RS-FRIS is a remote-sensing based forest inventory for WA DNR State Trust lands. RS-FRIS predicts forest conditions using statistical models that relate field measurements to three-dimensional remotely-sensed data (DAP and LiDAR point clouds). Forest metrics are predicted at a scale of 1/10th acre and stored as rasters. The attributes of each RIU are calculated as the mean of the raster cell values that fall within each polygon. Note: origin year and age are exceptions, and are based on the median value.RS-FRIS 5.4 was constructed using remote-sensing data collected in 2021 and 2022. Version 5.4 incorporates depletions for selected completed harvest types through 2025-08-31.Last edit date: 2025-06-09 NameDescriptionUnitsRIU_IDUnique identifier for each inventory unit.n/aLAND_COV_CDLand cover code.n/aLAND_COV_NMLand cover name.n/aAGENumber of years since the stand was initiated; a composite of known dates (where recorded in inventory data) and predicted dates (where not recorded in historical inventory data). Calculated as CURRENT YEAR - ORIGIN_YEAR.yearsORIGIN_YEARYear at which a stand was re-initiated, a composite of known dates (where recorded in inventory data) and predicted dates (where not recorded in historical inventory data). Based on the median of raster cell values.yearBAPredicted basal area.square feet / acreBA_4Predicted basal area of trees > 4" DBH.square feet / acreBA_4_CONIFERPredicted basal area of trees > 4" DBH which are of a conifer species.square feet / acreBA_4_HWDPredicted basal area of trees > 4" DBH which are of a hardwood species.square feet / acreBA_6Predicted basal area of trees > 6" DBH.square feet / acreBA_T100Predicted basal area of the 100 largest trees per acre.square feet / acreBAP_HWDPredicted percent of trees which are of a hardwood species.percent (0-100)BFVOL_GROSSPredicted gross board-foot volume. Values do not account for defect deductions.board feet / acreBFVOL_NETPredicted net board-foot volume.board feet / acreBIOMASS_ALLPredicted above-ground biomass (live and dead).metric tonnes / acBIOMASS_LIVEPredicted above-ground biomass (live).metric tonnes / acCANOPY_LAYERSPredicted count of distinct canopy layers. Units are continuous despite measurements being ordinal.countCARBON_ALLPredicted above-ground carbon (live and dead).metric tonnes / acCARBON_LIVEPredicted above-ground carbon (live).metric tonnes / acCFVOL_DDWMPredicted cubic foot volume of down and dead woody materials.cubic feet / acreCFVOL_TOTALPredicted total cubic-foot volume. This value does not account for merchantability or defect.cubic feet / acreCLOSUREPredicted canopy closure.percent (0-100)COVERPredicted canopy cover.percent (0-100)HT_LOREYPredicted Lorey height. Lorey height is basal-area weighted mean height.feetHT_T40Predicted height of the 40 largest trees per acre.feetHT_T100Predicted mean height of the 100 largest trees per acre.feetHTMAXPredicted maximum tree height.feetQMDPredicted quadratic mean diameter.inchesQMD_6Predicted quadratic mean diameter for trees > 6" DBH.inchesQMD_T100Predicted quadratic mean diameter for top 100 trees per acre.inchesRDPredicted Curtis relative density (RD)unitlessRD_6Predicted Curtis relative density (RD) for trees > 6" DBHunitlessRD_SUMPredicted Curtis relative density (RD), summation methodunitlessSDI_SUMPredicted Reineke's Stand Density Index (SDI), summation methodtrees / acreSDI_SUM_4Predicted Reineke's Stand Density Index (SDI), summation method, for trees > 4" DBH.trees / acreSDI_DF_EModeled maximum stand density index, Douglas-fir, eastern WA. 10" qmd.trees / acreSDI_GF_EModeled maximum stand density index, Grand-fir, eastern WA. 10" qmd.trees / acreSDI_LP_EModeled maximum stand density index, Lodgepole pine, eastern WA. 10" qmd.trees / acreSDI_PP_EModeled maximum stand density index, Ponderosa pine, eastern WA. 10" qmd.trees / acreSDI_WL_EModeled maximum stand density index,Western larch, eastern WA. 10" qmd.trees / acreSDI_DF_WModeled maximum stand density index, Douglas-fir, western WA. 10" qmd.trees / acreSDI_WH_WModeled maximum stand density index, Western hemlock, western WA. 10" qmd.trees / acreSNAG_ACRE_15Predicted number of snags per acre > 15" DBH.count / acreSNAG_ACRE_20Predicted number of snags per acre > 20" DBH.count / acreSNAG_ACRE_21Predicted number of snags per acre > 21" DBH.count / acreSNAG_ACRE_30Predicted number of snags per acre > 30" DBH.count / acreSPECIES1Primary speciesn/aSPECIES2Secondary speciesn/aTREE_ACREPredicted number of trees per acre.count / acreTREE_ACRE_4Predicted number of trees per acre > 4" DBH.count / acreTREE_ACRE_4_CONIFERPredicted number of trees per acre > 4" DBH which are conifer.count / acreTREE_ACRE_6Predicted number of trees per acre > 6" DBH.count / acreTREE_ACRE_8Predicted number of trees per acre > 8" DBH.count / acreTREE_ACRE_11Predicted number of trees per acre > 11" DBH.count / acreTREE_ACRE_20Predicted number of trees per acre > 20" DBH.count / acreTREE_ACRE_21Predicted number of trees per acre > 21" DBH.count / acreTREE_ACRE_30Predicted number of trees per acre > 30" DBH.count / acreTREE_ACRE_31Predicted number of trees per acre > 31" DBH.count / acreRS_COVEREDDescription of the extent of RS-FRIS raster coverage within inventory unit (NONE, PARTIAL, or FULL).n/aRS_COVERED_PCTPercent (0 to 100) of the inventory unit with RS-FRIS raster coverage.percent (0-100)RS_FRIS_POLY_ACRESAcres of RS-FRIS polygon.acres

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

Cet ensemble de données fournit les limites des bassins versants dérivés du LiDAR pour toutes les îles Calvert et Hecate, en Colombie-Britannique. Les bassins versants ont été délimités à partir d'un modèle altimétrique numérique de 3 m. Pour chaque polygone de bassin versant, le jeu de données comprend un identificateur unique et des statistiques sommaires simples pour décrire la topographie et l'hydrologie. Polygones de bassin versant Cet ensemble de données a été produit à partir des résultats de la modélisation hydrologique « traditionnelle » menée à l'aide du MNT de terre nue complet topographiquement complet basé sur lidar de 2012 + 2014 avec une zone tampon de 10 m autour de la côte pour s'assurer que tous les bassins versants modélisés atteignent l'océan. Les bassins versants ont été délimités à l'aide de points d'coulée créés à l'intersection des cours d'eau modélisés et du littoral. Après la délimitation du bassin versant, ceux-ci ont été coupés sur le rivage de l'île.

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, patched with data from 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Notice: this is not the latest Heat Island Severity image service.This layer contains the relative heat severity for every pixel for every city in the United States, including Alaska, Hawaii, and Puerto Rico. Heat Severity is a reclassified version of Heat Anomalies raster which is also published on this site. This data is generated from 30-meter Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2023.To explore previous versions of the data, visit the links below:Heat Severity - USA 2022Heat Severity - USA 2021Heat Severity - USA 2020Heat Severity - USA 2019Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us at http://goto.arcgisonline.com/landscape7/World_Population_Density_Estimate_2016.This layer is a global estimate of human population density for 2016. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Integrity layer:This layer represents the composite count overlap of six polygon source data sets that consider ecosystem structure, function, and composition in order to estimate relative ecological integrity across the High Divide region. We define and estimate ecological integrity by assembling publicly available spatial data that describe “elements of composition, structure, function, and ecological processes” (after Parrish et al. 2003; Wurtzebach and Schultz 2016) as described below.Connectivity layer:From Belote et al. 2022, we used the middle tolerance scenario with a 150 m moving window and reclassified raster based on the mean value (.727). Everything above the mean was considered "suitable" connectivity. The layer was clipped to the analysis area and converted into a polygon. Dreiss et al. (2022) extracted raw data values on connectivity and climate flow for areas that were IDed as climate-informed corridors based on categorical connectivity and climate flow dataset (TNC 2020). The remaining values were rescaled to fall between 0 and 1. A second climate corridor dataset (Carroll et al. 2018) was similarly rescaled. These two datasets were combined and locations in the 80th percentile of the distribution of combined values were analyzed. Higher values in the dataset indicate more optimal climate corridors. From Dreiss et al. 2022, here we took the upper 66% of values from the climate-informed wildlife corridors, as the top 33% and 50% were both insufficient to show data in the region given the dataset's national scale. The layer was clipped to the analysis area and converted into a polygon.These two layers were combined using the Count Overlap tool.

From Belote et al. 2022, we used the middle tolerance scenario with a 150 m moving window and reclassified raster based on the mean value (.727). Everything above the mean was considered "suitable" connectivity. The layer was clipped to the analysis area and converted into a polygon. Dreiss et al. (2022) extracted raw data values on connectivity and climate flow for areas that were IDed as climate-informed corridors based on categorical connectivity and climate flow dataset (TNC 2020). The remaining values were rescaled to fall between 0 and 1. A second climate corridor dataset (Carroll et al. 2018) was similarly rescaled. These two datasets were combined and locations in the 80th percentile of the distribution of combined values were analyzed. Higher values in the dataset indicate more optimal climate corridors. From Dreiss et al. 2022, here we took the upper 66% of values from the climate-informed wildlife corridors, as the top 33% and 50% were both insufficient to show data in the region given the dataset's national scale. The layer was clipped to the analysis area and converted into a polygon.These two layers were combined using the Count Overlap tool.

Reason for SelectionThis index measures the condition of the nation’s estuaries following standard national methodologies and is synthesized by the U.S. Environmental Protection Agency (EPA) roughly every five years. Estuaries serve as important nursery habitat for wildlife, including many species of fish and shellfish eaten as seafood. They also improve water quality by filtering out sediments and pollutants, provide recreational opportunities, and support coastal economies (NOAA 2019). The estuaries and surrounding coastal habitats of the Southeast coast “serve as important stopovers for migratory birds along the Atlantic Flyway as well as nurseries for fish and other animals. The highly productive ecosystems of Southeast estuarine waters contribute to commercial and recreational fishing. The Southeast Coast provides a wealth of economic and ecosystem services that sustain local economies and quality of life. These services include storm-surge and sea-level protection, maritime transportation and trade, commercial and recreational fisheries, and tourism.”Input DataEnvironmental Protection Agency (EPA) National Coastal Condition Assessment (NCCA): 2010 data and 2015 data; download NCCA dataThe NCCA surveys the estuaries and nearshore waters of the Great Lakes using various indicators of ecological condition and human health risk. We use four ecological indicators (USEPA 2015, 2021): The water quality/eutrophication index - includes measures of phosphorus, nitrogen, water clarity, chlorophyll a, and dissolved oxygen The sediment quality index - includes sediment contaminant levels and sediment toxicity to live organismsThe benthic/biological condition index – measures the condition of the community of macroinvertebrates (worms, mollusks and crustaceans) living in the sediment, based on diversity, abundance and sensitivity to pollutionThe fish tissue contaminants/ecological fish tissue quality index - measures the concentrations of metals and organic contaminants in fish and estimates potential harm to the wildlife that eat themThis indicator is scored in the same way as EPA’s scoring: 1 = poor, 3 = fair, and 5 = good. The 2015 assessment reported no strong regional trends in coastal condition between 2010 and 2015, so we combined both assessments to increase the density of the point data used to create the interpolation. NOAA coastal relief model: Shallow water bathymetry derived by extracting the area with depths from 0-10 m; we converted this spatial extent to a shapefile and used it to constrain the interpolation of the mean estuarine condition indexSoutheast Blueprint 2023 extentMapping StepsThe 2010 and 2015 point data contain separate fields for the water quality index, the sediment quality index, the fish tissue index, and the benthic index. Assign numeric values based on EPA’s scoring system. If a point scores poor in these fields, assign it a value of 1. If a point scores fair in these fields, assign it a value of 3. If a point scores good in these fields, assign it a value of 5.Add and calculate a field to contain the average of the four indices for all sites sampled in either 2010 or 2015. Note: Sites are drawn randomly so are not usually sampled in both timesteps. Import the tabular data into ArcPro and join to the table of EPA sampling locations. Interpolate the mean EPA coastal condition index value using Spline with Barriers. To avoid interpolating estuarine scores over land areas, use as the barriers input the shallow water extent from the NOAA coastal relief model. If a shallow water bathymetry polygon does not contain an EPA sampling location, that polygon is classified as 0 in a later step because we don’t know anything about its condition. Note: This operation had to be accomplished in two steps (Gulf and Atlantic regions) because of file size limitations. Merge the Gulf and Atlantic outputs and clip the resulting raster to the 0-10 m shallow water layer from the NOAA coastal relief model. Reclassify the above raster into the 5 classes seen in the final indicator values below: 0.3087-2 = 1 (Note: 0.3087 is the minimum value over the entire raster)2-2.4 = 22.4-3.7 = 33.7-4 = 44-5 = 5Intersect the shallow water bathymetry polygon with the EPA sampling locations. If a shallow water extent polygon does not contain an EPA sampling location, use the Calculate Field function to classify that polygon as 0 because we don’t know anything about its condition. Assign a value of 1 to all other polygons that do contain EPA sampling locations. Convert the resulting vector layer to raster.Multiply this new raster by the mean coastal condition index raster to produce a result that shows the mean coastal condition index where that value is known, and 0 for shallow estuaries lacking an EPA condition score. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.As a final step, clip to the spatial extent of Southeast Blueprint 2023.Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator valuesIndicator values are assigned as follows:5 = Good4 = Good to fair3 = Fair2 = Fair to poor1 = Poor0 = Shallow estuary not assessed for conditionKnown IssuesThe CCA was designed to support regional interpretations of coastal condition, not site-level interpolation as used in this indicator. As a result, estimates for areas far from NCCA sampling sites are highly uncertain. This indicator uses only two snapshots in time in a highly dynamic system.There is typically a significant time lag between the year the data is collected, and the year the data is published (allowing time for analysis, quality assurance, compiling the report, etc.). As a result, even the most recent available NCCA data (collected in 2015) is somewhat outdated.There is an error in the interpolation off the coast of Pensacola, FL that creates an unusual spatial pattern in the indicator. However, this pattern is not reflected in the final Blueprint as the entire area scores highest priority.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedNational Oceanic and Atmospheric Administration (NOAA). Last updated February 1, 2019. Life in an estuary. [https://www.noaa.gov/education/resource-collections/marine-life/life-in-estuary]. U.S. Environmental Protection Agency (EPA). August 2021. National Coastal Condition Assessment: A Collaborative Survey of the Nation’s Estuaries and Great Lakes Nearshore Waters (National Coastal Condition Assessment 2015). EPA 841-R-21-001. [https://www.epa.gov/system/files/documents/2021-09/nccareport_final_2021-09-01.pdf]. U.S. Environmental Protection Agency (EPA). 2012. National Coastal Condition Report IV. Office of Research and Development/Office of Water. Washington D.C. EPA-842-R-10-003. [https://www.epa.gov/national-aquatic-resource-surveys/national-coastal-condition-reports]. U.S. Environmental Protection Agency. Office of Water and Office of Research and Development. (2015). National Coastal Condition Assessment 2010 (EPA 841-R-15-006). Washington, DC. December 2015. U.S. Environmental Protection Agency (EPA). Last updated June 3, 2022. Southeast Coast Estuaries: National Coastal Condition Assessment 2015 (Setting). [https://www.epa.gov/national-aquatic-resource-surveys/southeast-coast-estuaries-national-coastal-condition-assessment].

We used version 5 of Zonation for the entire Southeast Blueprint. We ran Zonation within 6 zones. Zonation ranks the pixels in each zone according to their indicator scores, using a modeling approach that tries to conserve high-value representations of all indicators collectively. Pixels that rank higher in Zonation become higher priority in the Blueprint. INLAND CONTINENTAL REMOVING RESERVOIRS Reasoning Though reservoirs are highly altered systems, they still have conservation value. Unfortunately, the current set of indicators used in the Blueprint do not do a good job of capturing important parts of reservoirs or distinguishing the relative value of different reservoirs. As a result, we remove reservoirs from the zones used to define the boundaries of each Zonation run so that they are not eligible to be prioritized in the Blueprint. However, the indicators do capture the value of areas surrounding reservoirs, and those areas are not removed. The areas around reservoirs are also where most conservation actions occur to improve reservoir condition. Input Data

USGS National Hydrography Database (High Resolution) in FileGDB 10.1 format (published 08-30-2021) - NHDWaterbody and NHDFlowlines, accessed 10-14-2021
National Inventory of Dams (NID), accessed 10-15-2021; download the data
2019 National Land Cover Database (NLCD) 
Floodplain Inundation Frequency Southeast version: available on request by emailing yvonne_allen@fws.gov
Base Blueprint 2022 extent

Mapping Steps

Make copies of the NHDWaterbody and NHDFlowlines layers for editing. 
Extract features identified as either “LakePond” or “Reservoir” from the NHDWaterbody layer. Most reservoirs in the Southeast region are coded as “LakePond” in this dataset. 
Make a geospatial layer of the National Inventory of Dams (NID) from the source .csv file.
Select NHD waterbodies that are within 200 m of NID locations.
Add to the selection all NHD waterbodies that are within 5 m of the selection generated in the previous step to ensure that all parts of a single waterbody are selected.
Select NHD flowlines that are within 50 m of NID locations.
Select NHD waterbodies that are within 50 m of the selection generated in the previous step. 
At this stage, we did some hand-editing to add in obvious large reservoirs (especially in Texas) that were omitted from the above selections because the NID did not capture the dam locations. We used Inundation Frequency and the 2019 NLCD to assist in this step. In addition, the NHD contains some misclassified reservoirs (e.g., reservoirs classified as swamp/marsh or stream/river) that we manually added in. Note: The NID is also missing many dam locations associated with small farm ponds, which are too numerous to add by hand.
Convert to raster using the ArcPy Polygon to Raster function and clip to the spatial extent of Base Blueprint 2022.
Create a mask to use for Zonation runs by creating a raster where everything in the SECAS extent is 1 except for the reservoirs, which are NoData. For rebalancing later, we also create a raster where everything in the SECAS extent is 1 and the reservoirs have a value of 0. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. ZONATION INPUTS To create the inputs for each inland continental Zonation run:

Clip the reservoirs mask defined above to the 4 inland continental zones. This effectively removes reservoirs from each zone so that reservoirs are not included in each Zonation run. This step is not required in the continental marine or the Caribbean areas because the reservoir layer does not extend there. 
Clip the inland continental indicators to the 4 inland continental zones.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. The zones with reservoirs masked out serve as the boundaries of each Zonation run. Each Zonation run in the inland continental geography includes:

All indicators that occur in a given zone, clipped to that zone
All subregions that occur in a given zone

ZONATION RUN SETTINGS Determining Indicator Weights We use Zonation to identify a network of priorities that includes the areas most important for all indicators collectively. Since the goal is to create a balanced portfolio where all indicators are represented, in a perfect world, we would weight each indicator equally. However, in some cases, we have to downweight indicators that would otherwise have an outsized impact on the Blueprint priorities.

The way Zonation ranks pixels on the landscape can be influenced by factors like an indicator's spatial rarity within a given zone and the distribution of high and low values (i.e., is the indicator’s distribution top-heavy or bottom-heavy). In addition, some indicators are based on coarse-scale data. Other indicators have a wide spread of high to low values, but the data provider only intends the highest values to be considered good candidates for conservation action. In these situations, we use weighting to limit the disproportionate influence of certain indicators.

We developed four standard indicator weighting rules to ensure that:

Coarse-scale indicators have less of an influence
Spatially limited (i.e., rare) indicators were not overprioritized
Subregional indicators that don’t cover most of their target ecosystem were not overprioritized
Indicators with limited overlap of below-average values in a given zone were not overprioritized 

In a handful of edge cases, we developed additional exceptions to those four rules. The section below describes the application of the standard rules and additional exceptions. Coarse-scale indicators Two indicators (amphibian and reptile areas and estuarine coastal condition) have a much coarser spatial scale than the other indicators in the Blueprint. Amphibian and reptile areas uses generalized, expert-defined polygons, while estuarine coastal condition interpolates data from a relatively small number of sampling points into broad condition scores. These datasets oversimplify more site-specific variation in ecosystem health and habitat value, so we reduce their weight by 0.5 across all zones to limit their influence on the Blueprint priorities. This allows other finer-scale indicators to play a stronger role in teasing out key places within broad areas of importance for these coarser indicators. Spatially limited indicators With equal weights, the entire area of many indicators that cover a limited part of a zone (e.g., South Atlantic beach birds, greenways and trails) is identified as a high priority. To address this issue, we weighted indicators based on the proportion of their total area with values ≥1 in a given zone.

For indicators where the proportion of ≥1 values was between 0.1 and 0.5, we set their weights equal to that proportion. For example, we set the imperiled aquatic species indicator weight to approximately 0.26 in the Coastal Zone because that was the proportion of indicator pixels analyzed by Zonation in that zone with a value ≥1. For indicators where the proportion of ≥1 values was ≤0.1, we set their weights equal to that proportion multiplied by 3. We found that indicators in this range began to be underprioritized when only using the unadjusted proportion.

For indicators where the proportion of ≥1 values was 0.5 and higher, we set their weight to 1.0 unless they were covered by the exceptions discussed below. Subregional indicators that don’t cover most of their target ecosystem Nine indicators use source datasets that only cover specific subregions and do not apply to the majority of their target ecosystem (Mississippi Alluvial Valley forest birds - protection, Mississippi Alluvial Valley forest birds - reforestation, West Coastal Plain and Ouachitas forested wetland birds, West Coastal Plain and Ouachitas open pine birds, West Gulf Coast mottled duck nesting, South Atlantic forest birds, South Atlantic beach birds, South Atlantic low-urban historic landscapes, South Atlantic maritime forest). For example, South Atlantic forest birds does not capture important forest bird habitat that occurs elsewhere in the Southeast. While these indicators help the Blueprint identify important areas within these subregions, they can also unfairly penalize areas that aren’t covered by the models that would otherwise score highly due to their habitat or ecosystem value. This can cause Zonation to overprioritize the subregions that happen to be covered by these indicators. To reduce these unintended negative impacts, we reduce the weight of these indicators by 0.5. Indicators with limited overlap of below-average values in a given zone In a few cases, an indicator that targets a specific ecosystem will occur primarily in one zone, but spill over the boundary slightly into a neighboring zone. When this handful of overlapping pixels scores relatively low for that indicator, Zonation will often overprioritize these low-scoring areas. This occurs because Zonation can only see the range of indicator values that occur in a given zone—so while they score low overall, they are the highest values in that zone, which makes these areas seem more important than they actually are. To address this, we multiply the weight of the four indicators that have a small amount of overlapping below-average values in a given zone by 0.1. This rule applies to East Coastal Plain open pine birds and resilient coastal sites in the Greater Appalachians, Gulf coral and hardbottom in the Central West, and imperiled aquatic species in the Marine. Other exceptions Landscape condition & intact habitat cores in the Arid West The Arid West Zone is composed of much drier ecosystems that are climatically distinct from the rest of the SECAS

This map features the World Population Density Estimate 2016 layer for the Caribbean region. The advantage population density affords over raw counts is the ability to compare levels of persons per square kilometer anywhere in the world. Esri calculated density by converting the the World Population Estimate 2016 layer to polygons, then added an attribute for geodesic area, which allowed density to be derived, and that was converted back to raster. A population density raster is better to use for mapping and visualization than a raster of raw population counts because raster cells are square and do not account for area. For instance, compare a cell with 185 people in northern Quito, Ecuador, on the equator to a cell with 185 people in Edmonton, Canada at 53.5 degrees north latitude. This is difficult because the area of the cell in Edmonton is only 35.5% of the area of a cell in Quito. The cell in Edmonton represents a density of 9,810 persons per square kilometer, while the cell in Quito only represents a density of 3,485 persons per square kilometer. Dataset SummaryEach cell in this layer has an integer value with the estimated number of people per square kilometer likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers: World Population Estimate 2016: this layer contains estimates of the count of people living within the the area represented by the cell. World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: https://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is primarily intended for cartography and visualization, but may also be useful for analysis, particularly for estimating where people living above specified densities. There are two processing templates defined for this layer: the default, "World Population Estimated 2016 Density Classes" uses a classification, described above, to show locations of levels of rural and urban populations, and should be used for cartography and visualization; and "None," which provides access to the unclassified density values, and should be used for analysis. The breaks for the classes are at the following levels of persons per square kilometer:100 - Rural (3.2% [0.7%] of all people live at this density or lower) 400 - Settled (13.3% [4.1%] of all people live at this density or lower)1,908 - Urban (59.4% [81.1%] of all people live at this density or higher)16,978 - Heavy Urban (13.0% [24.2%] of all people live at this density or higher)26,331 - Extreme Urban (7.8% [15.4%] of all people live at this density or higher) Values over 50,000 are likely to be erroneous due to spatial inaccuracies in source boundary dataNote the above class breaks were derived from Esri's 2015 estimate, which have been maintained for the sake of comparison. The 2015 percentages are in gray brackets []. The differences are mostly due to improvements in the model and source data. While improvements in the source data will continue, it is hoped the 2017 estimate will produce percentages that shift less.For analysis, Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the average, highest, or lowest density within those zones.

Average winter (December-February) minimum temperature for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.

Average summer (June-August) minimum temperature for the Salish Sea Bioregion. 1991-2020 average climate variables were statistically downscaled to 90 meter resolution using the standalone ClimateNA software based on elevations from the Salish Sea Atlas's Digital Elevation Model. The data were converted to raster format for analysis and clipped to the Salish Sea Atlas's bioregional boundary dataset. All processing and analysis was completed using the NAD 83 UTM Zone 10 N coordinate system.For visualization purposes, raster climate datasets were reclassified into discrete ranges of values, then converted to vector polygons. Simplified attributes were calculated for each polygon.Gridded raster versions of the data can be downloaded from the Salish Sea Atlas data portal.Data sources:Original climate records were downscaled using ClimateNA: Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720ClimateNA downscaled data are derived from gridded climate records from PRISM Climate Group, Oregon State University: Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., ... & Pasteris, P. P. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology: a Journal of the Royal Meteorological Society, 28(15), 2031-2064.Elevation data from the Salish Sea Atlas were used in downscaling temperature data.