The summaries are derived from the Ecopia vector land cover dataset (a non-public resource) and not from the Ecopia raster land cover dataset available on this open data site.Percentages for each land cover type were calculated as compared to Ecopia’s total delineated area per summary polygon:sum_area_land_cover_type / sum_area_all_Ecopia_delineated_polygons_

Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (Strix occidentalis occidentalis) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (Neotoma spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions. Methods See README DOCUMENT Naming conventions *RSF or prey refers to prey capture analysis *delivery in a file name refers to delivery rate analysis *repro in a filename means that file is for the delivery rate analysis

Setup *files with vegetation data should work with minimal alteration(will need to specify working directory) with associated R code for each analysis *Shapefiles were made in ArcGIS pro but they can be opened with any GIS software such as QGIS.

Locational data files

NOTE LOCATIONAL DATA IS SHIFTED AND ROTATED FROM THE ORIGINAL -due to the sensitive nature of this species. The locational_data includes: * All_2021_owls_shifted * Point file showing all GPS tag locations for prey capture analysis * Attributes include: * TERRITORY ID: Numerical identifier for each bird * Year: year GPS tag was recorded * Month: month GPS tag was recorded * Day: Day GPS tag was recorded * Hour: Hour GPS tag was recorded * Minute: minute GPS tag was recorded * All_linked_polygons_shifted * Polygon file showing capture polygons for prey capture analysis * Attributes include * Territory ID: numerical identifier for each bird * Polygon id: numerical identifier for each capture polygon for each bird * Shape area: area of each polygon * SBNF_camera_nests_shifted * Point file showing spotted owl nests for prey capture analysis * Attributes include * Territory id: numerical identifier for each bird * C95_KDE_2021_socal_shifted * Polygon file of owls 95% kernel density estimate for prey delivery rate analysis * Attributes include * Id: numerical identifier for each territory(bird) * Area: area of each polygon * San_bernardino_territory_centers * Point file showing Territory centers for historical SBNF territories – shifted for repro success analysis * Attributes include * Repro Territory id: unique identifier for each territory in broader set of territories

Besides the sifted locational data we have included - For the Resource selection function vegetation data, for the delivery analysis we have included an overview of prey deliveries by territory and vegetation data used, and for the reproductive analysis we have again included vegetation data as well as an overview of reproductive success. these are labled as follows:

Files for the prey capture analysis

Socal_RSF_data.txt

*description: Text file with vegetation data paired with capture locations both buffered polygons used in prey capture analysis and the unbuffered ones which were not used.(Pair with Socal_rsf_code R script) *format: .txt *Dimensions: 2641 X 35

*Variables: *ORIG_fid: completely unique identifier for each row *unique_id: unique identifier for each capture polygon(shared between a buffered capture location and its unbuffered pair) *territory_id: unique numerical idenifier of territory *Polygon_id: within territory unique prey capture polygon id *buff: bianary buffered or unbuffered (1=buffered, 0=unbuffered) *used: bianary used=1 available=0 *prey_type: prey species associated with polygon unkn:unknown, flsq:flying squirel, wora:woodrat, umou:mouse, pogo:pocketgopher, grsq: grey squirel, ubrd: unknown bird, umol:unknown mole, uvol, unknown vole. *area_sqm: area of polygon in square meters *CanCov_2020_buff: average canopy cover in polygon *CanHeight_2020_buff: average canopy height in polygon *Canlayer_2020_buff: average number of canopy layers in polygon *Understory_density_2020_buff: average brushy vegetation density in polygon *pix_COUNT: count of pixels in polygon (not needed for analysis) *p_chaparral: percent of polygon comprised of chaparral habitat
*p_conifer: percent of polygon comprised of conifer habitat *p_hardwood: percent of polygon comprised of hardwood habitat *p_other: percent of polygon comprised of other habitat types *Calveg_cap_CHt_gt10_CC_30to70_intersect_buff: percent of polygon comprised of trees taller than 10m with 30-70percent canopy cover (used to check data) *Calveg_cap_CHt_gt10_CCgt70_intersect_buff: percent of polygon comprised of trees taller than 10m with greater than 70percent canopy cover (used to check data) *Calveg_cap_CHt_lt10_intersect_buff:percent of polygon comprised of trees less than 10m (used to check data)
*p_sm_conifer: percent of polygon comprised of conifer trees less than 10m (used to calculate diversity)
*p_lrg_conifer_sc: percent of polygon comprised of conifer forests >10m tall with sparse canopy(used to calculate diversity) *p_large_conifer_dc: percent of polygon comprised of conifer forests greater than 10m tall with dense canopy (used to calculate diversity) *p_sm_hard: percent of polygon comprised of hardwood trees less than 10m (used to calculate diversity) *p_lrg_hard_sc: percent of polygon comprised of hardwood forests greater than 10m with sparse canopy(used to calculate diversity)
*p_lrg_hard_dc: percent of polygon comprised of hardwood forests greater than 10m dense canopy (used to calculate diversity) *p_forests_gt10_verysparse_CC: percent of polygon comprised of trees less than 10m with very sparse canopies (used to calculate diversity) *primary_edge: total distance in meters of primary edge in a polygon
*normalized_by_area_primary_edge: total distance in m of primary edge in a polygon divided by the area of the polygon
*secondary_edge: total distance in meters of secondary edge in a polygon *normalized_by_area_secondary_edge:total distance in m of secondary edge in a polygon divided by the area of the polygon *coarse_diversity: shannon diversity in each polygon (see methods below) *fine_diversity: shannon diversity in each polygon (see methods below) *nest_distance: distance from polygon center to nest for each polygon in meters

For the Delivery analysis

note: For information on determining average prey biomass see methods as well as zulla et al 2022 for flying squirels and woodrat masses Zulla CJ, Jones GM, Kramer HA, Keane JJ, Roberts KN, Dotters BP, Sawyer SC, Whitmore SA, Berigan WJ, Kelly KG, Gutiérrez RJ, Peery MZ. Forest heterogeneity outweighs movement costs by enhancing hunting success and fitness in spotted owls. doi:10.21203/rs.3.rs-1370884/v1. PPR:PPR470028.

prey_deliveries_byterritory.csv *Description: overview file of prey delivered to each nest *format: .csv *dimensions:332 x 8

*Variables: *SITE: Unique numerical identifier for each territory *DATE: date prey was delivered (in UTC) *CAMERA TIME: time in UTC prey was delivered *VIDEO TIME: time on video prey was delivered - unrelated to real time just original file
*PREY ITEM: prey species delivered to nest unkn:unknown, uncr: unknown if delivery(removed from eventual analysis due to

Ecopia_CensusTracts2020_Summaries displays the summarized areas and percentages of each individual land cover type present in each 2020 census tract. Ecopia percentages are calculated as [sum of the land cover type] / [total area that Ecopia delineated within the summary polygon]. The percentages are not calculated using the total area of the summary polygon as Ecopia may not have included the entire area in its delineations (e.g. vast water areas).

This dataset corresponds to land area polygons of Australian coastline and surrounding islands. It was generated from 10 m Sentinel 2 imagery from 2022 - 2024 using the Normalized Difference Water Index (NDWI) to distinguish land from water. It was estimated from composite imagery made up from images where the tide is above the mean sea level. The coastline approximately corresponds to the mean high water level. This dataset was created as part of the NESP MaC 3.17 northern Australian Reef mapping project. It was developed to allow the inshore edge of digitised fringing reef features to be neatly clipped to the land areas without requiring manual digitisation of the neighbouring coastline. This required a coastline polygon with an edge positional error of below 50 m so as to not distort the shape of small fringing reefs. We found that existing coastline datasets such as the Geodata Coast 100K 2004 and the Australian Hydrographic Office (AHO) Australian land and coastline dataset did not meet our needs. The scale of the Geodata Coast 100K 2004 was too coarse to represent small islands and the the positional error of the Australian Hydrographic Office (AHO) Australian land and coastline dataset was too high (typically 80 m) for our application as the errors would have introduced significant errors in the shape of small fringing reefs. The Digital Earth Australia Coastline (GA) dataset was sufficiently accurate and detailed however the format of the data was unsuitable for our application as the coast was expressed as disconnected line features between rivers, rather than a closed polygon of the land areas. We did however base our approach on the process developed for the DEA coastline described in Bishop-Taylor et al., 2021 (https://doi.org/10.1016/j.rse.2021.112734). Adapting it to our existing Sentinel 2 Google Earth processing pipeline. The difference between the approach used for the DEA coastline and this dataset was the DEA coastline performed the tidal calculations and filtering at the pixel level, where as in this dataset we only estimated a single tidal level for each whole Sentinel image scene. This was done for computational simplicity and to align with our existing Google Earth Engine image processing code. The images in the stack were sorted by this tidal estimate and those with a tidal high greater than the mean seal level were combined into the composite. The Sentinel 2 satellite follows a sun synchronous orbit and so does not observe the full range of tidal levels. This observed tidal range varies spatially due to the relative timing of peak tides with satellite image timing. We made no accommodation for variation in the tidal levels of the images used to calculate the coastline, other than selecting images that were above the mean tide level. This means tidal height that the dataset coastline corresponds to will vary spatially. While this approach is less precise than that used in the DEA Coastline the resulting errors were sufficiently low to meet the project goals.
This simplified approach was chosen because it integrated well with our existing Sentinel 2 processing pipeline for generating composite imagery. To verify the accuracy of this dataset we manually checked the generated coastline with high resolution imagery (ArcGIS World Imagery). We found that 90% of the coastline polygons in this dataset have a horizontal position error of less than 20 m when compared to high-resolution imagery, except for isolated failure cases. During our manual checks we identified some areas where our algorithm can lead to falsely identifying land or not identifying land. We identified specific scenarios, or 'failure modes,' where our algorithm struggled to distinguish between land and water. These are shown in the image "Potential failure modes": a) The coastline is pushed out due to breaking waves (example: western coast, S2 tile ID 49KPG). b) False land polygons are created because of very turbid water due to suspended sediment. In clear water areas the near infrared channel is almost black, starkly different to the bright land areas. In very highly turbid waters the suspended sediment appears in the near infrared channel, raising its brightness to a level where it starts to overlap with the brightness of the dimmest land features. (example: Joseph Bonaparte Gulf, S2 tile ID 52LEJ). This results in turbid rivers not being correctly mapped. In version 1-1 of the dataset the rivers across northern Australia were manually corrected for these failures. c) Very shallow, gentle sloping areas are not recognised as water and the coastline is pushed out (example: Mornington Island, S2 tile ID 54KUG). Update: A second review of this area indicated that the mapped coastline is likely to be very close to the try coastline. d) The coastline is lower than the mean high water level (example: Great Keppel (Wop-pa) Island, S2 tile ID 55KHQ). Some of these potential failure modes could probably be addressed in the future by using a higher resolution tide calculation and using adjusted NDWI thresholds per region to accommodate for regional differences. Some of these failure modes are likely due to the near infrared channel (B8) being able to penetrate the water approximately 0.5 m leading to errors in very shallow areas. Some additional failures include: - Interpreting jetties as land - Interpreting oil rigs as land - Bridges being interpreted as land, cutting off rivers Methods: The coastline polygons were created in four separate steps: 1. Create above mean sea level (AMSL) composite images. 2. Calculate the Normalized Difference Water Index (NDWI) and visualise as a grey scale image. 3. Generate vector polygons from the grey scale image using a NDWI threshold. 4. Clean up and merge polygons. To create the AMSL composite images, multiple Sentinel 2 images were combined using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile filter the "COPERNICUS/S2_HARMONIZED" image collection by - tile ID - maximum cloud cover 20% - date between '2022-01-01' and '2024-06-30' - asset_size > 100000000 (remove small fragments of tiles) 2. Remove high sun-glint images (see "High sun-glint image detection" for more information). 3. Split images by "SENSING_ORBIT_NUMBER" (see "Using SENSING_ORBIT_NUMBER for a more balanced composite" for more information). 4. Iterate over all images in the split collections to predict the tide elevation for each image from the image timestamp (see "Tide prediction" for more information). 5. Remove images where tide elevation is below mean sea level. 6. Select maximum of 200 images with AMSL tide elevation. 7. Combine SENSING_ORBIT_NUMBER collections into one image collection. 8. Remove sun-glint and apply atmospheric correction on each image (see "Sun-glint removal and atmospheric correction" for more information). 9. Duplicate image collection to first create a composite image without cloud masking and using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 10. Apply cloud masking to all images in the original image collection (see "Cloud Masking" for more information) and create a composite by using the 15th percentile of the images in the collection (i.e. for each pixel the 15th percentile value of all images is used). 11. Combine the two composite images (no cloud mask composite and cloud mask composite). This solves the problem of some coral cays and islands being misinterpreted as clouds and therefore creating holes in the composite image. These holes are "plugged" with the underlying composite without cloud masking. (Lawrey et al. 2022) Next, for each image the NDWI was calculated: 1. Calculate the normalised difference using the B3 (green) and B8 (near infrared). 2. Shift the value range from between -1 and +1 to values between 1 and 255 (0 reserved as no-data value). 3. Export image as 8 bit unsigned Integer grey scale image. During the next step, we generated vector polygons from the grey scale image using a NDWI threshold: 1. Upscale image to 5 m resolution using bilinear interpolation. This was to help smooth the coastline and reduce the error introduced by the jagged pixel edges. 2. Apply a threshold to create a binary image (see "NDWI Threshold" for more information) with the value 1 for land and 2 for water (0: no data). 3. Create polygons for land values (1) in the binary image. 4. Export as shapefile. Finally, we created a single layer from the vectorised images: 1. Merge and dissolve all vector layers in QGIS. 2. Perform smoothing (QGIS toolbox, Iterations 1, Offset 0.25, Maximum node angle to smooth 180). 3. Perform simplification (QGIS toolbox, tolerance 0.00003). 4. Remove polygon vertices on the inner circle to fill out the continental Australia. 5. Perform manual QA/QC. In this step we removed false polygons created due to sun glint and breaking waves. We also removed very small features (1 – 1.5 pixel sized features, e.g. single mangrove trees) by calculating the area of each feature (in m2) and removing features smaller than 200 m2. 15th percentile composite: The composite image was created using the 15th percentile of the pixels values in the image stack. The 15th percentile was chosen, in preference to the median, to select darker pixels in the stack as these tend to correspond to images with clearer water conditions and higher tides. High sun-glint image detection: Images with high sun-glint can lead to lower quality composite images. To determine high sun-glint images, a land mask was first applied to the image to only retain water pixels. This land mask was estimated using NDWI. The proportion of the water pixels in the near-infrared and short-wave infrared bands above a sun-glint threshold was calculated. Images with a high proportion were then filtered out of the image collection.
Sun-glint removal and atmospheric correction: The Top of Atmosphere L1

Modern non-contact methods for data acquisition are becoming widely used for monitoring soil erosion and for assessing soil degradation after rainfall events. Photogrammetric methods are especially favored to obtain a detailed and precise digital surface model (DSM) of the surveyed area. This paper introduces the algorithm and its Python implementation as a tool for ArcGIS software, which makes efficient automatic calculations of the volume of erosion rills or gullies. The input parameters are a DSM, and the rill edge polygon. The method was tested on an artificially created rill, where the result acquired using presented method was compared to the real volume. The comparison showed that the algorithm may underestimate the volume by 10–15%. In addition, the influence of the position of the rill edge polygon was tested on two DSMs of erosion rills. The resulting volumes of the rills, calculated on the basis of eight different edge polygons, varied by 5%. The algorithm also automates interpolation of the surface prior to erosion, which simplifies its usage in firstly monitored regions. The algorithm can also be used for volumetric analyses in other research areas and it is made available as a supplement of the publication.

Ecopia_HUC12_Summaries displays the summarized areas and percentages of each individual land cover type present in each HUC12 subwatershed. Ecopia percentages are calculated as [sum of the land cover type] / [total area that Ecopia delineated within the summary polygon]. The percentages are not calculated using the total area of the summary polygon as Ecopia may not have included the entire area in its delineations (e.g. vast water areas).

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS development. Based on guidance from WO, SFAs were to be limited to BLM decision space (surface/sub-surface management areas) within PHMA. Each EIS was asked to provide the limited SFA dataset back to the National Operations Center to ensure consistent representation and analysis. Returned SFA data, modified by each individual EIS, was then consolidated at the BLM’s National Operations Center retaining the three standardized fields contained in this dataset.Several Modifications from the original FWS dataset have been made. Below is a summary of each modification.1. The data as received from FWS: 16,514,163 acres & 1 record.2. Edited to name SFAs by Wildlife Habitat Spatial Analysis Lab:Upon receipt of the “Outiline_AreasofSignificance” dataset from the FWS, a copy was made and the one existing & unnamed record was exploded in an edit session within ArcMap. A text field, “AoS_Name”, was added. Using the maps provided with Memorandum FWS/AES/058711, polygons were manually selected and the “AoS_Name” field was calculated to match the names as illustrated. Once all polygons in the exploded dataset were appropriately named, the dataset was dissolved, resulting in one record representing each of the seven SFAs identified in the memorandum.3. The NVCA EIS made modifications in concert with local FWS staff. Metadata and detailed change descriptions were not returned with the modified data. Contact Leisa Wesch, GIS Specialist, BLM Nevada State Office, 775-861-6421, lwesch@blm.gov, for details.4. Once the data was returned to the Wildlife Habitat Spatial Analysis Lab from the NVCA EIS, gaps surrounding the State of NV were closed. These gaps were introduced by the NVCA edits, exacerbated by them, or existed in the data as provided by the FWS. The gap closing was performed in an edit session by either extending each polygon towards each other or by creating a new polygon, which covered the gap, and merging it with the existing features. In addition to the gaps around state boundaries, a large area between the S. Idaho and S.E. Oregon SFAs was filled in. To accomplish this, ADPP habitat (current as of January 2015) and BLM GSSP SMA data were used to create a new polygon representing PHMA and BLM management that connected the two existing SFAs.5. In an effort to simplify the FWS dataset, features whose areas were less than 40 acres were identified and FWS was consulted for guidance on possible removal. To do so, features from #4 above were exploded once again in an ArcMap edit session. Features whose areas were less than forty acres were selected and exported (770 total features). This dataset was provided to the FWS and then returned with specific guidance on inclusion/exclusion via email by Lara Juliusson (lara_juliusson@fws.gov). The specific guidance was:a. Remove all features whose area is less than 10 acresb. Remove features identified as slivers (the thinness ratio was calculated and slivers identified by Lara Juliusson according to https://tereshenkov.wordpress.com/2014/04/08/fighting-sliver-polygons-in-arcgis-thinness-ratio/) and whose area was less than 20 acres.c. Remove features with areas less than 20 acres NOT identified as slivers and NOT adjacent to other features.d. Keep the remainder of features identified as less than 40 acres.To accomplish “a” and “b”, above, a simple selection was applied to the dataset representing features less than 40 acres. The select by location tool was used, set to select identical, to select these features from the dataset created in step 4 above. The records count was confirmed as matching between the two data sets and then these features were deleted. To accomplish “c” above, a field (“AdjacentSH”, added by FWS but not calculated) was calculated to identify features touching or intersecting other features. A series of selections was used: first to select records 6. Based on direction from the BLM Washington Office, the portion of the Upper Missouri River Breaks National Monument (UMRBNM) that was included in the FWS SFA dataset was removed. The BLM NOC GSSP NLCS dataset was used to erase these areas from #5 above. Resulting sliver polygons were also removed and geometry was repaired.7. In addition to removing UMRBNM, the BLM Washington Office also directed the removal of Non-ADPP habitat within the SFAs, on BLM managed lands, falling outside of Designated Wilderness’ & Wilderness Study Areas. An exception was the retention of the Donkey Hills ACEC and adjacent BLM lands. The BLM NOC GSSP NLCS datasets were used in conjunction with a dataset containing all ADPP habitat, BLM SMA and BLM sub-surface management unioned into one file to identify and delete these areas.8. The resulting dataset, after steps 2 – 8 above were completed, was dissolved to the SFA name field yielding this feature class with one record per SFA area.9. Data were provided to each EIS for use in FEIS allocation decision data development.10. Data were subset to BLM decision space (surface/sub-surface) within PHMA by each EIS and returned to the NOC.11. Due to variations in field names and values, three standardized fields were created and calculated by the NOC:a. SFA Name – The name of the SFA.b. Subsurface – Binary “Yes” or “No” to indicated federal subsurface estate.c. SMA – Represents BLM, USFS, other federal and non-federal surface management 12. The consolidated data (with standardized field names and values) were dissolved on the three fields illustrated above and geometry was repaired, resulting in this dataset.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

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.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers (ASCE), and reprinted with permission of ASCE(https://coast.noaa.gov/slr/).National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management. “VA_2010_CCAP_LAND_COVER,” “NC_2010_CCAP_LAND_COVER,” “SC_2010_CCAP_LAND_COVER,” “GA_2010_CCAP_LAND_COVER,” “FL_2010_CCAP_LAND_COVER”. Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed September 2017 at www.coast.noaa.gov/ccapftp.Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M L.; Wolff, C.; Linck, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; Hinkel J.; Nicholls, R.J.; and Sally Brown. 2018. Future response of global coastal wetlands to sea-level rise. Nature 561: 231-234.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers (ASCE), and reprinted with permission of ASCE(https://coast.noaa.gov/slr/).National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management. “VA_2010_CCAP_LAND_COVER,” “NC_2010_CCAP_LAND_COVER,” “SC_2010_CCAP_LAND_COVER,” “GA_2010_CCAP_LAND_COVER,” “FL_2010_CCAP_LAND_COVER”. Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed September 2017 at www.coast.noaa.gov/ccapftp.Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M L.; Wolff, C.; Linck, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; Hinkel J.; Nicholls, R.J.; and Sally Brown. 2018. Future response of global coastal wetlands to sea-level rise. Nature 561: 231-234.

Tabular Summaries - Communities at Risk As part of Montana DNRC’s Montana Wildfire Risk Assessment (MWRA), wildfire risk to homes, commercial buildings, and other structures was assessed across the state. The purpose of this assessment is to identify the counties and communities whose structures are most threatened by wildfire—both on average and in total. The risk-to-structures methods used for this assessment are identical to the methods used for structures within the overall MWRA project. See earlier section 3.4.1 of the report (page 20) for details. This portion of the report addresses only the tabular summaries. The summary methods used in this section were customized to the MWRA results from similar methods previously developed for the Pacific Northwest Risk Assessment (PNRA) and for the national Wildfire Risk to Communities (WRC) project.  Mean Risk to Structures We calculated the Mean Risk to Structures as the product of Mean Conditional Risk to Structures and Mean Burn Probability (multiplied by 1000 to remove decimal places). This is the primary variable by which the summary polygons are ranked. Like the components used to calculate it, Mean Risk to Structures is not a cumulative measure for a summary polygon, so it does not necessarily increase as the number or importance of structures increases. It represents the average of the structures in the polygon regardless of the total number or importance of structures. Total Structure Risk We calculated Total Structure Risk as the product of Mean Risk to Structures and Total Structure Importance. This is the secondary variable by which the summary polygons are ranked. Unlike the previous measures, the total importance of structures (their number and mean importance) strongly influences Total Structure Risk. The risk-to-structures results were summarized for two primary sets of summary polygons: MT Counties MT Communities Expanded Area Each set of summary polygons captures nearly all structures in Montana, without overlap. In the MT Counties set, a summary polygon is an individual county (e.g. Ravalli County). In the MT Communities (core plus zone combined) set called MT Communities Expanded Area, a summary polygon is the community core plus the zone surrounding the core (as defined below). Expanded Areas include populated areas outside of official community boundaries that are closer to the selected community than to any other community. Long definition: Populated areas not within the boundaries of a community were associated with the community to which they were closest, as measured by travel time. Travel time is influenced by road networks, associated travel speeds, and physical barriers such as water. Populated areas greater than 45 minutes travel time from any community are not included within the Expanded Area for any community. For this assessment, a community core was defined as a Populated Place Area (PPA) as identified by the U.S. Census Bureau. PPAs include incorporated cities and towns as well as Census Designated Places (CDPs). A CDP is an unincorporated concentration of population—a statistical counterpart to incorporated cities and towns. There are 364 PPAs across Montana. Of those, 127 (35 percent) are incorporated cities or towns, and 235 (65 percent) are CDPs. Two PPAs—Butte-Silver Bow and Anaconda-Deer Lodge—are unique in that they represent the balance of a county that is not otherwise incorporated; they are much larger in size than most PPAs. In the PPA dataset, the CDPs represent the location of highest concentration of population for a community; they do not include the less-densely populated areas surrounding the PPA. We refer to the U.S. Census PPA delineation as the community “core.” Approximately 66 percent of Montana’s total structure importance can be found within these PPA core areas (Figure A.1 of the Montana Wildfire Risk Assessment report). To include the populated area and structures surrounding the PPAs, Ager and others (2019) used a travel-time analysis to delineate the land areas closest by drive-time to each PPA core, up to a maximum of 45 minutes travel time. Approximately 33 percent of Montana’s total structure importance can be found within 45 minutes travel time of the cores. Only 1 percent of the total structure importance is not within 45-minutes travel time of any community core.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 3.0-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

Resilient Coastal SitesUsers can select their geography to access the data (Gulf of America, South Atlantic, Northeast)https://conservationgateway.org/ConservationByGeography/NorthAmerica/UnitedStates/edc/reportsdata/climate/CoastalResilience/Pages/default.aspx Report citations:Northeast: Anderson, M.G. and Barnett, A. 2017. Resilient Coastal Sites for Conservation in the Northeast and Mid-Atlantic US. The Nature Conservancy, Eastern Conservation Science.South Atlantic: Anderson, M.G. and Barnett, A. 2019. Resilient Coastal Sites for Conservation in the South Atlantic US. The Nature Conservancy, Eastern Conservation Science. Resilient Sites: Terrestrial & Coastal integratedhttps://tnc.maps.arcgis.com/home/item.html?id=2b0ff2a8fb5340a5a5e91ff9c185aa1d Credit: Center for Resilient Conservation Science, The Nature ConservancyTo assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017).The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected.Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marshcordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018.Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. https://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers (ASCE), and reprinted with permission of ASCE(https://coast.noaa.gov/slr/).National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management. “VA_2010_CCAP_LAND_COVER,” “NC_2010_CCAP_LAND_COVER,” “SC_2010_CCAP_LAND_COVER,” “GA_2010_CCAP_LAND_COVER,” “FL_2010_CCAP_LAND_COVER”. Coastal Change Analysis Program (C-CAP) Regional Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed September 2017 at https://.coast.noaa.gov/ccapftp.Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M L.; Wolff, C.; Linck, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; Hinkel J.; Nicholls, R.J.; and Sally Brown. 2018. Future response of global coastal wetlands to sea-level rise. Nature 561: 231-234.

Ecopia_County_Summaries displays the summarized areas and percentages of each individual land cover type present in each County. Ecopia percentages are calculated as [sum of the land cover type] / [total area that Ecopia delineated within the summary polygon]. The percentages are not calculated using the total area of the summary polygon as Ecopia may not have included the entire area in its delineations (e.g. vast water areas).

This activity will no longer be maintained after June 16, 2025. Current lessons are available in the K-12 Classroom Activities Gallery.

This activity uses Map Viewer. ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.StandardsCCSS: MATH.CONTENT.HSG.GPE.B.7 – Compute perimeters of polygons and areas of triangles and rectangles.CCSS: MATH.CONTENT.6.G.A.1 – Solve real-world and mathematical problems involving area, surface area, and volume.CCSS: MATH.CONTENT.7.G.B.6 – Solve real-world and mathematical problems involving area, volume, and surface area of two- and three-dimensional objects composed of triangles, quadrilaterals, polygons, cubes, and right prisms.Learning outcomesStudents will find the area of a complex figure.Students will find the areas of rectangles, squares, triangles, and trapezoids.More activitiesAll Mathematics GeoInquiriesAll GeoInquiries

Surface trust lands are displayed and summarized in a table by Tribal entity. A Tribal entity may be a federally recognized tribe or a constituent component of a federally recognized tribe. For each Tribal entity in the table the total trust acreage is given as well as the breakout of tribal trust and allotted trust acreage. Tribal trust lands are those where a tribe is the named landowner, and the tribe has a full percentage ownership interest in the tract division. Allotted trusts consist of two definitions of allotted. An allotted land may be a land where the tribe is the named landowner but only has a partial percentage interest in the tract division. An allotted land may also be one in which an individual associate of a tribe has any percentage ownership. A trust land would be any tribal, or allotted land that can be tied back the tribal entity. For any regional trust lands that do not have an identified entity interest or have a split entity interest that does not have a clear definition of the split, the trust lands are summarized in a row labeled undesignated. The Land Area Representation (LAR), BIA Regional Boundaries, Regional and BIA offices, as well as tribal entities without land are added in each region and adjacent territories when suitable to provide context. The tribal entity names are given by common use names instead of federal register names and may be further split into subdivisions within the recognized tribe. The Land Area Representation (LAR) is a combined dataset which creates a union of trust lands and reservation extents. The polygons are organized around tribal divisions and groupings and lack disambiguation between the trust land and reservation extent components. Due to the static creation date the LAR may not match different sources for components of the LAR. The Trust Asset and Accounting Management System (TAAMS) which is the system of record for trust lands is the source for records used in acreage calculations. (Last calculated May 2025) Tribal lands are lands where the tribe is the named landowner, and the tribe has a full percentage ownership interest of a tract division. Allotted lands are those in which the tribe is the named landowner but is a partial percentage ownership interest of a tract division, or an allotment may be individually owned by a tribe associate with any percentage ownership interest in a tract division. Trust lands are the summed totals of tribal and allotted interests for a tribal entity. LAR – Land Area Representation is a combined dataset which creates a union of trust lands and reservation extents. The polygons are organized around tribal divisions and groupings and lack disambiguation between the trust land and reservation extent components. TAAMS – The Trust Asset and Accounting Management System which is the system of record for trust land transactions and the source for records used in acreage calculations.

The offshore wind Maritime Technical Working Group (M-TWG), led by the New York State Department of State (DOS) and supported by NYSERDA is an advisory entity formed to engage regional stakeholders with maritime and/or offshore wind responsibilities and interests working to advance offshore wind (OSW) development.Using a vessel traffic model developed by COWI in 2022,* vessel AIS pings from 2017 were analyzed to determine idle ping counts (Speed Over Ground between 0 and 1 knots & pings further off shore than 1000ft to avoid mooring vessels) by area to identify CPAA outside of the DAA. The ping counts were based on idling AIS pings, number of unique Idling Episode Tracks (30 minutes or more), Average Duration of Idling Episodes, Median Duration of Idling Episodes, Total duration of Idling Episode Tracks and an (ping count) occupancy metric calculated using the total duration of the idling episode track within an anchorage area polygon for the 2017 year divided by the surface area of the anchorage area polygon. This metric is measured in hours per square mile (hr/sq mi).\ For full details on the analysis, see the Anchorage Area Assessment Companion Memo and contact COWI for further data.*see NYSERDA Vessel Traffic Assessment Report 22-11 here https://www.nyserda.ny.gov/All-Programs/Offshore-Wind/Focus-Areas/Supply-Chain-Economic-Development/Port-InfrastructureContact:BTMI (COWI) Engineering, P.C.88 Pine Street Suite 400, New York, NY 10005 Website: www.cowi.com

The National Urban Change Indicator (NUCI) is a change indicator dataset covering the lower 48 United States that uses Vantor’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.

The Buildings (BG) layer consists of photogrammetrically interpreted polygons representing roof outlines of manmade structures in Jefferson County, Kentucky in Spring of 2016. A building is a manmade structure which may be habitable by human beings, animals or which stores materials and is at a minimum 10' x 10' in roof surface area. A building may house a variety of activities at one time, or sequentially over its life. A building may also sit vacant, be in a partial state of destruction, or construction. A feature classified as building but not having a roof (silo, tank or water tower) will show outline of the features shape. View detailed metadata.Information on Building Height Attributes:Minimum Height – Feet: Minimum Height Feet calculated from Z_Max height (feet)Maximum Height – Feet: Maximum Height Meters calculated from Z_Max height (feet)Average Height – Feet: Average Height Feet calculated from Z_Max height (feet)SArea or Surface_Area: 3D surface area for the region defined by each polygon.Min_Slope: Slope value closest to zero within the area defined by the polygon.Max_Slope: Highest slope value along the line or within the area defined by the polygon.Avg_slope - Average slope value within the area defined by the polygon.Maximum Height Meters - Max Height Meters calculated from Z_Max height (feet)