This dataset is available for download from: Wetlands (File Geodatabase).

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader land cover raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log

Version 1.1 (January 26, 2023)

• Full resolution of wetlands replaced a coarser resolution version that was previously shared. Also, file type changed from polygon to raster (feature service to tile layer service).

Georgia 2021 Tree Canopy Cover (TCC) raster subset from the national TCC dataset created by the USDA Forest Service. Click here to download the dataset.

Ce modèle numérique d'altitude (MNT) a été créé à partir du jeu de données de terrain principal (MTD) de Hakai au moyen de l'outil « MNT to raster » dans ArcGIS for Desktop d'ESRI à l'aide d'une méthode d'échantillonnage Natural Neighbour. Le DEM a été créé en mode natif à une résolution de 3 m. Ce DEM a été fixé à une zone tampon à 10 m du rivage. Une combinaison de différentes altitudes autour de l'île a été utilisée pour créer le rivage.

Le MNT qui en résulte est un modèle d'élévation hydroaplati en terre nue et donc considéré comme « topographiquement complet ». Chaque pixel représente l'altitude en mètres au-dessus du niveau moyen de la mer de la terre nue à cet endroit. Le système de référence vertical est le « Système de référence géodésique vertical canadien 1928 » (CGVD28).

Hakai a produit des DEM à différentes résolutions de manière native directement à partir du MTD des données LiDAR. Pour vos recherches, veuillez utiliser le produit de résolution approprié parmi ceux produits par Hakai. Afin de maintenir l'homogénéité, il n'est pas recommandé de procéder à un suréchantillonnage ou à une mise à l'échelle supérieure à partir de produits de résolution supérieure car cela pourrait introduire et propager des erreurs de différentes grandeurs dans les analyses en cours ; veuillez utiliser des produits déjà disponibles, et si vous avez besoin d'une résolution non disponible, contactez data@hakai.org afin d'obtenir un DEM produit directement à partir du MTD.

Les DEM topographiquement complets suivants ont été produits en mode natif à partir du DTM par Hakai :

MNE topographiquement complète de 3 m. Ce produit a été utilisé pour produire les ensembles de données hydrologiques de Hakai (cours d'eau et bassins versants) DEM Topographiquement complet de 20 m. Compatible avec les mesures du couvert végétal de Hakai et les rasters associés. MNT topographiquement complet de 25 m. Compatible avec les produits de données TRIM BCGov. DEM Topographiquement complet de 30 m. Compatible avec les produits STRM.

Création du jeu de données de terrain principal

Nuages de points LiDAR issus de missions effectuées en 2012 et 2014 au-dessus de l'île Calvert où ils ont été chargés (XYZ uniquement) dans une classe d'entités ponctuelles d'une géodatabase ESRI.

Seul le sol (classe 2) renvoie l'endroit où il est chargé dans la géodatabase.

Le « jeu de données de MNT » ESRI a été créé dans la même géodatabase à l'aide des points LiDAR en tant que points de masse intégrés.

Les lacs et les étangs TEM Plus avec des valeurs d'altitude moyennes au-dessus des miroirs des plans d'eau ont été utilisés comme lignes de rupture de remplacement dur pour obtenir un hydroaplatissement.

La géométrie d'emprise minimale de toutes les étendues de fichiers LAS contigus a été utilisée comme masque de découpe souple lors de la création du jeu de données de MNT en tant que limite de projet.

Le système de coordonnées horizontales et le datum utilisés pour le jeu de données de MNT sont : UTM Zone 9 NAD1983 ; le système de référence vertical a été défini sur CGVD28. Les deux systèmes de référence correspondent au système de référence natif des nuages de points LiDAR.

L'espacement minimal des points défini pendant la création du jeu de données de MNT a été défini sur 1.

This submission revises the analysis and products for Thermal Quality Analysis for the northern half of the Appalachian Basin (https://gdr.openei.org/submissions/638) This submission is one of five major parts of a Low Temperature Geothermal Play Fairway Analysis. Phase 1 of the project identified potential Geothermal Play Fairways within the Appalachian basin of Pennsylvania, West Virginia and New York. This submission includes a subset of the necessary shapefiles, rasters, datasets, code, and references to code repositories that were used to create the thermal resource and risk factor maps as part of the project. This subset is those contents that were improved upon during calendar year 2016. Figures are provided as examples of some shapefiles and rasters. See also: Final Report: Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (https://gdr.openei.org/submissions/899). The 2015 data submission should be visited to obtain: 1) the regional standardized 1 square km grid used in the project as points (cell centers), polygons, and as a raster, 2) the raw well data for the state well temperature databases, 3) the COSUNA section shapefile and formation thermal conductivities by state as *.xlsx files, 4) the sediment thickness map and 30 m Digital Elevation Model for the Appalachian Basin as GeoTIFF raster files, 5) the BHT correction sections shapefile and drilling fluid databases as *.csv files, 6) the unbuffered interpolation regions as shapefiles, 7) several 50 km buffered interpolation regions as shapefiles, 8) several gridded interpolation regions as raster files, 9) an R script for organizing the thermal data and running the local spatial outlier analysis, 10) shapefiles and rasters for the prediction, uncertainty, and cross validation of the temperature at 1.5 km, 2.5 km, and 3.5 km depth, 11) shapefiles and rasters for the prediction, uncertainty, and cross validation depth to 100 degrees C, 12) an ArcGIS toolbox for thermal risk factor models, 13) an ArcGIS model for extracting results specific to each county of interest, 14) thermal resource cross section plots, 15) the geothermal Play Fairways.

Georgia 2016 Tree Canopy Cover (TCC) raster subset from the national TCC dataset created by the USDA Forest Service. Click here to download the dataset.

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This data release contains all digital geographic data used and produced by the Snake River Plain Play Fairway Analysis (DE EE0006733) for Phase 2 (ArcGIS shapefiles and raster files) as well as the model processing script, tables, and documentation used to generate data outputs. Phase 2 examines two subset areas of the Phase 1 study area, Mountain Home and Camas Prairie. Brief descriptions of data layers are in the metadata of GIS files, greater detail is available in the ‘Larger Work,' the Snake River Plain Play Fairway Analysis Phase 2 report. A link to the report is available in the ‘Related External Resources’ section.

This teaching data subset contains1. a subset of spatial data (gis layers for the California Madera County and NEON SOAP and SJER sites). 2. Some other general spatial boundary layers from natural earth3. NEON lidar data and insitu measurements for SOAP and SJER sites. The data are used in both the Earth Analytics R and python courses. The Lidar data can be used to teach uncertainty given there are ground measurements available. We have recently added an additional vector layer so that cropping raster data can be taught using this data set as well.

This raster file represents land within the Mountain Home study boundary classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by use of Random Forest, a supervised machine learning algorithm. Classification models often employ Random Forest due to its accuracy and efficiency at labeling large spatial datasets. To build a Random Forest model and supervise the learning process, IDWR staff create pre-labeled data, or training points, which are used by the algorithm to construct decision trees that will be later used on unseen data. Model accuracy is determined using a subset of the training points, otherwise known as a validation dataset. Several satellite-based input datasets are made available to the Random Forest model, which aid in distinguishing characteristics of irrigated lands. These characteristics allow patterns to be established by the model, e.g., high NDVI during summer months for cultivated crops, or consistently low ET for dryland areas. Mountain Home Irrigated Lands 2023 employed the following input datasets: US Geological Survey (USGS) products, including Landsat 8/9 and 10-meter 3DEP DEM, and European Space Agency (ESA) Copernicus products, including Harmonized Sentinel-2 and Global 30m Height Above Nearest Drainage (HAND). For the creation of manually labeled training points, IDWR staff accessed the following datasets: NDVI derived from Landsat 8/9, Sentinel-2 CIR imagery, US Department of Agriculture National Agricultural Statistics Service (USDA NASS) Cropland Data Layer, Active Water Rights Place of Use data from IDWR, and USDA’s National Agriculture Imagery Program (NAIP) imagery. All datasets were available for the current year of interest (2023). The published Mountain Home Irrigated Lands 2023 land classification raster was generated after four model runs, where at each iteration, IDWR staff added or removed training points to help improve results. Early model runs showed poor results in riparian areas near the Snake River, concentrated animal feeding operations (CAFOs), and non-irrigated areas at higher elevations. These issues were resolved after several model runs in combination with post-processing masks. Masks used include Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data. These data were amended to exclude polygons overlying irrigated areas, and to expand riparian area in specific locations. A manually created mask was primarily used to fill in areas around the Snake River that the model did not uniformly designate as irrigated. Ground-truthing and a thorough review of IDWR’s water rights database provided further insight for class assignments near the town of Mayfield. Lastly, the Majority Filter tool in ArcGIS was applied using a kernel of 8 nearest neighbors to smooth out “speckling” within irrigated fields. The masking datasets and the final iteration of training points are available on request. Information regarding Sentinel and Landsat imagery:All satellite data products used within the Random Forest model were accessed via the Google Earth Engine API. To find more information on Sentinel data used, query the Earth Engine Data Catalog https://developers.google.com/earth-engine/datasets) using “COPERNICUS/S2_SR_HARMONIZED.” Information on Landsat datasets used can be found by querying “LANDSAT/LC08/C02/T1_L2” (for Landsat 8) and “LANDSAT/LC09/C02/T1_L2” (for Landsat 9).Each satellite product has several bands of available data. For our purposes, shortwave infrared 2 (SWIR2), blue, Normalized Difference Vegetation Index (NDVI), and near infrared (NIR) were extracted from both Sentinel and Landsat images. These images were later interpolated to the following dates: 2023-04-15, 2023-05-15, 2023-06-14, 2023-07-14, 2023-08-13, 2023-09-12. Interpolated values were taken from up to 45 days before and after each interpolated date. April-June interpolated Landsat images, as well as the April interpolated Sentinel image, were not used in the model given the extent of cloud cover overlying irrigated area. For more information on the pre-processing of satellite data used in the Random Forest model, please reach out to IDWR at gisinfo@idwr.idaho.gov.

This is a portion of the data used to calculate 2008 and 2013 cumulative human impacts in: Halpern et al. 2015. Spatial and temporal changes in cumulative human impacts on the world's ocean. Seven data packages are available for this project: (1) supplementary data (habitat data and other files); (2) raw stressor data (2008 and 2013); (3) stressor data rescaled by one time period (2008 and 2013, scaled from 0-1); (4) stressor data rescaled by two time periods (2008 and 2013, scaled from 0-1); (5) pressure and cumulative impacts data (2013, all pressures); (6) pressure and cumulative impacts data (2008 and 2013, subset of pressures updated for both time periods); (7) change in pressures and cumulative impact (2008 to 2013). All raster files are .tif format and coordinate reference system is mollweide wgs84. Here is an overview of the calculations: Raw stressor data -> rescaled stressor data (values between 0-1) -> pressure data (stressor data after adjusting for habitat/pressure vulnerability) -> cumulative impact (sum of pressure data) -> difference between 2008 and 2013 pressure and cumulative impact data. This data package includes 2008 and 2013 raw stressor data. The 2008 data includes 18 raster files (preceeded by raw_2008_). The 2013 data includes 19 raster files (preceeded by raw_2013_). There is no sea level rise data for 2008.

This web map shows the Digital Terrain Model (DTM) of Hong Kong. It shows the topography of terrain (including non-ground information such as elevated roads and bridges) in 5-metre raster grid with an accuracy of ±5m. It is a subset of open data made available by the Survey and Mapping Office, Lands Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in ArcInfo ASCII Grid format and processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort. For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

Portneuf Irrigated Lands 2023 was created for use in water budget studies in the Portneuf area. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Portneuf Irrigated Lands 2023 used the following as input features: • Interpolated Landsat 8 and Sentinel-2 surface reflectance imagery (bands: SWIR 2, Blue, and calculated NDVI)• 10-meter digital elevation model (including slope and aspect)• Height Above Nearest Drainage (HAND)• OpenET Ensemble monthly evapotranspirationFor additional information on the interpolation process for Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), IDWR-provided Active Water Rights Place of Use, the Cropland Data Layer for 2023, and the National Agriculture Imagery Program (NAIP) imagery for Idaho 2023.The accuracy of the Portneuf Irrigated Lands 2023 dataset was verified by several methods. Firstly, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassed. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclass in the dataset. For Portneuf Irrigated Lands 2023, final review and correction of the dataset was aided by comparing with Portneuf Irrigated Lands 2016 and 2021, which were created in tandem. By comparing all three years together, patterns of irrigation and misclass were more easily identified.Consistent areas of misclass for Portneuf Irrigated Lands 2023 include the Marsh Valley/Marsh Creek area, fields south of Chesterfield, and a handful of fields in the Bancroft/Lund area that appear to be dryland farmed. Misclass largely occurred in areas with active water rights, but no visible irrigation or irrigation infrastructure in the satellite and aerial imagery available. Final decisions made for these areas were conservative, relying heavily on both an active water right and clear indication of diverted water and artificial application of water to a given field. A less-conservative version was created for Portneuf 2023 and is available on request.

The 5m DEM is derived from the LiDAR2019B dataset (consisting of the 2018, 2019A and 2019B datasets). The 5m DEM has a vertical accuracy of 30cm. The height reference used is the SA Land Levelling Datum and the SAGEOID2010 was employed.The City of Cape Town Ground Level Map 2019 is defined in the City of Cape Town Municipal Planning Amendment By-law, 2019 as: “‘City of Cape Town Ground Level Map’ means a map approved in terms of the development management scheme, indicating the existing ground level based on floating point raster’s and a contour dataset from LiDAR information available to the City”. The Ground Level Map was approved by the City Council on the 27th July 2023.All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&For a copy or subset of this dataset, please contact the City Maps Office: city.maps@capetown.gov.zaCCT Ground Level Map: ‘How to Access’ Guide – External Users: CCT Ground Level Map: ‘How to Access’ Guide – External Users | Open Data Portal (arcgis.com)Geomatics Ground Level Map Explainer: Geomatics Ground Level Map Explainer | Open Data Portal (arcgis.com)Land Use Management Ground Level Map Explainer: Land Use Management Ground Level Map Explainer | Open Data Portal (arcgis.com)

IMPERVIOUS_SURFACE_CHANGE_2001_2006_USGS_IN is a raster layer (30-meter cell size) containing the percent difference of impervious-surface values in Indiana that changed between NLCD 2001 Percent Developed Imperviousness Version 2.0 and NLCD 2006 Percent Developed Imperviousness. This raster layer is a subset of the National Land Cover Database (NLCD 2006) suite of data products.The following is excerpted from metadata provided by the USGS for the NLCD 2006:"The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). Previously, NLCD consisted of three major data releases based on a 10-year cycle. These include a circa 1992 conterminous U.S. land cover dataset with one thematic layer (NLCD 1992), a circa 2001 50-state/Puerto Rico updated U.S. land cover database (NLCD 2001) with three layers including thematic land cover, percent imperviousness, and percent tree canopy, and a 1992/2001 Land Cover Change Retrofit Product. With these national data layers, there is often a 5-year time lag between the image capture date and product release. In some areas, the land cover can undergo significant change during production time, resulting in products that may be perpetually out of date. To address these issues, this circa 2006 NLCD land cover product (NLCD 2006) was conceived to meet user community needs for more frequent land cover monitoring (moving to a 5-year cycle) and to reduce the production time between image capture and product release. NLCD 2006 is designed to provide the user both updated land cover data and additional information that can be used to identify the pattern, nature, and magnitude of changes occurring between 2006 for the conterminous United States at medium spatial resolution.For NLCD 2006, there are 3 primary data products: 1) NLCD 2006 Land Cover map; 2) NLCD 2001/2006 Change Pixels labeled with the 2006 land cover class; and 3) NLCD 2006 Percent Developed Imperviousness. Four additional data products were developed to provide supporting documentation and to provide information for land cover change analysis tasks: 4) NLCD 2001/2006 Percent Developed Imperviousness Change; 5) NLCD 2001/2006 Maximum Potential Change derived from the raw spectral change analysis; 6) NLCD 2001/2006 From-To Change pixels; and 7) NLCD 2006 Path/Row Index vector file showing the footprint of Landsat scene pairs used to derive 2001/2006 spectral change with change pair acquisition dates and scene identification numbers included in the attribute table.In addition to the 2006 data products listed in the paragraph above, two of the original release NLCD 2001 data products have been revised and reissued. Generation of NLCD 2006 data products helped to identify some update issues in the NLCD 2001 land cover and percent developed imperviousness data products. These issues were evaluated and corrected, necessitating a reissue of NLCD 2001 data products (NLCD 2001 Version 2.0) as part of the NLCD 2006 release. A majority of NLCD 2001 updates occur in coastal mapping zones where NLCD 2001 was published prior to the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD 2001 land cover for all coastal zones. NLCD 2001 percent developed imperviousness was also updated as part of this process.As part of the NLCD 2011 project, NLCD 2006 data products have been revised and reissued (2011 Edition) to provide full compatibility with all other NLCD 2011 Edition products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes.Land cover maps, derivatives and all associated documents are considered "provisional" until a formal accuracy assessment can be conducted. The NLCD 2006 is created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2006 land cover product can be directed to the NLCD 2006 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov."

ESPA Irrigated Lands 2023 was created for use in water budget studies within the ESPA study boundary. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. ESPA Irrigated Lands 2023 used the following as input features: • Harmonized Landsat 8 and 9 OLI and Sentinel-2A and -2B satellites (HLS-2 Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30 m [2], HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30 m [3]; bands: SWIR-2, NIR, Blue, and calculated NDVI)• 10-meter digital elevation model 4• Height Above Nearest Drainage 5• OpenET Ensemble monthly evapotranspiration [6]• PRISM Climate Dataset 7• Topographic Wetness Index, derived from the digital elevation model4For additional information on processing Landsat and Sentinel-2 surface reflectance imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) [8], IDWR-provided Active Water Rights Place of Use, the Cropland Data Layer [9] for 2023, and the National Agriculture Imagery Program (NAIP) imagery [10] for Idaho 2023.The accuracy of the ESPA Irrigated Lands 2023 dataset was verified by several methods. Firstly, a validation test was conducted by withholding a subset of the training data to evaluate how well the model classified unseen information. Second, GIS staff ran several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration was determined as ‘final’, a manual mask was created to correct any remaining misclassification in the dataset.Manual corrections for the ESPA Irrigated Lands 2023 dataset were focused on the area between Ashton and Lamont, where false positive labels of “irrigated” occurred on dryland-managed fields. Some areas classified as irrigated near Bellevue were masked out due to suspected wetland. A general wetland mask for the entire ESPA study boundary was also applied. Other manual corrections were made throughout the study area, specifically for pivot-irrigated fields not matching the NAIP field boundaries. Decisions made during manual masking were conservative, relying heavily on both the presence of an active water right and clear indications of artificial application of water as observed in satellite imagery.References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002[3] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002[4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[5] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.[6] https://developers.google.com/earth-engine/datasets/catalog/OpenET_ENSEMBLE_CONUS_GRIDMET_MONTHLY_v2_0[7] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. doi:10.1002/joc.1688[8] https://data-idwr.hub.arcgis.com/documents/4defd5144b314fdcb010717cc6936648/about[9] https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL[10] https://developers.google.com/earth-engine/datasets/catalog/USDA_NAIP_DOQQ

Mountain Home Irrigated Lands 2004 was created for use in water budget studies in Mountain Home. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Mountain Home Irrigated Lands 2004 used the following as input features: • Landsat 5 [2] and Landsat 7 [3] averaged surface reflectance imagery (bands: SWIR 2, NIR, Blue, and calculated NDVI)• 10-meter digital elevation model 4• Height Above Nearest Drainage (HAND) [5]• PRISM Climate Dataset 6• Topographic Wetness Index, derived from the digital elevation model [4]For additional information on the interpolation process for Landsat imagery, please see below. Additional datasets used only for labeling training data include IDWR-provided Active Water Rights Place of Use and National Agriculture Imagery Program (NAIP) aerial imagery for 2004 [7].The accuracy of Mountain Home Irrigated Lands 2004 dataset was verified by several methods. Firstly, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclassification in the dataset. Misclassification within the Mountain Home Irrigated Lands 2004 dataset was minimal, occurring primarily in the southern areas near the Snake River, as well as around reservoirs and stream channels. GIS staff manually reviewed potential misclassifications by examining Landsat 5 and Landsat 7 imagery, NAIP aerial imagery, and IDWR Active Irrigation Water Rights. References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC05_C02_T1_L2[3] https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C02_T1_L2[4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[5] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.[6] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatologicaltemperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. doi:10.1002/joc.1688[7] U.S. Department of Agriculture, Farm Service Agency. (2004). National Agriculture Imagery Program (NAIP) imagery [Digital image]. U.S. Department of Agriculture. https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/Information interpolated imagery:GIS staff prepared averaged Landsat images to reduce missing data from cloud cover. Images were averaged across four periods: March 1–May 1, May 1–July 1, July 1–September 1, and September 1–November 1. These same periods were also used to average PRISM climate data. The temporal extent of other input features was filtered to March 1–November 30, 2004, where applicable.

Reason for Selection Protected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0 national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code. Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

Portneuf Irrigated Lands 2021 was created for use in water budget studies in the Portneuf area. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Portneuf Irrigated Lands 2021 used the following as input features: • Interpolated Landsat and Sentinel-2 surface reflectance imagery (bands: SWIR 2, Blue, calculated NDVI, and NIR)• 10-meter digital elevation model (including slope and aspect)• Height Above Nearest Drainage (HAND)• Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC)For additional information on the interpolation process for Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include IDWR-provided Active Water Rights Place of Use, the Cropland Data Layer for 2021, and the National Agriculture Imagery Program (NAIP) imagery for Idaho 2021.The accuracy of the Portneuf Irrigated Lands 2021 dataset was verified by several methods. Firstly, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassed. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclass in the dataset. For Portneuf Irrigated Lands 2021, final review and correction of the dataset was aided by comparing with Portneuf Irrigated Lands 2016 and 2023, which were created in tandem. By comparing all three years together, patterns of irrigation and misclass were more easily identified.Consistent areas of misclass for Portneuf Irrigated Lands 2021 include the Marsh Valley/Marsh Creek area, fields south of Chesterfield, and a handful of fields in the Bancroft/Lund area that appear to be dryland farmed. Misclass largely occurred in areas with active water rights, but no visible irrigation or irrigation infrastructure in the satellite and aerial imagery available. Final decisions made for these areas were conservative, relying heavily on both an active water right and clear indication of diverted water and artificial application of water to a given field. A less-conservative version was created for Portneuf 2021 and is available on request.

Eastern Snake River Plain Irrigated Lands 2018 was created for use in water budget studies in the Eastern Snake River Plain. The area of interest was determined by Hydrology Section staff at IDWR, and a study boundary was given to GIS staff and used to clip the model output. The random forest (RF) model is a type of supervised machine learning algorithm requiring GIS staff to provide manually labeled training data. GIS staff also provide the RF model with several input features, typically raster datasets that help distinguish characteristics of irrigated lands. Eastern Snake River Plain Irrigated Lands 2018 used the following as input features: • Seasonally averaged Landsat 8 [2] and Sentinel-2 surface reflectance imagery 3• 10-meter digital elevation model 4• PRISM [5] 800 meter seasonal averaged climate data• IDWR METRIC [7] evapotranspiration dataset• Height Above Nearest Drainage (HAND) [6] • Topographic Wetness Index, derived from the digital elevation modelFor additional information on the averaging process for Landsat and Sentinel-2 imagery, please see below. Additional datasets used only for labeling training data include Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) [7], IDWR-provided Active Water Rights Place of Use, and the Cropland Data Layer [8] for 2018.The accuracy of the Eastern Snake River Plain Irrigated Lands 2018 dataset was verified by several methods. Firstly, a validation test is done by withholding a subset of the training data to evaluate how well the model classifies unseen information. Second, GIS staff will run several iterations of the model with variations of training data, with the goal of improving classification for areas consistently misclassified. This process requires GIS staff knowledge, aided by supplementary datasets, to review the area and make decisions. Once a model iteration is determined as ‘final’, a manual mask is created to correct any remaining misclassification in the dataset. Consistent areas of misclassification for Eastern Snake River Plain Irrigated Lands 2018 include the area between Ashton and Lamont, pastures within Chester, dry and fallow fields in southern Twin Falls county, and separation between wetlands and irrigated fields in Wood River and the Lost River Valleys. Misclassification largely occurred in areas with active water rights, but no visible irrigation or irrigation infrastructure in the satellite and aerial imagery available. Final decisions made for these areas were conservative, relying heavily on both an active water right and clear indication of diverted pressurized water and purposeful application of water to a given field.References:[1] https://developers.google.com/earth-engine/apidocs/ee-classifier-smilerandomforest[2] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSL30_v002[3] https://developers.google.com/earth-engine/datasets/catalog/NASA_HLS_HLSS30_v002[4] https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m[5] Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H., Curtis, J. & Pasteris, P.A. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031-2064. doi:10.1002/joc.1688[6] Donchyts, G., Winsemius, H., Schellekens, J., Erickson, T., Gao, H., Savenije, H., & van de Giesen, N. (2016). Global 30m height above the nearest drainage (HAND). Geophysical Research Abstracts, 18, EGU2016-17445-3. EGU General Assembly 2016.[7] https://data-idwr.hub.arcgis.com/documents/776cfc545e0944fc89a75d4777031bb4/about[8] https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDLInformation on averaged imagery:GIS staff average Landsat and Sentinel-2 imagery in two month increments to fill gaps of missing data. Images are averaged using the HLSS and HLSL datasets for March 1st through May 1st, May 1st through July 1st, July 1st through September 1st, and September 1st through November 1st. Averaging results in 4 total images that are entirely spectral data.

This document contains brief definitions of the NatureServe terrestrial ecological systems currently identified as occurring in Washington. Excluding Aggregates. Terrestrial ecological systems concepts form the basis for three map products from the inter-agency Landfire effort. First, they define the map legend for mapping Existing Vegetation Type (EVT); i.e., the current location of vegetative components of each terrestrial ecological system are mapped in that layer. Second, Environmental Site Potential (ESP) is a spatial model of environments that constrain the possible locations where a given ecological system could occur, without including natural disturbance regime as a factor. Third, Biophysical Settings (BpS) provide another spatial model depicting the probable location of each ecological system type, assuming the inclusion of natural disturbance regimes as a factor.This ecological systems classification has been developed in consultation with many individuals and agencies and incorporates information from a variety of publications and other classifications. Most of the following types will be further described, quantitatively modeled, and mapped for LANDFIRE. Comments and suggestions regarding the contents of this subset may be directed to Mary J. Russo, Central Ecology Data Manager, Durham, NC,