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.

Sieve filters are lacking in ArcGIS. Therefore, I developed a simple model that will perform a sieve filter based on the Jeffrey Evans' comments in the following forum:http://gis.stackexchange.com/questions/91609/where-can-i-use-a-sieve-filterThe basic idea of the sieve filter is that you can remove small "specks" or "polygons" from a categorical raster replacing them with their neighoring values. Unlike a focal majority operation which generalizes your data the sieve filter preserves the basic shapes of the "polygons". the only parameter required is the minimum number of cells in "polygon" (region group in raster terminology).Alternatively there may be some instances where you wish to generalize your data using a focal majority operation. However, the focal majority will return No Data in the case of a tie. Usually these are single cells or very small clusters of cells. The focal sieve tool allows you to remove these "specks" from your data. Hence, you get the generalization of the focal majority but use the sieve operation to clean up the specks. The focal sieve tool requires both a neighborhood size like a typical focal statistic but also a minimum number of cells.

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This is a view of the RoadsideSlopePoly featureclass created with the following query: WHERE LifeCycleCurrentStatus= 'Active' AND (WSDOTownership in ('Yes', 'Unknown') OR WSDOTownership IS NULL) This feature class contains WSDOT’s stormwater roadside slopes as polygons. The roadside slopes are that are documented by the Stormwater Features Inventory Group are limited to those that are designed and/or approved for use as a stormwater best management practice (BMP), such as vegetated filter strips. A roadside slope polygon documents the approximate boundaries of the area designated to act as a BMP.The majority of the data collected so far are within the Phase I and Phase II 2009 NPDES Municipal Stormwater Permit Areas.

The populations of many species of large mammals occur in small isolated and fragmented habitat patches in the human-dominated landscape. Maintenance of habitat connectivity in fragmented landscapes is important for maintaining a healthy population of large mammals. This study evaluated the landscape patches and their linkages on two carnivores (leopard and Himalayan black bear) and seven prey species (northern red muntjac, chital, sambar, wild pig, Himalayan goral, rhesus macaque, and langur) between Chitwan National Park (CNP) and Annapurna Conservation Area (ACA) by using the least-cost path approach and the Linkage Mapper tool in ArcGIS. A total of 15 habitat patches (average area 26.67 ± 12.70 km2) were identified that had more than 50% of the total studied mammals. A weak relation among the habitat patches was found for chital and sambar (Cost-weighted distance CWD: Euclidean distance EucD >100), showed poor connectivity between the habitat patches, while the ratio of CWD and EucD was low (i.e., low least-cost path) between the majority of the patches for muntjac, wild pig and leopard hence had potential functional connectivity along the landscape. Similarly, a low least cost path between the habitat patches located in the mid-hills was observed for Himalayan goral and Himalayan black bears. Furthermore, the multi-species connectivity analysis identified the potential structural connectivity between the isolated populations and habitat patches. Therefore, these sites need to be considered connectivity hotspots and be prioritized for the conservation of large mammals in the landscape. Methods Occurrence points collection The study blocks were divided into four distinct blocks, namely A, B, C, and D considering landscape features, the main courses of rivers, and topographical features (Figure 2C). Block A covers the BCF, part of CNP, and surrounding areas of BCF (Kabilas, Jugedi, Kerabari, Chaukidanda, Simaldhap) up to the Mahabharat range (it runs closely parallel to the Chure range and separates the Terai with the Hill region, i.e., mid-hill) of Chitwan district. Block B covers human-dominated mid-hill landscapes such as Devghat, Bandipur, Abu Khairani Rural Municipalities, and Vyas Municipality of Tanahun district. It follows the Seti and Trishuli River basins along with mid-hills. Block C covers the Bhimad Municipality, parts of Rishing Rural Municipality, Ghiring Rural Municipality, Magde Rural Municipality and Shuklagandaki Municipality of Tanahun District, and Rupa Rural Municipality of Kaski District along the Seti River basin. Block D covers Bharatpokhari, Nirmalpokhari, Pumdibhumdi, Panchase, Lumle, Ghandruk, Landruk, Deurali, and the Australian Camp area. This block harbors four types of forests: national forest, community forest, protected forest (Panchase), and conservation area (Annapurna). Transects were laid for the collection of presence points of selected species (nine species of mammals) in the landscape (Figure 1). The presence points of ungulates and primates were collected on the basis of direct sighting whereas, the presence points of the carnivores were collected on the basis of signs left by them (e.g., scats, scrapes, pugmarks, scent spray, etc.). Transect size and length were determined based on forest patch size. After identifying forest patches using a topo-base map (Esri, 2017), transects were overlaid, with patches selected based on diameter; patches less than 2 km in diameter were excluded. Transects (150 out of 164) were systematically laid out according to patch size and accessibility in four blocks (31 in A, 35 in B, 38 in C, and 46 in D). Inaccessible areas (14 transects) due to deep river gorges, steep mountains, and swampy lands were excluded. Transect lengths ranged from 1.18 to 7.84 km, with a minimum 500 m separation in regular forest patches, varying in scattered habitats like Mid Hills (Figure 2C, Table S1). We also collected the presence of those mammals opportunistically from other possible sites of the study area (e.g., croplands, river banks). These presence coordinates were recorded by using the Global Positioning System (GPS- Garmin eTrex 10). The collected occurrence data were spatially filtered in 30 m by using the Spatially Rarify Occurrence Data tools of SDMtoolbox 2.0.0 in ArcGIS (Brown, 2020; Kaboodvandpour et al., 2021). The filtered data were converted into .CSV format for Maxent modelling (Table 1). The large mammals whose presence locations were less than 25, were removed from further analysis. Environmental variables To minimize the risk of over-fitting the model and develop the most parsimonious model, the environmental variables were selected based on field knowledge, experts’ suggestions, and an extensive literature review of studied large mammals (Dickman & Marker, 2005; Mishra, 1982; Rather et al., 2020; Watts et al., 2019). The slope and terrain ruggedness index (TRI) were extracted by using the digital elevation model (DEM) in ArcGIS 10.8 (ESRI, 2019). The classified image from Landsat (acquisition date 2020-03-17) (Landsat 8, Operational Land Imager (OLI)) was used for calculating the Euclidian distances to the nearest forest, grassland, water sources, developed area or human settlements, and cropland. We classified the images into eight different classes (Water sources, barren areas, grassland, riverine forest, Sal-dominated forest, mixed forest, cropland, and developed area) by using supervised classification based on the ground-truthing points (Adhikari et al., 2022). Among these classified eight classes, we merged riverine forest, Sal-dominated forest, and mixed forest as a single forest layer. We extracted water sources, grassland, forest, cropland, and developed areas from the available data and calculated Euclidian distances in ArcGIS 10.8 to be used as environmental variables for modeling. The Normalized Difference Vegetation Index (NDVI) is the most popular and used to quantify the greenness of the vegetation, and vegetation density and detect the changes in plant health using red and near infra-red bands of a remotely sensed image (Pettorelli et al., 2011; USGS, 2022; Yengoh et al., 2015), hence we selected NDVI as one environmental layer for mammals. Additionally, the modified Normalized Difference Water Index (MNDWI) is calculated by using the green and Short-wave Infrared (SWIR) bands and it enhances the features of open water. MNDWI also minimizes the features of developed areas that are correlated with open water in other indices (Xu & Guo, 2014; Xu, 2006). Furthermore, the Normalized Difference Built-up Index (NDBI) is a ratio that minimizes the effects of terrain brightness differences and atmospheric effects (Zha et al., 2003). Two spectral bands NIR and SWIR are used to enhance the build-up or developed area, thus differentiating built-up over the natural area. The values of each environmental variable were extracted at presence locations (Table 1). For the layer of prey richness of leopard, the suitability map of preys was calibrated as 0 for absent and 1 for the present of the species based on mean equal test sensitivity and specificity logistic threshold. Then, these layers were combined as a single layer. A total of 13 environmental variables were used for the modelling (Table 2). The variables were differed on the basis of nature of the mammals (Table 2). The selected variable layers were converted into ASCII format with the same resolution, extent and projection system. The spatial resolution of 30 m and UTM 45 N projected coordinate system was used for the modelling. Habitat suitability models Maxent develops a model based on a series of features (environmental variables) (Phillips et al., 2006). Two types of data (occurrence data and environmental layers) were used for processing in the Maxent program (Phillips et al., 2006). The CSV file of the occurrence points in the samples menu and all selected variables layers in ASCII format in the environmental layers’ menu bar were loaded for analysis. The replicates and replicated run type were fixed 25 and subsample respectively. The Maxent model ran with 25 iterations and 1000 background points with 70 % of the points used as training data and 30 % points used as validation of the model. The output of the model was logistic. The performance of the model was evaluated based on AUC values of the receiver operator characteristic (ROC) plot analysis (Phillips, 2008; Phillips et al., 2006; Phillips & Dudík, 2008). The value of the predicted suitability ranges from 0 to 1. The logistic probability of suitability was further regrouped as 0 – 0.2 = unsuitable, 0.2 – 0.4 = moderately suitable, 0.4 – 0.6 = suitable, and 0.6 – 1 = highly suitable (Ansari & Ghoddousi, 2018; Kogo et al., 2019). All the spatial analysis and classification were performed in ArcGIS 10.8 (ESRI, 2019). We used these results of habitat suitability to identify the habitat patches of the species and preparation of resistance layer. Landscape resistance The resistance or cost map was prepared using a raster habitat suitability map (Figure S1). Every cell on the map has a numeric value that indicates the cost that should be paid to pass through each cell (Bagli et al., 2011; Morovati et al., 2020). The cost map was developed by inverting the value of habitat suitability using the following formula (Almasieh et al., 2019; Morovati et al., 2020). Cost = 100 × (1 - habitat suitability)
The lower cost is assigned to highly suitable areas whereas the highest cost is for the habitats with low suitability (Almasieh et al., 2019; Morovati et al., 2020). Identification of habitat patch The continuous probability of occurrence was converted to binary predictions of presence and absence based on average equal sensitivity and specificity threshold. The predicted maps of all species were combined to identify the species richness of an

This data set is part of an ongoing project to consolidate interagency fire point data. The incorporation of all available historical data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using top-of-atmosphere reflectance data from Landsat 5, Mapping Evapotranspiration with Internalized Calibration (METRIC) data, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 5, METRIC, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The National Land Cover Dataset (NLCD) from USGS, Bureau of Reclamation (BOR) Land Use and Land Cover data, as well as Digital Ortho Photo Quadrangle (DOQQ) data were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. “Speckling”, or small areas of incorrectly classified pixels, in the mountain areas was reduced by masking all pixels with a slope value of 15% or greater as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. Speckling within irrigated areas was reduced by a majority filter smoothing technique using a kernel of 8 nearest neighbors.

The "Restoration Projects" feature layer is a component of the "Pollinator Restoration 2022" map which is itself a component of the "USFWS Pollinator Restoration Projects Mapper" which is a dashboard showing management projects that benefit pollinators across the Western U.S. See below for a description of the "USFWS Pollinator Restoration Projects Mapper."The "USFWS Pollinator Restoration Projects Mapper" is under development by the Region 1 (Pacific Northwest) USFWS Science Applications program. Completion is anticipated by Winter 2023. Contact: Alan Yanahan (alan_yanahan@fws.gov).The purpose of the "USFWS Pollinator Restoration Projects Mapper" is to inform future pollinator conservation efforts by providing a way to identify geographic areas where additional pollinator conservation may be needed.The "USFWS Pollinator Restoration Projects Mapper" maps the locations of where on-the-ground projects that are beneficial to pollinators have taken place. Its primary focus is projects on public lands. The majority of records included in this tool come from internal databases for the USFWS, US Forest Service, and the Bureau of Land Management, which were queried for relevant projects. The tool is not intended as a database for reporting projects to. Rather, the tool synthesizes records from existing databases.The geographic scope of the tool includes the western states of Arizona, California, Idaho, Nevada, Oregon, Utah, and Washington.When possible, the tool includes projects from 2014 to the present. This timespan was chosen because it matches the timespan of the USFWS Monarch Conservation Database For consistency, the tool groups pollinator beneficial projects into the following four activity types:Restoration: Actions taken after a disturbance, such as planting native forbs after a wildfireMaintenance: Actions taken outside the growing season that maintain habitat quality through regular disturbance using manual or chemical means. Examples: mowing, spraying weeds, prescribed fireConservation: Acquiring land or creating easements that are managed for biodiversityEnhancement: Actions that increase forb diversity and nectar resources, such as planting native milkweedThe tool includes a map that aggregates project point locations within 49 square mile sized hexagon grid cells. Users can click on individual grid cells to activate a pop-up menu to cycle through the projects that occurred within that grid cell. Information for each project include, but are not limited to, acreage, type of activity (i.e., restoration, maintenance, conservation, enhancement), data source, and lead organization.The tool also includes a dashboard to view bar graphs and pie charts that display project acreages and project number based on location (i.e., state), project activity type (i.e., restoration, maintenance, conservation, enhancement), data source, and management type. Data can be filtered by data source, activity type, and year. Data filtering will update the map, bar graphs, and pie charts.

This raster file represents land within the Grandview-Bruneau Water Budget Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using top-of-atmosphere reflectance data from Landsat 5 and Landsat 7, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 5, Landsat 7, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The Cropland Data Layer (CDL) from the United States Department of Agriculture National Agricultural Statistics Service (USDA NASS), National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), and Mapping Evapotranspiration with Internalized Calibration (METRIC) data (where available) were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. Areas identified by the United States Fish and Wildlife Service National Wetlands Inventory as freshwater forested/shrub wetland areas, freshwater emergent wetland areas, and lake areas were masked as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. “Speckling”, or small areas of incorrectly classified pixels, was reduced by a majority filter smoothing technique using a kernel of 8 nearest neighbors.

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (8 adult females) from the Salt Springs herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fixes were between 11-14 hours. The Salt Springs herd migrates from winter ranges in the western foothills of the Sierra Nevada range to high altitude terrain near Lower Bear River Reservoir and Salt Springs Reservoir. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 6 migrating deer, including 13 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 18.53 days and 27.44 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 6 individual deer and 8 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

This project was initiated by the CDFW Central Region and was conducted on a portion of the Tuolumne herd that migrate to the Jawbone Ridge flats in the winter in Tuolumne County, Mariposa County, and Alpine County. Jawbone Ridge and the adjacent winter range habitat was further divided into the Clavey and Cherry sub-herd units. Additionally, a small sample of deer were captured from the Yosemite herd (south of the Tuolumne herd) to determine herd overlap. The raw dataset consisted of GPS way points collected from Advanced Telemetry Solutions (ATS) store on board GPS collars (G2110B/D model) and were placed on female mule deer only. Individuals were captured via darting or clover traps. This data was collected from 2009-2015 by Nathan Graveline and Ronald Anderson. GPS collars were set to take a location every 7 hours, and emit a signal Monday through Friday, 9am to 5pm. Some GPS collars were set to take a location fix every hour during periods of time when deer were thought to be migrating (May and November). The Clavey and Cherry sub-herd units support the highest concentration of wintering deer within the Tuolumne deer herd range. The majority of deer in these two sub-herds migrate east into the Emigrant and Yosemite Wilderness, with a few heading north to the Carson-Iceberg Wilderness. Low density populations of non-migratory deer are present in the winter range. Forest practices, wildfires, and recreation (hunting, camping, OHV) represent the most significant impacts to this herd. To improve the quality of the data set as per Bjørneraas et al. (2010), theGPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors in a single deer population. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 83 deer, including location, date, time, and average location error as inputs in Migration Mapper. 245 migration sequences were used in the modeling analysis. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates, separate models using Brownian bridge movement models (BMMM) and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (25% of sequences selected with BMMM). Migration corridors, stopovers, and winter range analyses were produced separately for the Yosemite Herd sample (n = 6) and merged with the Tuolumne dataset given the smaller capture effort and intention to prioritize moderate and high use corridors specifically in the Tuolumne herd. Winter range analyses were based on data from 85 individual deer in total. A separate BBMM was created for all deer locations designated as winter range using a fixed motion variance parameter of 1000. Winter range designations for this herd would likely expand with a larger sample south of Jawbone Ridge (Yosemite Herd) due to a small capture sample size from this area, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell in the BBMMs, with greater than or equal to 1 deer, greater than or equal to 9 deer (10% of the sample), and greater than or equal to 17 deer (20% of the sample) representing migration corridors, moderate use, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (6 adult females) from the Grizzly Flat herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2021. GPS fixes were between 11-13 hours. The Grizzly Flat herd migrates from winter ranges in the western foothills of the Sierra Nevada range near the Grizzly Flats eastward to higher altitude terrain in El Dorado national Forest, staying south of Interstate 50. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 5 migrating deer, including 16 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 11.63 days and 43.10 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 5 individual deer and 8 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

These points represent the individual trees that are managed by Three Rivers Park District. The majority of these trees are shade trees in picnic grounds, campgrounds, around buildings and in other maintained areas. The status of the trees is indicated by the "status" field. This indicates if a tree is planted, is in the process of being planted (tree spade), or if it has been removed. To visualize only the trees that are currently growing in the landscape, users must filter by "planted".This data set was first collected in 2010. The data are updated periodically, but some trees may have been removed and are still shown as "planted"

The "CountiesStatesInfo" feature layer is a component of the "Pollinator Restoration 2022" map which is itself a component of the "USFWS Pollinator Restoration Projects Mapper" which is a dashboard showing management projects that benefit pollinators across the Western U.S. See below for a description of the "USFWS Pollinator Restoration Projects Mapper."The "USFWS Pollinator Restoration Projects Mapper" is under development by the Region 1 (Pacific Northwest) USFWS Science Applications program. Completion is anticipated by Winter 2023. Contact: Alan Yanahan (alan_yanahan@fws.gov).The purpose of the "USFWS Pollinator Restoration Projects Mapper" is to inform future pollinator conservation efforts by providing a way to identify geographic areas where additional pollinator conservation may be needed.The "USFWS Pollinator Restoration Projects Mapper" maps the locations of where on-the-ground projects that are beneficial to pollinators have taken place. Its primary focus is projects on public lands. The majority of records included in this tool come from internal databases for the USFWS, US Forest Service, and the Bureau of Land Management, which were queried for relevant projects. The tool is not intended as a database for reporting projects to. Rather, the tool synthesizes records from existing databases.The geographic scope of the tool includes the western states of Arizona, California, Idaho, Nevada, Oregon, Utah, and Washington.When possible, the tool includes projects from 2014 to the present. This timespan was chosen because it matches the timespan of the USFWS Monarch Conservation Database For consistency, the tool groups pollinator beneficial projects into the following four activity types:Restoration: Actions taken after a disturbance, such as planting native forbs after a wildfireMaintenance: Actions taken outside the growing season that maintain habitat quality through regular disturbance using manual or chemical means. Examples: mowing, spraying weeds, prescribed fireConservation: Acquiring land or creating easements that are managed for biodiversityEnhancement: Actions that increase forb diversity and nectar resources, such as planting native milkweedThe tool includes a map that aggregates project point locations within 49 square mile sized hexagon grid cells. Users can click on individual grid cells to activate a pop-up menu to cycle through the projects that occurred within that grid cell. Information for each project include, but are not limited to, acreage, type of activity (i.e., restoration, maintenance, conservation, enhancement), data source, and lead organization.The tool also includes a dashboard to view bar graphs and pie charts that display project acreages and project number based on location (i.e., state), project activity type (i.e., restoration, maintenance, conservation, enhancement), data source, and management type. Data can be filtered by data source, activity type, and year. Data filtering will update the map, bar graphs, and pie charts.

The project leads for the collection of this data were Sara Holm and Julie Garcia. Mule deer (4 adult females) from the Bucks Mountain-Mooretown herd were captured and equipped with Lotek Iridium Track MGPS collars, transmitting data from 2018-2020. GPS fixes were between 12-13 hours. The Bucks Mountain-Mooretown herd migrates from winter ranges in the American Valley, Crescent Hill, and various high elevation locations of the Sierra Nevada range east and south to even higher altitude terrain in Plumas National Forest. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 4 migrating deer, including 11 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 13.09 days and 27.77 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to the majority of BBMMs producing variance rates greater than 8000, a fixed motion variance of 1000 was set per migration sequence. Winter range analyses were based on data from 4 individual deer and 6 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. This collar project was not specifically designed to pinpoint precise migration routes or winter range designations, hence the low sample size. Additional migration routes and winter range areas likely exist beyond what was modeled in our output.Corridor tiers (low, medium, high) could not be computed with such a small dataset. Therefore, all corridors were given the same weight and designation in this analysis. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

APD CAD 911 Calls 2010APD CAD 911 Calls-- This data is for the year 2010 and is a static dataset. Addresses are delivered in block format, resolving to the nearest 100 (e.g. 1-199 becomes 100-BLK; 200-299 becomes 200-BLK; etc.). APD Filters:The dataset includes the following agency-specific filters: Asheville Police Department (APD): APD is the assigned agency to the call. Use block-addressing The majority of the sensitive information on a CAD call is in the notes field. No information from the notes field is included in this view.Metadata: https://docs.google.com/document/d/143a0LoGwNwmmHxJu1msxjOFAfAXPk7otQSWkrLtUDk0/edit?usp=sharing

APD CAD 911 Calls 2023, updated monthly

APD CAD 911 Calls-- This data is updated monthly, with a 90 day hold. (ex. updated Aug 1, 2023, would show Jan 1 through May 31, 2023) Addresses are delivered in block format, resolving to the nearest 100 (e.g. 1-199 becomes 100-BLK; 200-299 becomes 200-BLK; etc.). APD Filters: The dataset includes the following agency-specific filters: Asheville Police Department (APD): APD is the assigned agency to the call. Use block-addressing The majority of the sensitive information on a CAD call is in the notes field. No information from the notes field is included in this view.

Metadata: https://docs.google.com/document/d/143a0LoGwNwmmHxJu1msxjOFAfAXPk7otQSWkrLtUDk0/edit?usp=sharing

APD CAD 911 Calls for 2020 APD CAD 911 Calls-- This data is static. Addresses are delivered in block format, resolving to the nearest 100 (e.g. 1-199 becomes 100-BLK; 200-299 becomes 200-BLK; etc.). APD Filters:The dataset includes the following agency-specific filters: Asheville Police Department (APD): APD is the assigned agency to the call. Use block-addressing The majority of the sensitive information on a CAD call is in the notes field. No information from the notes field is included in this view.Metadata: https://docs.google.com/document/d/143a0LoGwNwmmHxJu1msxjOFAfAXPk7otQSWkrLtUDk0/edit?usp=sharing