Imagery Program (NAIP)3 and includes four spectral bands definition an imperfect representation of reality. However, (blue, green, red, and near infrared). This MULC used NAIP these maps are the best estimation of the truth based on imagery collected in summer/fall 2007-2010 and LiDAR from the best available data. A formal accuracy assessment of 2009. 655 photo-interpreted reference points yielded an overall A classification software was used to identify five common User’s Accuracy of about 79 percent for the Des Moines land cover classes: Impervious Surface, Soil/Barren, MULC. Full accuracy results are reported in the Grass/Herbaceous, Tree/Forest, and Water. Ancillary data metadata. Accuracy information for the source data sets were primarily used to map three additional classes: Woody can be found on their respective web sites. Most errors Wetlands, Emergent Wetlands,4 and Agriculture. are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Des Moines MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Des Moines, IA area were made by modifying existing High Resolution Land Cover (HRLC) data originally created by the Iowa Department of Natural Resources (IDNR). MULC adaptations were made by Charles Rudder, EPA Student Services Contractor, and Drew Pilant, EPA. Figure 3: Des Moines MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover Durham, NC This EnviroAtlas map shows land cover for the Durham, NC area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to recreation and green space, urban forestry, and urban Figure 2: MULC for Durham, NC. landscape ecology. How can I use this information? This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 1% derivatives support numerous lines of investigation into Water human and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat 14% 17% 2% Impervious island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? Soil/Barren More than 85 EnviroAtlas data layers incorporate MULC in their computation, including: 66% Tree/Forest • Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) Grass/Herbaceous • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge Figure 1: Areal percentage of MULC classes for Durham, NC. • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr) How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Durham, NC were generated from digital description of the classification techniques and workflow is image processing and supervised classification of aerial given in the metadata. photography and relevant ancillary datasets. The aerial photography is from the United States Department of What are the limitations of these data? Agriculture (USDA) National Agricultural Imagery Program A land cover map is a model of the landscape, and by (NAIP)3 and includes four spectral bands (blue, green, red, and definition an imperfect representation of reality. However, near infrared). This MULC used NAIP imagery collected in these maps are the best estimation of the truth based on the July 2010. best available data. A formal accuracy assessment of 500 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 83 percent for the Durham MULC. Full Grass/Herbaceous, Tree/Forest, and Water. accuracy results are reported in the metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Durham MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Durham, NC area was developed by Jeremy Baynes and Matthew Dannenberg, EPA Figure 3: Durham, NC MULC with 50% transparency displayed on top Student Services Contractors, and Drew Pilant, EPA. of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

June 2018 Meter Scale Urban Land Cover Fresno, California This EnviroAtlas map shows land cover for the Fresno, CA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous, Agriculture, and Orchard.

Agriculture 8% 25% More than 85 EnviroAtlas data layers incorporate MULC in Orchard their computation, including: • Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) Figure 1: Areal percentage of MULC classes for Fresno, CA. • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? classes: Agriculture and Orchard. Manual editing was used to reduce confusion between classes, as needed. Data were The MULC data for Fresno, CA were generated from digital organized and manipulated in a GIS. A description of the image processing and supervised classification of aerial classification techniques and workflow is given in the photography, LiDAR data, and relevant ancillary datasets. metadata. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural Imagery What are the limitations of these data? Program (NAIP)3 and includes four spectral bands (blue, A land cover map is a model of the landscape, and by green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in summer 2010 and LiDAR collected in these maps are the best estimation of the truth based on the 2012. best available data. A formal accuracy assessment of 603 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 81 percent for the Fresno MULC. Full Grass/Herbaceous, Tree/Forest, and Water. Ancillary data accuracy results are reported in the metadata. Accuracy were primarily used to map two additional information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Fresno MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Fresno, CA area was developed by Jeremy Baynes, EPA Student Services Contractor and Drew Pilant, EPA.

Figure 3: Fresno, CA with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency June 2018 Meter Scale Urban Land Cover Green Bay, WI This EnviroAtlas map shows land cover for the Green Bay, WI area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody 1 Wetland and Emergent Wetland.

More than 85 EnviroAtlas data layers incorporate MULC in their computation, including:

• Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy Figure 1: Areal percentage of MULC classes for Green Bay, WI road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). 4 How were the data for this map created? classes: Wetlands (Woody and Emergent) and Agriculture. Manual editing was used to reduce confusion between classes, The MULC data for Green Bay, WI were generated from as needed. Data were organized and manipulated in a GIS. A digital image processing and supervised classification of description of the classification techniques and workflow is aerial photography, LiDAR data, and relevant ancillary given in the metadata. datasets. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural What are the limitations of these data? Imagery Program (NAIP)3 and includes four spectral bands A land cover map is a model of the landscape, and by (blue, green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in summer 2010 and LiDAR collected in these maps are the best estimation of the truth based on the fall 2010. best available data. A formal accuracy assessment of 614 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 90 percent for the Green Bay MULC. Full Grass/Herbaceous, Tree/Forest, and Water. Ancillary data accuracy results are reported in the metadata. Accuracy were primarily used to map three additional information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Green Bay MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Green Bay, WI area was developed by Ben Riegel, EPA Student Services Contractor and Drew Pilant, EPA.

Figure 3: Green Bay, WI with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency June 2018 Meter Scale Urban Land Cover LA County, CA This EnviroAtlas map shows land cover for the Los Angeles County, CA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Shrub, Grass and Herbaceous, Woody Wetland and Emergent Wetland.1

More than 85 EnviroAtlas data layers incorporate MULC in their computation, including:

How were the data for this map created? Supervised classification and manual editing were used to add The MULC data for Los Angeles County, CA were generated mapped features (e.g., coastal and solar farm infrastructure) from digital image processing and object-based image and to reduce confusion between classes, as needed. Data analysis (OBIA) of aerial photography, LiDAR data, and were organized and manipulated in a GIS. A description of relevant ancillary datasets. The aerial photography is from the the classification techniques and workflow is given in the United States Department of Agriculture (USDA) National metadata. Agricultural Imagery Program (NAIP)3 and includes four spectral bands (blue, green, red, and near infrared). This What are the limitations of these data? MULC used NAIP and LiDAR data collected in 2016 and A land cover map is a model of the landscape, and by ortho imagery collected in 2014. definition an imperfect representation of reality. However, these maps are the best estimation of the truth based on the A rule-based feature extraction software was used to identify best available data. A formal accuracy assessment of 927 six common land cover classes: Impervious Surface, photo-interpreted reference points yielded an overall fuzzy Soil/Barren, Grass/Herbaceous, Shrub, Tree/Forest, and User’s Accuracy of about 89 percent for the Los Angeles Water. Ancillary data were primarily used to map two County MULC. Full accuracy results are reported in the additional classes: Wetlands (Woody and Emergent)4. metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Los Angeles County MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Development Team for further inquiries. Acknowledgements Figure 3: Los Angeles County MULC with 50% transparency displayed The EnviroAtlas MULC data for the Los Angeles County area on top of 2016 aerial imagery. The fine spatial detail shows individual was developed by the University of Vermont Spatial Analysis buildings, trees, and roads. Laboratory (SAL) as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Megan Van Fossen and Drew Pilant, EPA, made land cover class corrections and additions and assessed accuracy. Selected Publications 1. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A Land Use and LC Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964, U. S. Dept. of Interior. 2. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354. 3. U.S. Department of Agriculture, Farm Service Agency, 2010. Aerial Photography Field Office: U.S. Department of Agriculture Web page. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. 4. U.S. Fish and Wildlife Service. National Wetlands Inventory digital data. Website: http://wetlands.fws.gov/. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services, 14, 45-55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency July 2019 Meter Scale Urban Land Cover Memphis, TN This EnviroAtlas map shows land cover for the Memphis, TN area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland, and Emergent Wetland.1

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Memphis, TN were generated from description of the classification techniques and workflow is digital image processing and supervised classification of given in the metadata. aerial photography, LiDAR data, and relevant ancillary datasets. The aerial photography is from the United States What are the limitations of these data? Department of Agriculture (USDA) National Agricultural A land cover map is a model of the landscape, and by Imagery Program (NAIP)3 and includes four spectral bands definition an imperfect representation of reality. However, (blue, green, red, and near infrared). This MULC used NAIP these maps are the best estimation of the truth based on the imagery collected in summer 2012 and 2013 and LiDAR from best available data. A formal accuracy assessment of 612 2009, 2010, 2011 and 2012. photo-interpreted reference points yielded an overall User’s A classification software was used to identify five common Accuracy of about 87 percent for the Memphis MULC. Full land cover classes: Impervious Surface, Soil/Barren, accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, and Water. Ancillary data information for the source data sets can be found on their were primarily used to map three additional classes: Woody respective web sites. Most errors are not evident when Wetlands, Emergent Wetlands,4 and Agriculture. viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Memphis MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Memphis, TN area was developed by Akhilesh Khopkar, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Memphis MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover Milwaukee, WI This EnviroAtlas map shows land cover for the Milwaukee, WI area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody 1 Wetland and Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to Figure 2: MULC for Milwaukee, WI. recreation and green space, urban forestry, and urban landscape ecology. How can I use this information? This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 3% Water derivatives support numerous lines of investigation into 6% 8% Impervious human and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can 9% Soil/Barren 19% benefit from more trees? What areas are subject to urban heat Tree/Forest island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? 1% Grass/Herbaceous 25% More than 85 EnviroAtlas data layers incorporate MULC in Agriculture 29% their computation, including: Woody Wetland • Total carbon stored in above ground tree biomass (mt) Emergent Wetland • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to Figure 1: Areal percentage of MULC classes for Milwaukee, WI. sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). 4 How were the data for this map created? classes: Wetlands (Woody and Emergent) and Agriculture. Manual editing was used to reduce confusion between classes, The MULC data for Milwaukee, WI were generated from as needed. Data were organized and manipulated in a GIS. A digital image processing and object-based image analysis description of the classification techniques and workflow is (OBIA) of aerial photography, LiDAR data, and relevant given in the metadata. ancillary datasets. The aerial photography is from the United States Department of Agriculture (USDA) National What are the limitations of these data? Agricultural Imagery Program (NAIP)3 and includes four A land cover map is a model of the landscape, and by spectral bands (blue, green, red, and near infrared). This definition an imperfect representation of reality. However, MULC used NAIP imagery collected in summer 2010 and these maps are the best estimation of the truth based on the LiDAR collected in summer 2010 and spring 2012. best available data. A formal accuracy assessment of 626 A rule-based feature extraction software was used to identify photo-interpreted reference points yielded an overall User’s five common land cover classes: Impervious Surface, Accuracy of about 76 percent for the Milwaukee MULC. Full Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. accuracy results are reported in the metadata. Accuracy Ancillary data were primarily used to map three additional information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Milwaukee, WI MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Milwaukee, WI area were developed by EPA and Oneida Total Integrated Enterprises (OTIE) and OTIE’s subcontractor, the University of Arkansas Center for Advanced Spatial Technologies (CAST). Charles Figure 3: Milwaukee, WI MULC with 50% transparency displayed on Rudder and Chelsea Fizer, EPA Student Services Contractors top of aerial imagery. The fine spatial detail shows individual buildings, and Drew Pilant, EPA created the EPA portion of the data. trees, and roads.

Selected Publications

1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency June 2018 Meter Scale Urban Land Cover Minneapolis, MN This EnviroAtlas map shows land cover for the Minneapolis, MN area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland and Emergent Wetland.1

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the assessment of wildlife corridors and riparian buffers, Figure 2: MULC for Minneapolis, MN. stormwater management, urban heat island mitigation, access to recreation and green space, urban forestry, and How can I use this information? urban landscape ecology. This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its derivatives support numerous lines of investigation into human and ecosystem health in the urban landscape, such as: 2% Where are the green and gray spaces? Which streets can Water 7% 7% benefit from more trees? What areas are subject to urban heat 8% Impervious island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? 24% Soil/Barren Tree/Forest More than 85 EnviroAtlas data layers incorporate MULC in Grass/Herbaceous their computation, including: 27% Agriculture • Total carbon stored in above ground tree biomass (metric tons) 2% Woody wetland 23% • Reduction in annual stormwater runoff (m3/yr) Emergent wetland • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy Figure 1: Areal percentage of MULC classes for Minneapolis, MN. road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids (kg/yr) from filtration by trees 1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes as needed. Data were organized and manipulated in a GIS. A The MULC data for Minneapolis, MN were generated from description of the classification techniques and workflow is digital image processing and object-based image analysis given in the metadata. (OBIA) of aerial photography, LiDAR data, and relevant ancillary datasets. The aerial photography is from the United What are the limitations of these data? States Department of Agriculture (USDA) National A land cover map is a model of the landscape, and by Agricultural Imagery Program (NAIP)3 and includes four definition an imperfect representation of reality. However, spectral bands (blue, green, red, and near infrared). This these maps are the best estimation of the truth based on the MULC used NAIP imagery collected in summer/fall 2010 and best available data. A formal accuracy assessment of 644 LiDAR from spring and fall 2011. photo-interpreted reference points yielded an overall User’s A rule-based feature extraction software was used to identify Accuracy of about 83 percent for the Minneapolis MULC. six common land cover classes: Impervious Surface, Full accuracy results are reported in the metadata. Accuracy Soil/Barren, Grass/Herbaceous, Tree/Forest, Shadow, and information for the source data sets can be found on their Water. Ancillary data were primarily used to dissolve shadow respective web sites. Most errors are not evident when into appropriate classes as well as map three additional viewing at map scales smaller than approximately 1:8000. classes: Wetlands (Woody and Emergent)4 and Agriculture. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Minneapolis MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Minneapolis, MN area was developed by Akhilesh Khopkar, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Minneapolis MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2018 Meter Scale Urban Land Cover New Bedford, MA This EnviroAtlas map shows land cover for the New Bedford, MA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Woody Wetland and 1 Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to Figure 2: MULC for New Bedford, MA. recreation and green space, urban forestry, and urban landscape ecology. How can I use this information? This data layer can be used alone or combined visually and 3% Water analytically with other spatial data layers. MULC and its derivatives support numerous lines of investigation into 12% Impervious human and ecosystem health in the urban landscape, such as: 23% Where are the green and gray spaces? Which streets can Soil/Barren benefit from more trees? What areas are subject to urban heat 14% island and stormwater runoff effects? Do urban streams have Tree/Forest 11% healthy vegetated buffers? Grass/Herbaceous More than 85 EnviroAtlas data layers incorporate MULC in 36% their computation, including: 1% Woody Wetland • Total carbon stored in above ground tree biomass (mt) Emergent Wetland • Reduction in annual stormwater runoff (m3/yr) Figure 1: Areal percentage of MULC classes for New Bedford, MA. • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). 4 How were the data for this map created? Emergent) . Manual editing was used to reduce confusion between classes, as needed. Data were organized and The MULC data for New Bedford, MA were generated from manipulated in a GIS. A description of the classification digital image processing and supervised classification of aerial photography and relevant ancillary datasets. The aerial techniques and workflow is given in the metadata. photography is from the United States Department of What are the limitations of these data? Agriculture (USDA) National Agricultural Imagery Program A land cover map is a model of the landscape, and by (NAIP)3 and includes four spectral bands (blue, green, red, and definition an imperfect representation of reality. However, near infrared). This MULC used NAIP imagery collected in these maps are the best estimation of the truth based on the summer 2010. best available data. A formal accuracy assessment of 545 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 92 percent for the New Bedford MULC. Grass/Herbaceous, Tree/Forest, and Water. Ancillary data Full accuracy results are reported in the metadata. Accuracy were primarily used to map Wetlands (Woody and information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the New Bedford, MA MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the New Bedford area was developed by Jeremy Baynes, EPA Student Services Contractor and Drew Pilant, EPA.

Figure 3: New Bedford MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency July 2018 Meter Scale Urban Land Cover New Haven, CT This EnviroAtlas map shows land cover for the New Haven, CT area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Woody Wetland and Emergent Wetland.1

Why is high resolution land cover important? Land cover data present a “birds-eye” view that can help identify important features, patterns and relationships in the landscape. The National Land Cover Dataset (NLCD)2 provides land cover for the entire contiguous U.S. at 30- meter pixel resolution. However, for some analyses, the density and heterogeneity of an urban landscape requires higher resolution land cover data. Meter Scale Urban Land Cover (MULC) data were developed to fulfill that need. In comparison, there are 900 MULC pixels for every one NLCD pixel. Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource Figure 2: MULC for New Haven, CT. managers, public health officials, citizens, teachers, and How can I use this information? students. Potential applications of these data include the This data layer can be used alone or combined visually and assessment of wildlife corridors and riparian buffers, analytically with other spatial data layers. MULC and its stormwater management, urban heat island mitigation, access derivatives support numerous lines of investigation into to recreation and green space, urban forestry, and urban human and ecosystem health in the urban landscape, such as: landscape ecology. Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat 2% island and stormwater runoff effects? Do urban streams have Water healthy vegetated buffers? 5% 13% Impervious 14% More than 85 EnviroAtlas data layers incorporate MULC in 11% Soil/Barren their computation, including: 2% Tree/Forest • Total carbon stored in above ground tree biomass Grass/Herbaceous (metric tons) • Reduction in annual stormwater runoff (m3/yr) 53% Woody Wetland • Value of asthma exacerbation cases avoided due to Emergent Wetland sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge Figure 1: Areal percentage of MULC classes for New Haven, CT • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids (kg/yr) from filtration by trees

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between The MULC data for New Haven, CT were generated from classes, as needed. Data were organized and manipulated in a digital image processing and supervised classification of GIS. A description of the classification techniques and aerial photography, LiDAR, and relevant ancillary datasets. workflow is given in the metadata. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural Imagery What are the limitations of these data? Program (NAIP)3 and includes four spectral bands (blue, A land cover map is a model of the landscape, and by green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in summer/fall 2014 and LiDAR collected these maps are the best estimation of the truth based on the in winter 2006 and 2010/2011. best available data. A formal accuracy assessment of 600 photo-interpreted reference points yielded an overall User’s A pixel-based classification software was used to identify five Accuracy of about 89 percent for the New Haven MULC. Full common land cover classes: Impervious Surface, Soil/Barren, accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, and Water. Ancillary data information for the source data sets can be found on their were primarily used to map two additional classes: Woody respective web sites. Most errors are not evident when Wetlands and Emergent Wetlands.4 viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the New Haven MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the New Haven, CT area was developed by Chelsea Fizer, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: New Haven MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover New York City, NY This EnviroAtlas map shows land cover for the New York City, NY area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous.1

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the Figure 2: MULC for New York, NY. assessment of wildlife corridors and riparian buffers, How can I use this information? stormwater management, urban heat island mitigation, access This data layer can be used alone or combined visually and to recreation and green space, urban forestry, and urban analytically with other spatial data layers. MULC and its landscape ecology. derivatives support numerous lines of investigation into human and ecosystem health in the urban landscape, such as: Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat Water island and stormwater runoff effects? Do urban streams have 14% healthy vegetated buffers? 25% Impervious More than 85 EnviroAtlas data layers incorporate MULC in 16% Soil/Barren their computation, including: • Total carbon stored in above ground tree biomass Tree/Forest (metric tons) 44% 1% • Reduction in annual stormwater runoff (m3/yr) Grass/Herbaceou • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy Figure 4: Areal percentage of MULC classes for New York, NY. road edge • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids (kg/yr) from filtration by trees

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between The MULC data for New York City, NY were generated from classes, as needed. Data were organized and manipulated in a digital image processing and object-based image analysis GIS. A description of the classification techniques and (OBIA) of aerial photography, LiDAR data, and relevant workflow is given in the metadata. ancillary datasets. The aerial photography is from the United States Department of Agriculture (USDA) National What are the limitations of these data? Agricultural Imagery Program (NAIP)3 and includes four A land cover map is a model of the landscape, and by spectral bands (blue, green, red, and near infrared). This definition an imperfect representation of reality. However, MULC used NAIP imagery collected in summer 2011 and these maps are the best estimation of the truth based on the LiDAR collected in 2010. best available data. A formal accuracy assessment of 655 photo-interpreted reference points yielded an overall User’s A rule-based feature extraction software was used to identify Accuracy of about 87 percent for the New York City MULC. five common land cover classes: Impervious Surface, Full accuracy results are reported in the metadata. Accuracy Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. information for the source data sets can be found on their respective web sites. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Austin MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for New York City, NY was developed by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O’Neil- Dunne and adapted by Charles Rudder, EPA Student Services Contractor, and Drew Pilant, EPA. Figure 3: New York City MULC with 50% transparency displayed on top of aerial imagery. The spatial detail shows individual buildings, trees, and roads.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover Paterson, New Jersey This EnviroAtlas map shows land cover for the Paterson, NJ area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous.

• Total carbon stored in above ground tree biomass (mt) 3 Figure 1: Areal percentage of MULC classes for Paterson, NJ. • Reduction in annual stormwater runoff (m /yr) • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Paterson, NJ were generated from digital description of the classification techniques and workflow is image processing and supervised classification of aerial given in the metadata. photography. The aerial photography is from the United States Department of Agriculture (USDA) National What are the limitations of these data? Agricultural Imagery Program (NAIP)3 and includes four A land cover map is a model of the landscape, and by spectral bands (blue, green, red, and near infrared). This definition an imperfect representation of reality. However, MULC used NAIP imagery collected in summer 2010. these maps are the best estimation of the truth based on the A supervised classification software was used to identify five best available data. A formal accuracy assessment of 564 common land cover classes: Impervious Surface, Soil/Barren, photo-interpreted reference points yielded an overall User’s Grass/Herbaceous, Tree/Forest, and Water. Accuracy of about 87 percent for the Paterson MULC. Full accuracy results are reported in the metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Paterson MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Paterson, NJ area was developed by Jeremy Baynes, EPA Student Services Figure 3: Paterson MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, Contractor and Drew Pilant, EPA. and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

July 2018 Meter Scale Urban Land Cover Philadelphia, PA This EnviroAtlas map shows land cover for the Philadephia, PA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Shrub, Grass and Herbaceous, Agriculture, Orchard, Woody Wetland and Emergent Wetland.1

Anticipated users of MULC include city and regional Figure 2: MULC for Philadelphia, PA planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and How can I use this information? students. Potential applications of these data include the This data layer can be used alone or combined visually and design of wildlife corridors and riparian buffers, stormwater analytically with other spatial data layers. MULC and its management, urban heat island mitigation, access to derivatives support numerous lines of investigation into recreation and green space, urban forestry, and urban human and ecosystem health in the urban landscape, such as: landscape ecology. Where are the green and gray spaces? Which streets can benefit from more trees? What areas are subject to urban heat 1.5% Water 5.3% 3.8% island and stormwater runoff effects? Do urban streams have Impervious 4.8% healthy vegetated buffers? Soil/Barren More than 85 EnviroAtlas data layers incorporate MULC in 20.2% Tree/Forest 0.1% their computation, including: Shrub 30.6% Grass/Herbaceous • Total carbon stored in above ground tree biomass (mt) Agriculture • Reduction in annual stormwater runoff (m3/yr) 33.1% Orchard • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) Woody Wetland • 0.5% Estimated percent of tree cover within 26 m of a busy Emergent Wetland road edge • Figure 1: Areal percentage of MULC classes for Philadelphia, PA. Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr).

How were the data for this map created? classes: Wetlands (Woody and Emergent)4, Agriculture, and The MULC data for Philadelphia, PA were generated from Orchards. Manual editing was used to reduce confusion digital image processing and object-based image analysis between classes, as needed. Data were organized and (OBIA) of aerial photography, LiDAR data, and relevant manipulated in a GIS. A description of the classification ancillary datasets. The aerial photography is from multiple techniques and workflow is given in the metadata. sources including the United States Department of Agriculture (USDA) National Agricultural Imagery Program What are the limitations of these data? (NAIP)3 and includes four spectral bands (blue, green, red, A land cover map is a model of the landscape, and by and near infrared). This MULC used NAIP imagery collected definition an imperfect representation of reality. However, in summer/fall 2013, additional orthoimagery from 2005- these maps are the best estimation of the truth based on the 2008 and 2012-2015, and 2006-2008 (PA), 2011-2015 (MD), best available data. A formal accuracy assessment of 851 2014 (DE), and 2015 (NJ) leaf-off LiDAR. photo-interpreted reference points yielded an overall User’s Accuracy of about 77 percent for the Philadelphia MULC. A rule-based feature extraction software was used to identify Full accuracy results are reported in the metadata. Accuracy five common land cover classes: Impervious Surface, information for the source data sets can be found on their Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. respective web sites and metadata. Most errors are not evident Ancillary data were primarily used to map three additional when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Philadephia MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Development Team for further inquiries. Acknowledgements EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Philadephia MULC was developed by the UVM Spatial Analysis Lab, Gillian Gundersen, EPA Student Services Contractor and Drew Pilant, EPA. Figure 3: Philadelphia MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A Land Use and LC Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964, U. S. Dept. of Interior. 2. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354. 3. U.S. Department of Agriculture, Farm Service Agency, 2010. Aerial Photography Field Office: U.S. Department of Agriculture Web page. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. 4. U.S. Fish and Wildlife Service. National Wetlands Inventory digital data. Website: http://wetlands.fws.gov/. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services, 14, 45-55. EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2019 Meter Scale Urban Land Cover Phoenix, Arizona This EnviroAtlas map shows land cover for the Phoenix, AZ area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous, and Agriculture.

More than 85 EnviroAtlas data layers incorporate MULC in their computation, including:

• Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) Figure 1: Areal percentage of MULC classes for Phoenix, AZ. • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? Manual editing was used to reduce confusion between classes, The MULC data for Phoenix, AZ were generated from digital as needed. Data were organized and manipulated in a GIS. A image processing and object-based image analysis (OBIA) of description of the classification techniques and workflow is aerial photography and relevant ancillary datasets. The aerial given in the metadata. photography is from the United States Department of Agriculture (USDA) National Agricultural Imagery Program What are the limitations of these data? (NAIP)3 and includes four spectral bands (blue, green, red, and A land cover map is a model of the landscape, and by near infrared). This MULC used NAIP imagery collected in definition an imperfect representation of reality. However, summer/fall 2010. these maps are the best estimation of the truth based on the best available data. A formal accuracy assessment of 598 A rule-based feature extraction software was used to identify photo-interpreted reference points yielded an overall User’s six common land cover classes: Impervious Surface, Accuracy of about 69.2 percent for the Phoenix MULC. Full Soil/Barren, Grass/Herbaceous, Tree/Forest, Water, and accuracy results are reported in the metadata. Accuracy Agriculture. information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Phoenix MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Phoenix, Arizona area was developed by Arizona State University ERSG Lab and Figure 3: Phoenix MULC with 50% transparency displayed on top of supplemented by Matthew Dannenberg, EPA Student aerial imagery. The fine spatial detail shows individual buildings, trees, Services Contractor, Sam Pardo, EPA Student Services and roads. Contractor, and Drew Pilant, EPA.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency July 2018 Meter Scale Urban Land Cover Pittsburgh, PA This EnviroAtlas map shows land cover for the Pittsburgh, PA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous.

More than 85 EnviroAtlas data layers incorporate MULC in their computation, including:

• Total carbon stored in above ground tree biomass (mt) 3 Figure 1: Areal percentage of MULC classes for Pittsburgh, PA. • Reduction in annual stormwater runoff (m /yr) • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? Manual editing was used to reduce confusion between classes, The MULC data for Pittsburgh, PA were generated from as needed. Data were organized and manipulated in a GIS. A digital image processing and supervised classification of description of the classification techniques and workflow is aerial photography, LiDAR data, and relevant ancillary given in the metadata. datasets. The aerial photography is from the United States Department of Agriculture (USDA) National Agricultural What are the limitations of these data? Imagery Program (NAIP)3 and includes four spectral bands A land cover map is a model of the landscape, and by (blue, green, red, and near infrared). This MULC used NAIP definition an imperfect representation of reality. However, imagery collected in summer 2010 and LiDAR collected in these maps are the best estimation of the truth based on the 2006. best available data. A formal accuracy assessment of 581 photo-interpreted reference points yielded an overall User’s A supervised classification software was used to identify five Accuracy of about 86 percent for the Pittsburgh MULC. Full common land cover classes: Impervious Surface, Soil/Barren, accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, and Water. information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Pittsburgh MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Pittsburgh, PA area was developed by Jeremy Baynes, EPA Student Services Contractor and Drew Pilant, EPA.

Figure 3: Pittsburgh MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency July 2018 Meter Scale Urban Land Cover Portland, Maine This EnviroAtlas map shows land cover for the Portland, ME area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody 1 Wetland and Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to Figure 2: MULC for Portland, ME. recreation and green space, urban forestry, and urban landscape ecology. How can I use this information? This data layer can be used alone or combined visually and 3% analytically with other spatial data layers. MULC and its Water 1% derivatives support numerous lines of investigation into 7% Impervious human and ecosystem health in the urban landscape, such as: 23% Where are the green and gray spaces? Which streets can Soil/Barren 13% benefit from more trees? What areas are subject to urban heat Tree/Forest island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? 11% Grass/Herbaceous Agriculture More than 85 EnviroAtlas data layers incorporate MULC in 41% Woody Wetland their computation, including: 1% Emergent Wetland • Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) Figure 1: Areal percentage of MULC classes for Portland, ME. • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? classes: Wetlands (Woody and Emergent)4 and Agriculture. The MULC data for Portland, ME were generated from digital Manual editing was used to reduce confusion between classes, image processing and supervised classification of aerial as needed. Data were organized and manipulated in a GIS. A photography and relevant ancillary datasets. The aerial description of the classification techniques and workflow is photography is from the United States Department of given in the metadata. Agriculture (USDA) National Agricultural Imagery Program (NAIP)3 and includes four spectral bands (blue, green, red, and What are the limitations of these data? near infrared). This MULC used NAIP imagery collected in A land cover map is a model of the landscape, and by summer 2010. definition an imperfect representation of reality. However, these maps are the best estimation of the truth based on the A supervised classification software was used to identify five best available data. A formal accuracy assessment of 600 common land cover classes: Impervious Surface, Soil/Barren, photo-interpreted reference points yielded an overall User’s Grass/Herbaceous, Tree/Forest, and Water. Ancillary data Accuracy of about 87 percent for the Portland, ME MULC. were primarily used to map three additional Full accuracy results are reported in the metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Portland, ME MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Portland, ME area was developed by Jeremy Baynes, EPA Student Services Figure 3: Portland, ME MULC with 50% transparency displayed on top Contractor and Drew Pilant, EPA. of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency July 2018 Meter Scale Urban Land Cover Portland, Oregon This EnviroAtlas map shows land cover for the Portland, OR area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody 1 Wetland and Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater Figure 2: MULC for Portland, OR. management, urban heat island mitigation, access to recreation and green space, urban forestry, and urban How can I use this information? landscape ecology. This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 1% 1% derivatives support numerous lines of investigation into Water 6% human and ecosystem health in the urban landscape, such as: 10% Impervious Where are the green and gray spaces? Which streets can Soil/Barren benefit from more trees? What areas are subject to urban heat 25% island and stormwater runoff effects? Do urban streams have Tree/Forest healthy vegetated buffers? 30% Grass/Herbaceous 0.3% More than 85 EnviroAtlas data layers incorporate MULC in Agriculture 27% their computation, including: Woody Wetland • Total carbon stored in above ground tree biomass (mt) Emergent Wetland • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to Figure 1: Areal percentage of MULC classes for Portland, OR. sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? classes: Wetlands (Woody and Emergent)4 and Agriculture. The MULC data for Portland, Oregon were generated from Manual editing was used to reduce confusion between classes, digital image processing and supervised classification of as needed. Data were organized and manipulated in a GIS. A aerial photography, LiDAR data, and relevant ancillary description of the classification techniques and workflow is datasets. The aerial photography is from the United States given in the metadata. Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP)3 and includes four spectral bands What are the limitations of these data? (blue, green, red, and near infrared). This MULC used NAIP A land cover map is a model of the landscape, and by imagery collected in summer 2011 and 2012. LiDAR was definition an imperfect representation of reality. However, collected in 2007 and 2010. these maps are the best estimation of the truth based on the best available data. A formal accuracy assessment of 654 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 79 percent for the Portland, OR MULC. Grass/Herbaceous, Tree/Forest, and Water. Ancillary data Full accuracy results are reported in the metadata. Accuracy were primarily used to map three additional information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Portland, OR MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Portland, OR area was developed by Ben Riegel, Jeremy Baynes, and Charles Rudder, EPA Student Services Contractors, and Drew Pilant, Figure 3: Portland, OR MULC with 50% transparency displayed on top EPA. of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency August 2018 Meter Scale Urban Land Cover Salt Lake City, Utah This EnviroAtlas map shows land cover for the Salt Lake City, UT area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Shrub, Grass and Herbaceous, Woody 1 Wetland and Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to Figure 2: MULC for Salt Lake City, UT. recreation and green space, urban forestry, and urban How can I use this information? landscape ecology. This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 0.001% 1% Water derivatives support numerous lines of investigation into 6% human and ecosystem health in the urban landscape, such as: Impervious Where are the green and gray spaces? Which streets can 16% Soil/Barren benefit from more trees? What areas are subject to urban heat 40% Tree/Forest island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? 13% Shrub More than 85 EnviroAtlas data layers incorporate MULC in Grass/Herbaceous 19% their computation, including: 5% Woody Wetland • Total carbon stored in above ground tree biomass (mt) Emergent Wetland • Reduction in annual stormwater runoff (m3/yr) Figure 1: Areal percentage of MULC classes for Salt Lake City, UT. • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). 4 How were the data for this map created? Wetlands (Woody and Emergent) . Manual editing was used to reduce confusion between classes, as needed. Data were The MULC data for Salt Lake City, UT were generated from organized and manipulated in a GIS. A description of the digital image processing and object-based image analysis classification techniques and workflow is given in the (OBIA) of aerial photography, LiDAR data, and relevant metadata. ancillary datasets. The aerial photography is from the United States Department of Agriculture (USDA) National What are the limitations of these data? Agricultural Imagery Program (NAIP)3 and includes four A land cover map is a model of the landscape, and by spectral bands (blue, green, red, and near infrared). This definition an imperfect representation of reality. However, MULC used NAIP imagery collected in summer 2014 and these maps are the best estimation of the truth based on the LiDAR from 2006, 2011, and 2013. best available data. A formal accuracy assessment of 647 A rule-based feature extraction software was used to identify photo-interpreted reference points yielded an overall User’s six common land cover classes: Impervious Surface, Accuracy of about 79 percent for the Salt Lake City MULC. Soil/Barren, Shrub, Grass/Herbaceous, Tree/Forest, and Full accuracy results are reported in the metadata. Accuracy Water. Ancillary data were primarily used to map information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Salt Lake City MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Salt Lake City, UT area was developed by Daniel Rosenbaum, EPA Student Services Contractor and Drew Pilant, EPA.

Figure 3: Salt Lake City, UT MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency June 2018 Meter Scale Urban Land Cover Sonoma County, CA This EnviroAtlas map shows land cover for the Sonoma County, CA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Shrub, Grass and Herbaceous, Agriculture, Orchard, Woody Wetland and Emergent 1 Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater Figure 2: MULC for Sonoma County, CA. management, urban heat island mitigation, access to recreation and green space, urban forestry, and urban How can I use this information? landscape ecology. This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 1.4% 0.1% 1.3% 5.0% 4.2% Water derivatives support numerous lines of investigation into 2.9% Impervious human and ecosystem health in the urban landscape, such as: 8.1% Where are the green and gray spaces? Which streets can Soil/Barren benefit from more trees? What areas are subject to urban heat Tree/Forest island and stormwater runoff effects? Do urban streams have Shrub healthy vegetated buffers? 27.0% Grass/Herbaceous More than 85 EnviroAtlas data layers incorporate MULC in 47.7% Agriculture their computation, including: Orchard • Total carbon stored in above ground tree biomass (mt) 2.3% Woody Wetland • Reduction in annual stormwater runoff (m3/yr) Emergent Wetland • Value of asthma exacerbation cases avoided due to Figure 1: Areal percentage of MULC classes for Sonoma County, CA. sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? Orchard, and Wetlands (Woody and Emergent). Manual editing was used to reduce confusion between classes, as The MULC data for Sonoma County, CA were generated needed. Data were organized and manipulated in a GIS. A from digital image processing and a hybrid method using description of the classification techniques and workflow is object-based image analysis (OBIA) and threshold ruleset of given in the metadata. aerial photography, LiDAR data, and the Sonoma County Vegetation Mapping & LiDAR Program datasets. The aerial What are the limitations of these data? photography is from the Sonoma County Vegetation Mapping A land cover map is a model of the landscape, and by & LiDAR Program and includes four spectral bands (blue, definition an imperfect representation of reality. However, green, red, and near infrared) captured in 2011 and 2013. these maps are the best estimation of the truth based on the A threshold-based ruleset in combination with the OBIA- best available data. A formal accuracy assessment of 805 derived classes produced by the Sonoma County Vegetation photo-interpreted reference points yielded an overall User’s Mapping & LiDAR Program was used to identify ten common Accuracy of about 79 percent for the Sonoma County, CA. land cover classes: Impervious Surface, Soil/Barren, Full accuracy results are reported in the metadata. Accuracy Grass/Herbaceous, Tree/Forest, Shrub, Water, Agriculture, information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Sonoma County MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Sonoma County, CA area was developed by the Sonoma County Vegetation Mapping and LiDAR Program, Jeremy Baynes, EPA, Gillian Gundersen, Figure 3: Sonoma County, CA with 50% transparency displayed on top of Student Services Contractor and Drew Pilant, EPA. aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications

1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency February 2019 Meter Scale Urban Land Cover St. Louis, MO This EnviroAtlas map shows land cover for the St. Louis, MO How can I use this information? area at high spatial resolution (1 meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland and Emergent Wetland.1

How were the data for this map created? Ancillary data were used to map three additional classes: 4 The MULC data for St. Louis, MO were generated from Wetlands (Woody and Emergent) and Agriculture. Manual digital image processing, object-based image analysis editing was used to reduce confusion between classes, as (OBIA), and pixel based analysis of aerial photography, needed. Data were organized and manipulated in a GIS. A LiDAR data, and relevant ancillary datasets in three phases. description of the classification techniques and workflow is The aerial photography is from the United States Department given in the metadata. of Agriculture (USDA) National Agricultural Imagery Program (NAIP)3 and other sources and includes four spectral What are the limitations of these data? bands (blue, green, red, and near infrared). This MULC used A land cover map is a model of the landscape, and by NAIP and other imagery collected in summer/fall 2012, 2014, definition an imperfect representation of reality. However, 2015, and 2016. LiDAR was collected between 2008 and these maps are the best estimation of the truth based on the 2012. best available data. A formal accuracy assessment of 921 photo-interpreted reference points yielded an overall User’s Two different rule-based feature extraction software and one Accuracy of about 82 percent for the St. Louis MULC. Full pixel based supervised classification software were used to accuracy results are reported in the metadata. Accuracy identify five common land cover classes: Impervious Surface, information for the source data sets can be found on their Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the St. Louis MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Development Team for further inquiries. Acknowledgements EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the St. Louis, MO area were developed by Missouri Resource Assessment Partnership under David Diamond, Akhilesh Khopkar and Gillian Figure 3: St. Louis MULC with 50% transparency displayed on top of Gundersen, contractors to the EPA, and Drew Pilant, EPA. aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A Land Use and LC Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964, U. S. Dept. of Interior. 2. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354. 3. U.S. Department of Agriculture, Farm Service Agency, 2016. Aerial Photography Field Office: U.S. Department of Agriculture Web page. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. 4. U.S. Fish and Wildlife Service. National Wetlands Inventory digital data. Website: http://wetlands.fws.gov/. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services, 14, 45-55. EnviroAtlas: Led by the U.S. Environmental Protection Agency June 2019 Meter Scale Urban Land Cover Tampa, Florida This EnviroAtlas map shows land cover for the Tampa, Florida area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, 1 Woody Wetland and Emergent Wetland.

Anticipated users of MULC include city and regional planners, water authorities, wildlife and natural resource managers, public health officials, citizens, teachers, and students. Potential applications of these data include the design of wildlife corridors and riparian buffers, stormwater management, urban heat island mitigation, access to Figure 2: MULC for Tampa, FL. recreation and green space, urban forestry, and urban landscape ecology. How can I use this information? This data layer can be used alone or combined visually and analytically with other spatial data layers. MULC and its 1% 1% Water derivatives support numerous lines of investigation into 6% human and ecosystem health in the urban landscape, such as: Impervious 21% Where are the green and gray spaces? Which streets can 17% Soil/Barren benefit from more trees? What areas are subject to urban heat Tree/Forest island and stormwater runoff effects? Do urban streams have healthy vegetated buffers? 19% Grass/Herbaceous More than 85 EnviroAtlas data layers incorporate MULC in Agriculture 33% their computation, including: Woody Wetland • Total carbon stored in above ground tree biomass (mt) 2% Emergent Wetland • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to Figure 1: Areal percentage of MULC classes for Tampa, FL. sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). 4 How were the data for this map created? classes: Wetlands (Woody and Emergent) and Agriculture. Manual editing was used to reduce confusion between classes, The MULC data for Tampa, FL were generated from digital as needed. Data were organized and manipulated in a GIS. A image processing and supervised classification of aerial description of the classification techniques and workflow is photography and relevant ancillary datasets. The aerial given in the metadata. photography is from the United States Department of Agriculture (USDA) National Agricultural Imagery Program What are the limitations of these data? (NAIP)3 and includes four spectral bands (blue, green, red, and A land cover map is a model of the landscape, and by near infrared). This MULC used NAIP imagery collected in definition an imperfect representation of reality. However, spring 2010. these maps are the best estimation of the truth based on A supervised classification software was used to identify five the best available data. A formal accuracy assessment of common land cover classes: Impervious Surface, Soil/Barren, 597 photo-interpreted reference points yielded an overall Grass/Herbaceous, Tree/Forest, and Water. Ancillary data User’s Accuracy of about 71 percent for the Tampa were primarily used to map three additional MULC. Full accuracy results are reported in the metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Tampa, FL MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas meter scale land cover data for the Tampa, Florida area was developed by Matthew Dannenberg, EPA Student Services Contractor, and Drew Pilant, EPA.

Figure 3: Tampa MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency August 2018 Meter Scale Urban Land Cover Virginia Beach, VA This EnviroAtlas map shows land cover for the Virginia Beach, VA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland, and Emergent Wetland.1

6% Tree/Forest More than 85 EnviroAtlas data layers incorporate MULC in their computation, including: 12% 12% Grass/Herbaceous • Total carbon stored in above ground tree biomass 26% Agriculture 1% (metric tons) Woody Wetland • Reduction in annual stormwater runoff (m3/yr) Emergent Wetland • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy Figure 7: Areal percentage of MULC classes for Virginia Beach, VA road edge • Reduction in median load of nitrites and nitrates, soluble phosphorus, and total suspended solids (kg/yr) from filtration by trees.

1 CONTINUED ON BACK How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Virginia Beach were generated from description of the classification techniques and workflow is digital image processing and supervised feature extraction of given in the metadata. aerial photography, LiDAR data (2010; 2013) and relevant ancillary datasets. The aerial photography is from the Virginia What are the limitations of these data? Base Mapping Program (VBMP, 2013 leaf off) and the United A land cover map is a model of the landscape, and by States Department of Agriculture (USDA) National definition an imperfect representation of reality. However, Agricultural Imagery Program (NAIP, 2014 leaf on), 3 both of these maps are the best estimation of the truth based on the which include four spectral bands (blue, green, red, and near best available data. A formal accuracy assessment of 712 infrared). photo-interpreted reference points yielded an overall User’s A rule-based feature extraction software was used to identify Accuracy of about 84 percent for the Virginia Beach MULC. five common land cover classes: Impervious Surface, Full accuracy results are reported in the metadata. Accuracy Soil/Barren, Grass/Herbaceous, Tree/Forest, and Water. information for the source data sets can be found on their Ancillary data were primarily used to map Wetlands (Woody respective web sites and metadata. Most errors are not evident and Emergent)4 and Agriculture. when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Austin MULC data were created. A selection of resources related to land cover is available below. To ask specific questions about these data, please contact the EnviroAtlas Team. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Virginia Beach, VA area was created by WorldView Solutions, Inc. for the Virginia Geographic Information Network, amended by the Chesapeake Conservancy, and further amended by EPA Figure 3: Virginia Beach, VA MULC with 50% transparency displayed on analysts. top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads.

Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018. 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency

February 2018 Meter Scale Urban Land Cover Washington DC This EnviroAtlas map shows land cover for the Washington DC area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees and Forest, Grass and Herbaceous, Agriculture, Woody Wetland and Emergent Wetland.1

More than 85 EnviroAtlas data layers incorporate MULC in their computation, including:

• Total carbon stored in above ground tree biomass (mt) • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to

sulfur dioxide removed ($/yr) Figure 1: Areal percentage of MULC classes for Washington DC. • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr).

How were the data for this map created? five common land cover classes: Impervious Surface, The MULC data for Washington D.C. were developed by the Soil/Barren, Grass/Herbaceous, Tree, and Water. Three 5 Chesapeake Bay Conservancy3 and converted into additional classes: Wetlands (Woody and Emergent) and EnviroAtlas MULC by the EPA. The data were generated Agriculture were added to the classified product. Manual from digital image processing and supervised classification of editing was used to reduce confusion between classes, as aerial photography, LiDAR data, and relevant ancillary needed. Data were organized and manipulated in a GIS. A datasets. The aerial photography is from the United States description of the classification techniques and workflow is Department of Agriculture (USDA) National Agricultural given in the metadata. Imagery Program (NAIP)4 and includes four spectral bands (blue, green, red and NIR). This MULC used NAIP imagery What are the limitations of these data? collected in 2014 for the Virginia area and summer/fall 2013 A land cover map is a model of the landscape, and an for the Maryland and DC sections. LiDAR was subject to imperfect representation of reality. However, these maps are availability and collected between 2002 and 2016. the best estimation of the truth based on the best available data. A formal accuracy assessment of 711 photo-interpreted A rule-based feature extraction software was used to identify reference points yielded an overall User’s accuracy of about 85 percent for the Washington DC MULC. Full accuracy results are reported in the metadata. Accuracy information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Washington DC MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Development Team for further inquiries. Acknowledgements EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Washington DC area were Figure 3: Washington DC MULC with 50% transparency displayed developed by Chesapeake Conservancy, Shanti Shrestha, on top of aerial imagery. The fine spatial detail shows individual ORAU Contractor and Drew Pilant, EPA. buildings, trees, and roads.

Selected Publications 1. Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer. 1976. A Land Use and LC Classification System for Use with Remote Sensor Data. Geological Survey Professional Paper 964, U. S. Dept. of Interior. 2. Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354. 3. Chesapeake Conservancy, 2019. Land Cover Data Project. Website: http://chesapeakeconservancy.org/conservation- innovation-center/high-resolution-data/land-cover-data-project/. 4. U.S. Department of Agriculture, Farm Service Agency, 2010. Aerial Photography Field Office: U.S. Department of Agriculture Web page. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. 5. U.S. Fish and Wildlife Service. National Wetlands Inventory digital data. Website: http://wetlands.fws.gov/. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services, 14, 45-55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency April 2019 Meter Scale Urban Land Cover Woodbine, IA This EnviroAtlas map shows land cover for the Woodbine, IA area at high spatial resolution (1-meter pixels). The land cover classes are Water, Impervious Surface, Soil and Barren, Trees 1 and Forest, Grass and Herbaceous, and Agriculture.

• Total carbon stored in above ground tree biomass (mt) Figure 1: Areal percentage of MULC classes for Woodbine, IA. • Reduction in annual stormwater runoff (m3/yr) • Value of asthma exacerbation cases avoided due to sulfur dioxide removed ($/yr) • Estimated percent of tree cover within 26 m of a busy road edge • Reduction in median load of nitrites and nitrates, soluble phosphorous, total suspended solids, etc. (kg/yr). How were the data for this map created? Manual editing was used to reduce confusion between classes, as needed. Data were organized and manipulated in a GIS. A The MULC data for Woodbine, IA were generated from description of the classification techniques and workflow is digital image processing and supervised classification of given in the metadata. aerial photography, LiDAR data, and relevant ancillary datasets. The aerial photography is from the United States What are the limitations of these data? Department of Agriculture (USDA) National Agricultural A land cover map is a model of the landscape, and by Imagery Program (NAIP)3 and includes four spectral bands definition an imperfect representation of reality. However, (blue, green, red, and near infrared). This MULC used NAIP these maps are the best estimation of the truth based on the imagery collected in summer 2011 and LiDAR from 2009. best available data. A formal accuracy assessment of 600 A supervised classification software was used to identify five photo-interpreted reference points yielded an overall User’s common land cover classes: Impervious Surface, Soil/Barren, Accuracy of about 87 percent for the Woodbine MULC. Full Grass/Herbaceous, Tree/Forest, and Water. Ancillary data accuracy results are reported in the metadata. Accuracy were primarily used to map Agriculture. information for the source data sets can be found on their respective web sites and metadata. Most errors are not evident when viewing at map scales smaller than approximately 1:8000. How can I access these data? EnviroAtlas data can be accessed by: (1) viewing them in the interactive map, (2) using the web services, or (3) downloading the data directly. Where can I get more information? Please refer to the metadata for a detailed description of how the Woodbine MULC data were created. The EnviroAtlas data download page links to MULC metadata. A selection of resources related to land cover is available below. Please contact the EnviroAtlas Team for further inquiries. Acknowledgments EnviroAtlas is a collaborative effort led by EPA. The EnviroAtlas MULC data for the Woodbine, IA area was developed by Charles Rudder, EPA Student Services Contractor and Drew Pilant, EPA. Figure 3: Woodbine MULC with 50% transparency displayed on top of aerial imagery. The fine spatial detail shows individual buildings, trees, and roads. Selected Publications 1. Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer. 1976. A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964, U. S. Geological Survey, Washington, D.C. 2. Homer, C.G., J.A. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N.D. Herold, J.D. Wickham, and K. Megown. 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81(5): 345–354. 3. U.S. Department of Agriculture, Farm Service Agency. 2013. Aerial Photography Field Office, NAIP Imagery. Accessed March 2018 4. U.S. Fish and Wildlife Service. 2016. National Wetlands Inventory. Accessed March 2018. Pickard, B. R., Daniel, J., Mehaffey, M., Jackson, L. E., & Neale, A. 2015. EnviroAtlas: A new geospatial tool to foster ecosystem services science and resource management. Ecosystem Services 14: 45–55.

EnviroAtlas: Led by the U.S. Environmental Protection Agency August 2018