Originators: US Environmental Protection Agency Publisher: US EPA Office of Research & Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL) Publication place: Corvallis, OR Publication date: Time Period of Data: 1900-2010; Projected data for 2041-2070. Data location: GeoPlatform ("https://www.geoplatform.gov/") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are not within two-standard deviations of the historical decadal average contribute to the vulnerability index for each metric. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially-uniform as temperature. The highest elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the West are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. Purpose: These data were created in support of the US EPA’s ACE CIVA 2.3, Task Project (QAPP: E-WED-0030854). However, these climate data and hydrologic landscape summaries should have broad applicability for hydrological, geomorphic, or ecological modeling, management, and restoration. This raster contains the modeled change in the seasonality of the month of maximum surplus precipitation between the target time period (in title) and the 1971-2000 Normal period (1 = same season, 0 = earlier season, 2 = later season).

The National Urban Change Indicator (NUCI) is a change indicator dataset covering the lower 48 United States that uses Maxar’s PCM®, imagery-derived change detection, to map persistent changes to the landscape resulting from urban development. The input data for the PCM process are a multi-temporal stack of precision, co-registered Landsat multispectral scenes. This NUCI 2016 database provides a history of change areas on an annual basis from 1987 through 2016.Co-Registered Geospatial DataIn addition to capturing the PCM-determined date of change, the NUCI 2016 dataset is attributed with data elements extracted from the following co-registered geospatial data sets:2011 National Land Cover Data (NLCD 2011) Land Cover: Each change polygon is attributed with NLCD 2011 land cover name and class number of the area covered by the polygon. If more than one land cover category is present, attributes are also provided for the secondary (by percentage pixel count) and tertiary classes. The percentage of polygon area for each class is also captured and provided.Urban Gravity: Each change polygon is attributed with an “Urban Gravity” value. The Urban Gravity is calculated by treating the Impervious Surface (percent impervious by pixel) data of the NLCD 2011 dataset as units of mass and then calculating a “gravitational pull” as the inverse square distance measure at the center of the change polygon. The higher the Urban Gravity value, the closer the polygon center is to existing concentrations of NLCD 2011 mapped impervious surface areas.Distance to Water: Distance, in meters, to the nearest water body as defined by the NLCD land cover dataset. Values greater than 2,000 meters are shown as “999999”.Shuttle Radar Topography Mission (SRTM)Height Variance: Each change polygon is attributed with a measure of the average elevation variance, in meters, across the polygon. The measure is calculated from the 3 arc-second SRTM digital elevation data using a standard variance filter over a 7x7 kernel.Additional NotesThis feature layer is intended to be used as input for spatial analysis tools and applications.For visualization purposes, it is best to use the NUCI tile layer.A NUCI 2016 change is defined as one which meets the “3-observation change (3oc)” criteria where the detected state of change has persisted for three independent date observations.This version of NUCI 2016 was filtered to focus on changes related to human activity in order to mute spurious changes and false positives.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

The National Urban Change Indicator (NUCI) is a change indicator dataset covering the lower 48 United States that uses Maxar’s PCM®, imagery-derived change detection, to map persistent changes to the landscape resulting from urban development. The input data for the PCM process are a multi-temporal stack of precision, co-registered Landsat multispectral scenes. This NUCI 2016 layer provides a history of change areas on an annual basis from 1987 through 2016Co-Registered Geospatial DataIn addition to capturing the PCM-determined date of change, the NUCI 2016 dataset is attributed with data elements extracted from the following co-registered geospatial data sets:2011 National Land Cover Data (NLCD 2011) Land Cover: Each change polygon is attributed with NLCD 2011 land cover name and class number of the area covered by the polygon. If more than one land cover category is present, attributes are also provided for the secondary (by percentage pixel count) and tertiary classes. The percentage of polygon area for each class is also captured and provided.Urban Gravity: Each change polygon is attributed with an “Urban Gravity” value. The Urban Gravity is calculated by treating the Impervious Surface (percent impervious by pixel) data of the NLCD 2011 dataset as units of mass and then calculating a “gravitational pull” as the inverse square distance measure at the center of the change polygon. The higher the Urban Gravity value, the closer the polygon center is to existing concentrations of NLCD 2011 mapped impervious surface areas.Distance to Water: Distance, in meters, to the nearest water body as defined by the NLCD land cover dataset. Values greater than 2,000 meters are shown as “999999”.Shuttle Radar Topography Mission (SRTM)Height Variance: Each change polygon is attributed with a measure of the average elevation variance, in meters, across the polygon. The measure is calculated from the 3 arc-second SRTM digital elevation data using a standard variance filter over a 7x7 kernel.Additional NotesThis tile layer is intended for visualization purposes. The NUCI feature layer can be used as input to spatial analysis tools and applications.A NUCI 2016 change is defined as one which meets the “3-observation change (3oc)” criteria where the detected state of change has persisted for three independent date observations.This version of NUCI 2016 was filtered to focus on changes related to human activity in order to mute spurious changes and false positives.

Originators: US Environmental Protection Agency Publisher: US EPA Office of Research & Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL) Publication place: Corvallis, OR Publication date: Time Period of Data: 1900-2010; Projected data for 2041-2070. Data location: GeoPlatform ("https://www.geoplatform.gov/") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are not within two-standard deviations of the historical decadal average contribute to the vulnerability index for each metric. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially-uniform as temperature. The highest elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the West are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. Purpose: These data were created in support of the US EPA’s ACE CIVA 2.3, Task Project (QAPP: E-WED-0030854). However, these climate data and hydrologic landscape summaries should have broad applicability for hydrological, geomorphic, or ecological modeling, management, and restoration. This raster contains the modeled change in the average Feddema Moisture Index over 2041-2070 (HadGEM2-AO r1i1p1) and the 1971-2000 Normal period.

This application compares changes between aggregated 2011 National Land Cover Database land cover categories with similarly aggregated land cover categories from The Clark Labs 2050 Conterminous US Land Cover Prediction. It also provides a few summary statistics about possible changes in developed, forest and agricultural land cover. Look for the soon to be released Clark Labs American Land Change Explorer application, which provides exhaustive analysis and summaries of potential transitions from each of the NLCD categories to each of the projected 2050 categories.The Clark Labs 2050 Conterminous US Land Cover Prediction© 2016 Clark LabsIntroductionThe Clark Labs’ conterminous US land cover prediction for 2050 was produced as part of the development of the Land Change Explorer – a web application to illustrate the potential of predictive land change modeling and to introduce potential users to the Land Change Modeler – a cloud-based software service for land change modeling to be offered in the ArcGIS Marketplace.ProcedureThe prediction is based on an empirical modeling of the relationship between land cover change from 2001 to 2011 and a series of explanatory variables. The land cover data were at a 30 meter resolution from the National Land Cover Database (NLCD). The explanatory variables(1) were:ElevationSlopeProximity to primary roadsProximity to secondary roadsProximity to local roadsProximity to high intensity developmentProximity to open waterProximity to cropland (used only for transitions to cropland)Protected areasCounty subdivisions or counties/incorporated places (depending on the state)(2)The modeling procedure used is a newly developed algorithm suitable for distributed computing in a cloud computing environment(3). Briefly, the procedure is based of kernel density estimation of the normalized likelihood of change associated with varying levels of each independent variable. These estimates are then aggregated by means of a locally-weighted average where the weights are based on the degree of conviction each variable has about the outcome at that specific pixel. Testing has shown it to be comparable in skill to a multi-layer perceptron neural network with the added advantages of rapid calculation and capability of being distributed across multiple computer nodes.Because the drivers of change can vary over space, modeling was done separately for each state. All transitions that met or exceed 2 km2 in area (at the state level) were modeled independently. Within a single state, as many as 128 individual transitions might occur. In total, over the 48 conterminous states, 3330 transitions were modeled. The modeling process initially establishes the potential to transition. This potential is expressed as a continuous value from 0 to 1 at each pixel for each transition. The procedure then uses the Markovian assumption that the rate of transition experienced in the historical period (2001-2011 in this case) will continue into the future. A competitive greedy selection process then allocates the projected change(4).ValidationIn the training process for each transition, 50% of historical instances of change and 50% of an equal-sized sample of pixels eligible to change, but which did not (e.g., persistence), were reserved for model validation. The median accuracy over all 3330 transitions was 80% with 79% of change validation pixels being correctly predicted and 83% of persistence pixels being correctly predicted. Thus the models, on average, are quite balanced in their ability to predict change and persistence.The accuracy associated with more specific transitions varied. A key objective was to be able to monitor and project anthropogenic changes and thus the explanatory variables chosen were focused on such drivers. Consequently, the median accuracy of natural to developed transitions (such as deciduous forest to low intensity development) was 92%. Again, accuracy was evenly balanced (93% for change / 91% for persistence).Accuracy for transitions between developed categories was lower at 77% (80% change and 75% persistence). A large part of this is because of the inconsistent manner in which roads are classified in the NLCD system. Roads are classified as one of the developed categories (high, medium, low and open development) based on the amount of impervious surface detected within pixels. Alignment of image pixels can cause this to vary resulting in roads that frequently switch classes between the years mapped.Natural transitions, such as forest to shrub, had the lowest overall accuracy at 74%. This was expected because many drivers cannot be predicted with the variables used. An example would be forest fires caused by lightning. This is also reflected in the fact that accuracy for predicting change was only 71% while that for predicting persistence was 78%.Finally, in states with significant cropland development, natural to cropland transitions were modeled with a 79% overall median accuracy. Accuracies for change and persistence were 78% and 81% respectively.DisclaimerNote that there are many highly plausible future outcomes and the specific scenario presented is only one of these (albeit judged to be the most plausible). Also note that each state is modeled separately (on the assumption that drivers of change many differ between states). As a consequence, there may be some mismatches at the boundaries between states. Generally, these are only evident for states that have large quantities of natural to natural transitions (e.g., with forest plantation crop cycles or frequent fire) where the accuracy is lower. Also note that the protected areas layer does not include all protected areas. Some local conservation land may be missing. Finally, note that the modeling is based on the assumption that rate of change experienced within the historical period (2001-2011) will persist into the future.1 Elevation data were from the National Elevation Database while slope was derived from those data. All roads data were acquired from the Census Bureau TIGER line files for 2014. Earlier road data would have been preferred, but errors in earlier TIGER line files were deemed to be unacceptable. Country subdivisions, counties and incorporated places were also acquired from the Census Bureau. Protected areas came from the Protected Areas Database of the USGS National Gap Analysis Program. All proximity layers were derived by Clark Labs.2 In some states, planning jurisdiction is controlled by county subdivisions (such as in New England), while in others, planning is governed by a combination of counties and incorporated places (such as in many of the western states).3 Eastman, J.R., Crema, S.C., and Rush, H.R., (forthcoming) A Weighted Normalized Likelihood Procedure for Empirical Land Change Modeling.4 Greedy selection assumes that the specific pixels that will change are those that are ranked the highest. Conflicts are resolved by assigning them to the transition with the highest marginal transition potential.

This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. This dataset displays shoreline change rates for north shore Long Island. Shoreline change rates are based on analysis of digital vector shorelines acquired from historical topographic sheets provided by National Oceanic and Atmospheric Administration (NOAA). Analysis was performed using the Digital Shoreline Analysis System (DSAS), an extension for ArcMap, created by the U.S. Geological Survey. Linear Regression Rates (LRR) and End Point Rates (EPR) of shoreline change were averaged along the shoreline of each salt marsh unit to generate this dataset. LRR rates were used in areas where three or more historical shorelines were available while EPR was used in areas where two were available. Positive and negative values indicate accretion and erosion respectively.

Originators: US Environmental Protection Agency Publisher: US EPA Office of Research & Development (ORD) - National Health and Environmental Effects Research Laboratory (NHEERL) Publication place: Corvallis, OR Publication date: Time Period of Data: 1900-2010; Projected data for 2041-2070. Data location: GeoPlatform ("https://www.geoplatform.gov/") and EPA Environmental Dataset Gateway (https://edg.epa.gov/). Abstract: We apply the hydrologic landscapes (HL) concept to assess the hydrologic vulnerability of the western United States (U.S.) to projected climate conditions. Our goal is to understand the potential impacts for stakeholder-defined interests across large geographic areas. The basic assumption of the HL approach is that catchments that share similar physical and climatic characteristics are expected to have similar hydrologic characteristics. We map climate vulnerability by integrating the HL approach into a retrospective analysis of historical data to assess variability in future climate projections and hydrology, which includes temperature, precipitation, potential evapotranspiration, snow accumulation, climatic moisture, surplus water, and seasonality of water surplus. Projections that are not within two-standard deviations of the historical decadal average contribute to the vulnerability index for each metric. The resulting vulnerability maps show that temperature and potential evapotranspiration are consistently projected to have high vulnerability indices for the western U.S. Precipitation vulnerability is not as spatially-uniform as temperature. The highest elevation areas with snow are projected to experience significant changes in snow accumulation. The seasonality vulnerability map shows that specific mountainous areas in the West are most prone to changes in seasonality, whereas many transitional terrains are moderately susceptible. This paper illustrates how the HL approach can help assess climatic and hydrologic vulnerability across large spatial scales. By combining the HL concept and climate vulnerability analyses, we provide a planning approach that could allow resource managers to consider how future climate conditions may impact important economic and conservation resources. Purpose: These data were created in support of the US EPA’s ACE CIVA 2.3, Task Project (QAPP: E-WED-0030854). However, these climate data and hydrologic landscape summaries should have broad applicability for hydrological, geomorphic, or ecological modeling, management, and restoration. This raster contains the modeled change in the seasonality of the month of maximum surplus precipitation between the target modeled time period (in title) and the 1971-2000 Normal period (1 = same season, 0 = earlier season, 2 = later season).

This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. This dataset displays shoreline change rates for Hudson Valley and New York City. Shoreline change rates are based on analysis of digital vector shorelines acquired from historical topographic sheets provided by National Oceanic and Atmospheric Administration (NOAA). Analysis was performed using the Digital Shoreline Analysis System (DSAS), an extension for ArcMap, created by the U.S. Geological Survey. Linear Regression Rates (LRR) and End Point Rates (EPR) of shoreline change were averaged along the shoreline of each salt marsh unit to generate this dataset. LRR rates were used in areas where three or more historical shorelines were available while EPR was used in areas where two were available. Positive and negative values indicate accretion and erosion respectively.