{f4db962b-0c81-42bc-b934-446fed441201} eng; USA utf8 dataset EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact 2025-02-13 ISO 19115 Geographic Information - Metadata ISO 19115:2003/Cor.1:2006(E) EnviroAtlas -- Salt Lake City, UT -- Meter-Scale Urban Land Cover (MULC) Data (2014) 2018-09-07 publication U.S. EPA Office of Research & Development (ORD) - National Exposure Research Laboratory (NERL) Research Triangle Park NC publisher US Environmental Protection Agency (US EPA) originator These Salt Lake City, Utah MULC data were created using 2014, leaf-on United States Department of Agriculture (USDA) National Agriculture Imagery Program (NAIP) 1-meter resolution ortho imagery and LiDAR data collected in 2006-2007, 2011, and 2013-2014. This rule-based classification used object-based image analysis (OBIA) methods and was performed using eCognition Developer 9.3 (Trimble) with pre and post processing performed in ArcMap Desktop version 10.5.1 (ESRI) and ENVI LiDAR version 5.3 (Harris Geospatial Solutions). The area was selected based on the US Census Bureau's 2010 Urban Area for Salt Lake City with a 1 km buffer added. This dataset has an overall user's accuracy of 78.6% and an overall fuzzy user's accuracy of 82.5%. Seven land cover classes were mapped: Water, Impervious Surfaces, Soil/Barren, Tree/Forest, Shrubs, Grass/Herbaceous Non-Woody Vegetation, and Wetlands (Woody and Emergent). An analysis of 600 photo-interpreted land cover reference points yielded an overall user's accuracy of 78.7% (MAX) and an overall fuzzy user's accuracy of 82.5% (RIGHT) (see confusion matrices below). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). The purpose of the Meter-Scale Urban Land Cover (MULC) data is to provide a detailed snapshot of the urban environment and ecosystem for analysis of multiple human and ecosystem health metrics and services. This analysis is performed typically by U.S. Census block group. Greenspace and impervious surface land cover classes figure prominently into such analyses. Land cover maps are integral to sound, scientific decision making and effective stewardship. A high resolution urban LC map quantitatively and qualitatively represents the physical environment and ecosystems of a city. At 1 meter resolution, MULC data capture fine scale land cover variability in urban environments, and are complementary to the NLCD, which at 30 meter resolution, are usually too coarse to measure many urban phenomena. There are many potential applications, for example: - Knowing the exact location and size of green and grey spaces; - Identifying highly impervious areas that would most benefit from additional trees and other vegetation to mitigate surface runoff and urban heat island effects; - Measuring the width of urban riparian buffers; - Locating potential wildlife corridors, gaps and linkages; - Planning park development for maximum benefits. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation. completed EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact asNeeded Ecology Ecosystem Environment Health Impact Land Water theme EPA GIS Keyword Thesaurus Ecosystem Services EnviroAtlas Environment Environmental Atlas theme EnviroAtlas Agriculture Biophysical Communities Ecosystem Services EnviroAtlas Environment Environmental Atlas Green Space Land Land Cover Surface Water Water Wetlands theme User Utah Salt Lake City, UT place None https://edg.epa.gov/EPA_Data_License.html otherRestrictions Use Constraints: None. Please check sources, scale, accuracy, currency and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated. otherRestrictions Access Constraints: None PUBLIC http://project-open-data.github.io/schema/#accessLevel Standard Technical Controls eng; USA environment geoscientificInformation imageryBaseMapsEarthCover -112.303320 -111.509055 40.379029 40.978978 2014-01-01 true Entity and Attribute Information Entity and Attribute Overview: For easier readability, this XML metadata can be opened in a text editor (e.g., Notepad). The Salt Lake City study area is 2,244 square kilometers that includes Salt Lake City, Salt Lake County, and small sections of surrounding counties (Wasatch, Summit, Davis, Tooele, Utah, and Morgan). The MULC data for Salt Lake City, UT were generated from digital image processing and object-based image analysis (OBIA) of aerial photography, LiDAR data, and relevant ancillary datasets. All data was processed in UTM 12N, NAD83 projection unless otherwise noted. Pre and post processing was completed in ArcMap Desktop version 10.5.1 (ESRI) and ENVI LiDAR version 5.3 (Harris Geospatial Solutions). The aerial imagery is from the United States Department of Agriculture's (USDA) National Agriculture Imagery Program (NAIP) and was collected in the summer and fall of 2014. NAIP contains four spectral bands -- blue, green, red and near-infrared -- with 1-meter pixel resolution and 8-bit pixel depth. In addition to these four spectral bands, a Normalized Difference Vegetation Index (NDVI) raster was derived from the NAIP data using raster algebra ((NIR-Red)/(NIR+Red)). The study area had full coverage for Light Detection and Ranging (LiDAR) collected in 2006-2007 (Utah Automated Geographic Reference Center AGRC Topo, 2 m point spacing), 2011 (Utah Great Salt Lake Wetlands - Great Salt Lake South Farmington Bay, 1 m point spacing), and 2013-2014 (Wasatch Front Lidar Collection, 0.3 m point spacing) (https://gis.utah.gov/data/elevation-and-terrain/). LiDAR data was processed in ArcMap into surface raster datasets representing height of above-ground features (normalized digital surface model, nDSM) and pulse intensity (See processing steps below). Vector datasets were obtained for water bodies and road centerlines from Utah's State Geographic Information Database (https://gis.utah.gov/) and were used to support the classification. Additionally, a building footprint was created in ENVI LiDAR using the 2013-2014 point cloud data, which was the highest quality data and provided coverage for the urban core of the study area. eCognition Developer version 9.3 (http://www.ecognition.com/) was used to perform a rule-based classification. Tiles were processed individually in eCognition that were approximately 3,000 x 3,000 pixels at 1-meter resolution for the urban core of the study area. The peripheral (non-urban core) portions of the study area were processed using full NAIP Quarter Quad boundaries, which are approximately 45 km2. The general workflow (outlined below) was based on rulesets developed by the analysts who had expert knowledge of the study area. Raster layers were segmented into polygons of "like" pixels and multiple attributes were computed for each segment before building rules to classify features of interest. Rules typically were based on several criteria and included attributes that ranged from spectrally derived values, height and intensity values, spatial relationships between objects, and object geometric characteristics. A "good" segmentation ideally creates polygons corresponding to homogeneous features of interest (e.g. trees, buildings, roads, etc). The following is a detailed description of the multi-step classification procedure. This MULC dataset consists of the following land cover classes: Water (10), Impervious Surface (20), Soil and Barren (30), Trees and Forest (40), Shrub (52), Grass and Herbaceous (70), Woody Wetland (91), Emergent Wetland (92). Processing Steps: Step 1: Individual 2014 4 band (Red, Green, Blue, Near Infrared) NAIP tiles were clipped from USDA NAIP Web Coverage Service (https://gis.apfo.usda.gov/arcgis/services) using USGS Quarter Quadrangle vector file. Step 2: Return intensity and normalized digital surface model (nDSM) rasters were created from the three LiDAR point cloud datasets listed above. Return intensity for all first return points were filtered within the LiDAR dataset and then rasterized. The nDSM represented the height of objects on the surface of the earth where ground points are equal to zero. Creating the nDSM raster involved several steps. LiDAR data were processed into a digital surface model (DSM) and a digital elevation model (DEM). The DSM consists of all first return points in the dataset, resulting in surface elevation grid that includes objects that are above the ground, such as trees and buildings. The DEM consists of only ground points, resulting in an elevation grid of bare earth without built structures or vegetation. Finally, the nDSM raster was created by subtracting the DEM from the DSM. Step 3: An NDVI raster was created using the NAIP imagery as follows: NDVI = (NIR-Red)/(NIR+Red), where NIR is near infrared intensity (brightness), Red is intensity (brightness) of visible red light and NDVI is a dimensionless number ranging from -1 to 1. NDVI exploits the high NIR reflectance of vegetation to separate it from non-vegetation. In addition, the NDVI ratio helps to normalize variable illumination and atmospheric conditions across the entire image mosaic. Step 4: Each raster dataset that covered the full Salt Lake City study area was divided into smaller rectangular subsets. The purpose of creating smaller tiles was to make it less processing intensive for image segmentation in eCognition. eCognition allows for a customized import of raster datasets and vector files. This import method creates projects for each section of the study area within a single workspace. This was completed twice for Salt Lake City, once using full USDA Quarter Quads for the peripheral (non-urban core) areas that required less detailed processing because they were less developed, and a second time using Quarter Quads divided into 4 sections (Octo Quads) to achieve smaller tiles that were approximately 3,000 x 3,000 pixels at 1 meter resolution. A 10 percent overlap between tiles was included in order to ensure easier mosaicking during post-processing. Step 5: The classification: The core component of the eCognition rule-based classification process consisted of the creation of a ruleset decision tree that took all data inputs and, through successive segmentation and classification algorithms, generated a classified image. Object features (e.g., area, mean near infrared pixel values per object) were assigned to objects following each segmentation. Through trial and error, threshold values, object sizes, shapes, and contextual relationships were identified and used to systematically classify objects. Where applicable, vector data was used in the classification. Water bodies, building footprints, and road centerlines were available as vector data and were applied to the classification. All "tall", or above ground features, which consisted of Tree/Forest, Shrub, and Buildings, were classified first. The eCognition "Contrast Split Segmentation" algorithm segmented all objects > 2 m, leaving all objects <= 2 m unsegmented. These tall object segments were then further segmented using the "Multiresolution Segmentation" algorithm in order to separate Trees from Buildings. Segmented tall objects with mean NDVI >= 0.1 were classified as Trees, and all other objects were left unclassified. Building footprints generated from raw LAS LiDAR data in ENVI LiDAR were classified as Building. Missing buildings and inaccuracies in the building footprint classification were classified as buildings using a combination of nDSM and the blue imagery band, which performed well at classifying impervious surfaces. Building classification was then placed into the Impervious class. Shrubs were identified using the Multiresolution Segment algorithm, weighting nDSM, NIR band, and NDVI most heavily to be able to successfully classify objects by height and the presence of green vegetation. A fine segmentation was used in order to locate as close to individual Shrubs as possible. Objects >= 0.5 m and <= 2 m with NDVI >= 0.1 were classified as Shrub. Additionally, any of these objects with high NDVI but < 0.5 m in height were classified as Grass/Herbaceous. "Short" objects were to consist of "Water", "Grass/Herbaceous", "Impervious Surface", and "Soil/Barren". The vector layer available from the Utah Mapping Portal well represented the water visible in the NAIP imagery and included the coastal water of the Salt Lake, as well as all small lakes and streams. Therefore, this vector file was used as the water classification for the study area and no other water was classified. A distance raster was generated in order to classify roads (Impervious) using the roads centerlines vector layer. This distance raster was then segmented and objects near the centerline were classified as Impervious. Other Impervious was classified using a ratio of the NAIP blue band, which indicated the amount that blue contributed to total brightness of each object. Soil/Barren and Grass/Herbaceous were classified primarily using various LiDAR intensity thresholds. Salt Lake City had widespread areas that included brown Grass that, from a spectral standpoint was more consistent with Soil. However, in consultation with local experts and in referencing street level photography from the same time of year (Google Maps/Bing Maps), it was determined that nearly all of these areas in the urban core of the study area actually contained senescent brown Grass or patchy green Grass. In times of the year with greater precipitation, these areas often contained fully developed green Grass. These areas, from an ecosystem services point of view, behave most similarly to Grass/Herbaceous. As a result, intensity thresholds were set to favor a Grass classification in the urban core area. Areas in the peripheral portions of the study area (non-urban core), however, contained more true Soil/Barren areas, including sections at higher elevation that were comprised of bare rock. A second ruleset was created for these peripheral areas in which intensity thresholds allowed for more Soil in the classification. Shadow in the NAIP imagery created erroneous classification. To correct this, a temporary Shadow class was created for objects with brightness values >= 30 and <= 50. These Shadow objects were classified based on neighboring objects. For example, if Shadow objects shared outer borders with Tree by >= 30 percent, the shadow object was classified as Tree/Forest. Due to normal differences between the LiDAR and NAIP imagery (which are different types of data, collected at different times, using different methods), the edges of some trees were erroneously classified as Grass. This happened because the NDVI generated from the imagery showed a high vegetation signature but the nDSM generated from the LiDAR showed ground-level heights. To correct this, Tree pixels that bordered Grass were grown by 1-2 pixels, which corrected a significant portion of these errors. The ruleset used in the peripheral portion of the study had a pixelated Grass classification, most likely because the LiDAR dataset was older and lower quality for this portion of the study area. For that ruleset, pixel-density filters were used to convert some of these Grass pixels to neighboring pixels. Step 6: The final portion of the classification consisted of hand editing using the suite of editing tools in the eCognition environment. Each area being processed was visually assessed for errors. When errors were located, the class that needed reclassifying was re-segmented in order to change the exact area in question, and then corrected. Step 7: Finished classifications were exported from eCognition as GeoTiff raster datasets and post-processed in ArcGIS 10.5. Completed tiles were re-classified to match EnviroAtlas MULC class codes and then mosaicked into a single raster dataset. Visual QA was performed on the mosaicked raster at 1:5000 m scale and noted errors were corrected using the Reclassify (Spatial Analyst) tool in ArcMap. Step 8: Wetlands ancillary vector data from the National Wetlands Inventory (NWI) was overlayed on the completed land cover raster in order to identify the best estimate of wetland areas. For land cover located under the wetlands vector layer, if land cover was Tree, it was reclassified to Woody Wetland (91), and if land cover was Grass, it was reclassified to Emergent Wetland (92). ***-------------------------------------------*** Step 9: Accuracy Assessment A second, independent analyst photointerpreted a random sample of six hundred reference points using a fuzzy classification approach (Gopal and Woodcock, 1994). Ancillary image data such as Google Satellite and Street Views, and Bing Aerial and Birdseye views, were used as appropriate to substantiate the interpretation based on the NAIP imagery. Uninterpretable points (e.g., dark shadow) were noted and discarded. As follows, the independent analyst assigned a confidence value to her interpretation using a scale of 1 to 5 for the fuzzy assessment. 1: Absolutely wrong: classification value is unacceptable (Very Wrong); 2: Understandable but Wrong: classification value is not good. There is something about the site that makes the answer understandable, but there is clearly a better answer. Classification would pose a problem for users of the map. (Not Right); 3: Reasonable or Acceptable: Maybe not the best possible classification but it is acceptable; the classification does not pose a problem to users of the map. (Right); 4: Good Answer: Would be happy to find this classification given on the map (Very Right); 5: Absolutely Right: No doubt about the match. (Perfect) If any land cover class ended up with less than fifty reference points a stratified random sample of additional points was generated and interpreted. This step typically applies only to the Soil and Barren class. Thus, in the end, all classes had a minimum of fifty reference samples. The final accuracy assessment resulted in confusion matrices for a non-fuzzy method (MAX) and a fuzzy method (RIGHT), both presented below. The fuzzy method allowed for uncertainty in the analyst's photo interpretation due to complex land cover characteristics. For example, a point located within a pixel on land comprised of patchy grass and soil can be assigned a 4 for grass and a 3 for soil. This was accounted for by the RIGHT results, while MAX only accounted for the highest value recorded by the interpreter. For easier readability, this XML metadata can be opened in a text editor (e.g., Notepad) and the confusion matrix may be copied from text editor to an Excel spreadsheet. Confusion matrices can also be viewed in original formatting by opening in ArcCatalog: A second, independent analyst photointerpreted a random sample of six hundred reference points using a fuzzy classification approach (Gopal and Woodcock, 1994). Ancillary image data such as Google Satellite and Street Views, and Bing Aerial and Birdseye views, were used as appropriate to substantiate the interpretation based on the NAIP imagery. Uninterpretable points (e.g., dark shadow) were noted and discarded. As follows, the independent analyst assigned a confidence value to her interpretation using a scale of 1 to 5 for the fuzzy assessment. 1: Absolutely wrong: classification value is unacceptable (Very Wrong); 2: Understandable but Wrong: classification value is not good. There is something about the site that makes the answer understandable, but there is clearly a better answer. Classification would pose a problem for users of the map. (Not Right); 3: Reasonable or Acceptable: Maybe not the best possible classification but it is acceptable; the classification does not pose a problem to users of the map. (Right); 4: Good Answer: Would be happy to find this classification given on the map (Very Right); 5: Absolutely Right: No doubt about the match. (Perfect) If any land cover class ended up with less than fifty reference points a stratified random sample of additional points was generated and interpreted. This step typically applies only to the Soil and Barren class. Thus, in the end, all classes had a minimum of fifty reference samples. The final accuracy assessment resulted in confusion matrices for a non-fuzzy method (MAX) and a fuzzy method (RIGHT), both presented below. The fuzzy method allowed for uncertainty in the analyst's photo interpretation due to complex land cover characteristics. For example, a point located within a pixel on land comprised of patchy grass and soil can be assigned a 4 for grass and a 3 for soil. This was accounted for by the RIGHT results, while MAX only accounted for the highest value recorded by the interpreter. For easier readability, this XML metadata can be opened in a text editor (e.g., Notepad) and the confusion matrix may be copied from text editor to an Excel spreadsheet. Confusion matrices can also be viewed in original formatting by opening in ArcCatalog: MAX Grass_Herb Impervious Shrub SoilBarren TreeForest Water Row Total User's Accuracy Grass_Herb 205 11 7 8 8 1 240 0.854167 Impervious 10 91 0 3 1 0 105 0.866667 Shrub 15 0 37 0 2 0 54 0.685185 SoilBarren 24 0 0 46 1 1 72 0.638889 TreeForest 14 5 8 3 89 0 119 0.747899 Water 8 0 0 8 0 41 57 0.719298 Row Total 276 107 52 68 101 43 647 Producer's Accuracy 0.742754 0.850467 0.711538 0.676471 0.881188 0.953488 Overall Accuracy 0.786708 K_Hat 0.720071 K Variance 0.000452 RIGHT Grass_Herb Impervious Shrub SoilBarren TreeForest Water Row Total User's Accuracy Grass_Herb 211 10 6 4 8 1 240 0.879167 Impervious 10 93 0 1 1 0 105 0.885714 Shrub 15 0 38 0 1 0 54 0.703704 SoilBarren 16 0 0 54 1 1 72 0.75 TreeForest 12 5 7 3 92 0 119 0.773109 Water 5 0 0 6 0 46 57 0.807018 Row Total 269 108 51 68 103 48 647 Producer's Accuracy 0.784387 0.861111 0.745098 0.794118 0.893204 0.958333 Overall Accuracy 0.825348 K_Hat 0.771575 K Variance 0.000386 The lowest User's Accuracy, or errors of commission, were for Soil/Barren (63.8%) and for Shrub (68.5%). Soil and Grass interblend in nature, a continuum from completely barren soil and rock to completely vegetated grass-herbaceous cover. Soil-Grass confusion is typically a large contributor to overall error in MULC data. A portion of this error is an artifact due to the difficulty of defining concise operational rules for distinguishing between brown or sparse grass, and soil, both in imagery and in the field. In NAIP imagery, acquired during dry summer conditions, grass pixels may appear tan, brown or grey and devoid of vegetation, and thus be mislabeled as Soil. However, viewed as a NIR false color composite with NIR in the red display channel, the same pixels may appear with red tones, indicating photosynthetic activity. Shrub classification is highly dependent on having quality LiDAR data. The Salt Lake City LiDAR was older and lower quality in the peripheral portion of the Salt Lake City study area and this is where most of the pixels classified as Shrub (and true Shrub presence) occurred. Shrub classification can also be difficult to photo-interpret because MULC shrub heights range from 0.5 m to 2 m, the lower of which may appear herbaceous in aerial and satellite imagery. References: Gopal, S. and Woodcock, C. 1994. Theory and Methods for Accuracy Assessment of Thematic Maps Using Fuzzy Sets. Photogrammetric Engineering and Remote Sensing 60(2), 181-188. Entity and Attribute Detail Citation: https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas distributor https://enviroatlas.epa.gov/download/SLCUT_MULC_tif.zip download Download The URL providing direct access to the downloadable dataset. download https://enviroatlas.epa.gov/arcgis/rest/services/Supplemental/Landcover_AllCommunities/ImageServer ESRI:ArcGIS API The endpoint of a web service to access the dataset (REST endpoint, WMS GetCapabilities URL, or a SOAP WSDL endpoint). webMapService https://www.epa.gov/enviroatlas/enviroatlas-data information Documentation URL for accessing related documents such as data dictionary, technical information about a dataset, developer documentation, etc. information https://www.epa.gov/enviroatlas information Home Page Alternative landing page used to redirect user to a contextual, Agency-hosted "homepage" for the Dataset or API information dataset Horizontal Positional Accuracy Report Data were collected using methods that are accurate to within 2-5 meters (EPA National Geospatial Data Policy [NGDP] Accuracy Tier 2). For more information, please see EPA's NGDP at https://www.epa.gov/geospatial/geospatial-policies-and-standards) Features represented have not been tested for completeness Features represented have not been tested for completeness Tests for integrity have not been performed. This lineage was generated by the FGDC CSDGM to 19139 transformation See the Overview section for the complete processing workflow. 2018-09-01T00:00:00 unknown 2029-02-13 This metadata was automatically generated from the FGDC Content Standard for Digital Geospatial Metadata standard (version FGDC-STD-001-1998) using the 2024-02-09T11:11:00 version of the FGDC CSDGM to ISO 19139 transform. EnviroAtlas Coordinator U.S. Environmental Protection Agency, Office of Research and Development-Sustainable and Healthy Communities Research Program, EnviroAtlas Geospatial Data Owner (919) 541-3832 109 T.W. Alexander Drive Research Triangle Park NC 27709 EnviroAtlas@epa.gov https://www.epa.gov/enviroatlas pointOfContact