This data set represents smoothed, 2-foot bare earth contours (isolines) for the Lower Androscoggin (01040002) HUC 8 unit. It was derived from a data set which was compiled from LIDAR collections in NH available as of spring, 2019. The raster was filtered using the ArcGIS FOCAL STATISTICS tool with a 3x3 circular neighborhood. The contours were generated using the ArcGIS CONTOUR tool while applying a Z factor of 3.2808 to convert the elevation values from meters to feet. The filtered contours were then smoothed using the ArcGIS SMOOTH LINE tool. The data include an INDEX field with values of 10 and 100 to flag 10 and 100-foot contours. When viewed using this service, contours become visible at scales greater than 1:10,000.

This data set represents smoothed, 2-foot bare earth contours (isolines) for the Lower Millers River (0108020202) HUC 10 unit. It was derived from a data set which was compiled from LIDAR collections in NH available as of spring, 2019. The raster was filtered using the ArcGIS FOCAL STATISTICS tool with a 3x3 circular neighborhood. The contours were generated using the ArcGIS CONTOUR tool while applying a Z factor of 3.2808 to convert the elevation values from meters to feet. The filtered contours were then smoothed using the ArcGIS SMOOTH LINE tool. The data include an INDEX field with values of 10 and 100 to flag 10 and 100-foot contours. Note on HUC 01060000310: Due to limitations in the source LIDAR data, some anomalies exist in the generated contours in coastal areas of the state. These were left in the data so that users can determine what further processing best meets their application needs.

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of LOW marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Low marsh (LM) was defined as regularly flooded marsh [SLAMM category 8]. LM is normally inundated by tidal water at least once per day. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories.

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years.

2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year.

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh).

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities.

Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

This geodatabase includes spatial datasets that represent the Pennsylvanian aquifers in the Lower Peninsula of Michigan. Included are: (1) polygon extents; datasets that represent the entire aquifer extent in the States of Alabama, Georgia, Illinois, Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, Tennessee, Virginia, and West Virginia; the entire extent subdivided into subareas or subunits where data exist (Lower Peninsula of Michigan only), (2) raster datasets for the altitude of each aquifer subarea or subunit, (3) points of altitude used to generate the surface rasters

The extent of the Pennsylvanian aquifer is derived from the linework of the Pennsylvanian aquifers extent maps in U.S. Geological Survey Scientific Investigations Report 2009-5060 (USGS SIR 2009-5060), and a digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas 730-J.

The area with top and bottom aquifer surface data, the Lower Peninsula of Michigan, subarea 2 (SA2) of the Pennsylvanian aquifer has 2 aquifer subunits, the (A1) Upper and (A2) Lower Pennsylvania aquifers. The altitude values for each subunit available were digitized from georeferenced datasets of altitude points used in the modeling effort described in USGS SIR 2009-5060, and the resultant top and bottom altitude values were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from point altitude data. The primary tool was a version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. The raster surfaces were corrected for the areas where the altitude of an underlying layer of the aquifer exceeded altitude of an overlying layer bu setting the underlying surface raster to the altitude values of the overlying surface raster.

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of total marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). Total marsh (TM) was defined as the sum of low marsh and high marsh [SLAMM category 8 + SLAMM category 7 + SLAMM category 20]. Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories.

1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years.

2) In Raster Calculator, set the SLAMM codeequal to8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year.

3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh).

Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities.

Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

This raster GIS dataset contains 5-meter-resolution cells depicting the areas of HIGH marsh gain (value=1), lost (value=-1) and remaining (no change; value=0). High marsh (HM) was defined as the aggregation of irregularly-flooded marsh [SLAMM category 7] and transitional salt marsh [SLAMM category 20]. HM is covered by water only sporadically (once per day or less). Based on SLAMM simulation outputs, we generated the gain and loss map by using the “Raster Calculator” tool under “Spatial Analyst Tools” in ArcGIS software. The methodology consists of the three steps listed below (where we use low marsh [LM] as an example). The same process can be applied to other SLAMM land cover categories. 1) Open ArcMap, add SLAMM simulation raster outputs (all SLAMM categories) for baseline year and future years. 2) In Raster Calculator, set the SLAMM code equal to 8 (low marsh = SLAMM category 8) to generate a new raster. Each individual cell in the new raster is assigned a value of “0” or “1”. “1” is low marsh and “0” is any other SLAMM land cover category. Perform this step for both the baseline year and future year. 3) In Raster Calculator, subtract the new raster for the baseline year from the new raster for the future year (formula = new future year raster - new baseline year raster). The calculation generates a new raster, in which each individual cell is assigned a value of “-1”, “0”, or “1”. Based on the calculation, “-1” means low marsh loss in the future (the cell has converted from low marsh to a different SLAMM category), “0” means low marsh is remaining (the cell stays the same), and “1” means low marsh gain in the future (the cell has converted from a different SLAMM category to low marsh). Prior SLAMM work has been performed in the Delaware Bay, but our methods differ in that we derive results for specific marsh areas and utilize more recent, higher resolution elevation data (2015 USGS CoNED Topobathy Model: New Jersey and Delaware), the most recent SLR projections, and site-specific accretion data (through 2016). These SLAMM simulations were performed as part of a larger project by the USEPA on frameworks and methods for characterizing relative wetland vulnerabilities. Note: additional raster files from this project are available upon request. These include files from low and high SLR scenarios and different model protection scenarios. For more information, contact Jordan West (West.Jordan@epa.gov).

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2023 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This collection of gridded data layers provides the extent of inundation in May 2020 resulting from the cyclone Amphan in 39 coastal districts in India and Bangladesh.

Input data:

These geospatial data layers are derived from Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) data for pre-Amphan (May 5-18, 2020) and post-Amphan (May 22-30, 2020) periods. We accessed ready-to-use SAR data on Google Earth Engine (GEE). These input data were preprocessed using Ground Range Detected (GRD) border-noise removal, thermal noise removal, radiometric calibration, and terrain correction, to derive backscatter coefficients (σ°) in decibels (dB). We used VH polarisation instead of VV, since the latter is known to be affected by windy conditions as compared to VH.

Methods:

We developed a binary water/non-water classification scheme for the pre- and post-Amphan images using the automated Otsu thresholding approach that finds optimum threshold values based on clusters found in the histograms of pixel values. This analysis resulted in eight images: four each for pre-Amphan and post-Amphan periods (one each for coastal districts of Odisha and West Bengal and two for Bangladesh for each period). The pixels in these images have two values: 0 for non-water and 1 for water.

We then used a decision rule to identify areas that changed from ‘non-water’ to ‘water’ after the cyclone. The decision rule generated the ‘inundation layer’ with the permanent water bodies such as river, lakes, oceans and aquaculture masked out. This analysis resulted in four images, each with pixels with a value of 1 for inundated regions.

Data set format:

The spatial resolution of all the derived datasets is 10m. These georeferenced datasets are distributed in GEOTIFF format, and are compatible with GIS and/or image processing software, such as R and ArcGIS. The GIS-ready raster files can be used directly in mapping and geospatial analysis.

Data set for download:

A. Three data layers for Odisha, India:

OD_pre_binary.tif

OD_post_binary.tif

OD_inundation.tif

These data layers cover 10 districts: Baleshwar, Bhadrak, Cuttack, Jagatsinghpur, Jajpur, Kendrapara, Keonjhar, Khordha, Mayurbhanj and Puri.

B. Three data layers for West Bengal, India:

WB_pre_binary.tif

WB_post_binary.tif

WB_inundation.tif

These data layers cover 9 districts: Barddhaman, East Midnapore, Haora, Hugli, Kolkata, Nadia, North 24 Parganas, South 24 Parganas, and West Midnapore.

C. Six data layers for Bangladesh – three each for lower (L) region and upper (U) region.

BNG_L_pre_binary.tif

BNG_L_post_binary.tif

BNG_L_inundation.tif

BNG_U_pre_binary.tif

BNG_U_post_binary.tif

BNG_U_inundation.tif

The data layers for the lower region cover 11 districts: Bagerhat, Barguna, Barisal, Bhola, Jhalokati, Khulna, Lakshmipur, Noakhali, Patuakhali, Pirojpur, and Satkhira.

The data layers for the upper region cover 9 districts: Chuadanga, Jessore, Jhenaidah, Kushtia, Meherpur, Naogaon, Natore, Pabna, and Rajshahi.

Abstract

The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset. The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation). The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

Data acquisition

The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

Data collection

Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

Value of the data

The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

Data description

A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

Experimental design, materials and methods

The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

Acknowledgments

This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

References

[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

This data set represents smoothed, 2-foot bare earth contours (isolines) for the Lower Ammonoosuc River (0108010305) HUC 10 unit. It was derived from a data set which was compiled from LIDAR collections in NH available as of spring, 2019. The raster was filtered using the ArcGIS FOCAL STATISTICS tool with a 3x3 circular neighborhood. The contours were generated using the ArcGIS CONTOUR tool while applying a Z factor of 3.2808 to convert the elevation values from meters to feet. The filtered contours were then smoothed using the ArcGIS SMOOTH LINE tool. The data include an INDEX field with values of 10 and 100 to flag 10 and 100-foot contours. Note on HUC 01060000310: Due to limitations in the source LIDAR data, some anomalies exist in the generated contours in coastal areas of the state. These were left in the data so that users can determine what further processing best meets their application needs.

Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values.This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

The Florida Panther Hunting Habitat Model (FPHHM) evaluates the quality of Florida panther (Puma concolor coryi) hunting habitat on private lands in areas of southwest Florida designated as the primary, secondary, and dispersal zones of panther habitat. The model also provides a means for evaluating predation risk to livestock across the landscape. The model was developed using the species distribution model MaxEnt (Phillips et al. 2006), nighttime GPS telemetry locations from panthers on private lands, and environmental variables that influence the movement of panthers. This model provides a means both to evaluate the quality of panther hunting habitat and the relative risk to livestock (calves). To assess whether the model could predict predation risk to livestock we compared model results to documented calf depredation locations collected in south Florida during 2010-2014. The model correctly predicted panther GPS locations with 99.9% accuracy and is a useful tool for predicting predation risk. Model output consists of an ArcGIS raster map in which each cell (10m x 10m) provides an estimate of the probability of panther presence which ranges from 1 (highest probability of presence) to 0 (lowest probability of presence). It is recommended that probability of presence be interpreted as an index of habitat suitability, rather than a literal interpretation of percent probability (Elith et al. 2011; Merow et al. 2013). For example, a cell with a value of 0.6 should be interpreted as having a greater chance of species occupancy than a cell of value 0.5, and not as the literal interpretation of having a 60% probability of the species occurring at the site. The process of assessing individual ranches for the amount of hunting habitat, and therefore predation risk, is fairly simple and can be performed by anyone proficient in ArcGIS. To do so, ranch property layers should be extracted from the panther hunting habitat model output map in order to calculate an average probability of presence value. This value can then be compared between ranches for prioritization purposes. Model data and information are provided within the hub Dataverse: Florida Panther Hunting Habitat Model (FPHHM), which includes the GIS Output dataset for the FPHHM. There are also 3 subdataverses, GIS layers, MaxEnt Output, and Metadata, each of which include datasets.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery.Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications. This Z-scores dataset as well as the raw lbs/acre data that the Z-scores were derived from can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.

This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications.

Saturated thickness map of the Rush Springs aquifer in central Oklahoma. Map displays the thickness of the water level (potentiometric) surface in the Rush Springs aquifer to the base of the unit, which is defined as the base of the Marlow Formation by the OWRB for their study released in 2018. Saturated thickness ranges between 0-432 feet, with an average saturated thickness of 181 feet. In areas where the potentiometric surface rises above the top of the Rush Springs Formation into the Cloud Chief Formation, the thickness was capped at the Rush Springs/Cloud Chief contact. This calculation was done in ArcGIS 10.2.2 using a raster calculator subtracting the saturated thickness within the Cloud Chief from the total saturated thickness. Also used in the process was the Mosaic to New Raster tool to create a raster that included values from both the smaller extent Cloud Chief and the larger extent Rush Springs in one output raster with the extent of the entire Rush Springs aquifer.

This layer provides an index of the Wildlife Habitat and Biodiversity Potential of forests and wetlands across the state of Maryland. Index values range from 1 (low) to 5 (high), based on a number of factors, including the size of habitat, degree of connection to other habitats (scored through the MD Green Infrastructure model) , and presence of rare species or habitats (scored through the MD BioNet model). Land in the top two ranks of MD BioNet was assigned the 1st and 2nd quintile of value, respectively. Land in the Green Infrastructure was assigned into quintiles based upon their score, and assigned corresponding values. Forests and wetlands occurring outside both models were given the lowest quintile value.

This data layer was created as part of the Maryland Department of Natural Resources "Accounting for Maryland's Ecosystem Services" program.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Environment/MD_EcosystemServices/MapServer/20Download the Ecosystem Services layers at: https://www.dropbox.com/s/e6ovfcc01dxvnmo/EcosystemServices.gdb.zip?dl=0.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery. Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.

Reason for SelectionThe Southeast United States is a global biodiversity hotspot that supports many rare and endemic reptile and amphibian species (Barrett et al. 2014, EPA 2014). These species are experiencing dramatic population declines driven by habitat loss, pollution, invasive species, and disease (Sutherland and deMaynadier 2012, EPA 2014, CI et al. 2004). Amphibians provide an early signal of environmental change because they rely on both terrestrial and aquatic habitats, are sensitive to pollutants, and are often narrowly adapted to specific geographic areas and climatic conditions. As a result, they serve as effective indicators of ecosystem health (CI et al. 2004, EPA 2014). Their association with particular microhabitats and microclimates makes amphibians vulnerable to climate change, and Southeast amphibians are predicted to lose significant amounts of climatically suitable habitat in the future (Barrett et al. 2014). PARCAs also represent the condition and arrangement of embedded isolated wetlands. Many amphibians breed in temporary (i.e., ephemeral) wetlands surrounded by upland habitat, which are not well-captured by existing indicators in the Blueprint (Erwin et al. 2016).Input DataSoutheast Blueprint 2024 extent2023 U.S. Census TIGER/Line state boundaries, accessed 4-5-2024: download the data Southeast Priority Amphibian and Reptile Conservation Areas (PARCAs) PARCAs for all Southeast states except for Mississippi, Virginia, and Kentucky, shared by José Garrido with the Amphibian and Reptile Conservancy (ARC) on 3-5-2024PARCAs for Mississippi, shared by Luis Tirado with ARC on 4-26-2024 (these PARCAs were identified more recently and were not yet captured in ARC’s Southeast PARCAs dataset)South Atlantic PARCAs: Neuse Tar River PARCA (this PARCA was identified through a project funded by the South Atlantic Landscape Conservation Cooperative and is not yet captured in ARC’s Southeast PARCAs dataset; we added this PARCA after consultation with ARC staff) To view a map depicting some of the PARCAs provided, scroll to the bottom of the work page of the ARC website under the heading “PARCAs Nationwide”; to access the data, email info@ARCProtects.org. PARCA is a nonregulatory designation established to raise public awareness and spark voluntary action by landowners and conservation partners to benefit amphibians and/or reptiles. Areas are nominated using scientific criteria and expert review, drawing on the concepts of species rarity, richness, regional responsibility, and landscape integrity. Modeled in part after the Important Bird Areas program developed by BirdLife International, PARCAs are intended to be nationally coordinated but locally implemented at state or regional scales. Importantly, PARCAs are not designed to compete with existing landscape biodiversity initiatives, but to complement them, providing an additional spatially explicit layer for conservation consideration.
PARCAs are intended to be established in areas: capable of supporting viable amphibian and reptile populations, occupied by rare, imperiled, or at-risk species, and rich in species diversity or endemism. For example, species used in identifying the PARCAs in the Southeast include: alligator snapping turtle, Barbour’s map turtle, one-toed amphiuma, Savannah slimy salamander, Mabee’s salamander, dwarf waterdog, Neuse river waterdog, chicken turtle, spotted turtle, tiger salamander, rainbow snake, lesser siren, gopher frog, Eastern diamondback rattlesnake, Southern hognose snake, pine snake, flatwoods salamander, gopher tortoise, striped newt, pine barrens tree frog, indigo snake, and others. There are four major implementation steps: Regional PARC task teams or state experts can use the criteria and modify them when appropriate to designate potential PARCAs in their area of interest. Following the identification of all potential PARCAs, the group then reduces these to a final set of exceptional sites that best represent the area of interest. Experts and stakeholders in the area of interest collaborate to produce a map that identifies these peer-reviewed PARCAs. Final PARCAs are shared with the community to encourage the implementation of voluntary habitat management and conservation efforts. PARCA boundaries can be updated as needed. Mapping Steps Merge the three PARCA polygon datasets and convert from vector to a 30 m pixel raster using the ArcPy Feature to Raster function. Give all PARCAs a value of 1.Add zero values to represent the extent of the source data and to make it perform better in online tools. Convert to raster the TIGER/Line state boundaries for all SEAFWA states except for Virginia and Kentucky and assign them a value of 0. We excluded Virginia and Kentucky because PARCAs have not yet been identified for these states. Use the Cell Statistics “MAX” function to combine the two above rasters.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:1 = Priority Amphibian and Reptile Conservation Area (PARCA) 0 = Not a PARCA (excluding Kentucky and Virginia)Known IssuesThe mapping of this indicator is relatively coarse and doesn’t always capture differences in pixel-level quality in the outer edge of PARCAs. For example, some PARCAs include developed areas.This indicator is binary and doesn’t capture the full continuum of value across the Southeast.The methods of combining expert knowledge and data in this indicator may have caused some poorly known and/or under-surveyed areas to be scored too low.This indicator underprioritizes important reptile and amphibian habitat in Kentucky and Virginia because PARCAs have not yet been identified for these areas. ARC is working to expand PARCAs to more states in the future.Because of the state-by-state PARCA development and review process, sometimes PARCA boundaries stop at the state line, though suitable habitat for reptiles and amphibians does not always follow jurisdictional boundaries.This indicator excludes “protected” PARCAs maintained by ARC that are too small and spatially explicit to share publicly due to concerns about poaching. As a result, it underprioritizes some important reptile and amphibian habitat. However, these areas are, with a few exceptions in northwest Arkansas and Tennessee, generally well-represented in the Blueprint due to their value for other indicators.This indicator contains small gaps 1-2 pixels wide between some adjoining PARCAs that likely should be continuous, often on either side of a state line. These are represented in the source data as separate polygons with tiny gaps between them, and these translate into gaps in the resulting indicator raster. This results from the PARCA digitizing process and does not reflect meaningful differences in priority.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAmphibian and Reptile Conservancy. Priority Amphibian and Reptile Conservation Areas (PARCAs). Revised February 7, 2024. Apodaca, Joseph. 2013. Determining Priority Amphibian and Reptile Conservation Areas (PARCAs) in the South Atlantic landscape, and assessing their efficacy for cross-taxa conservation: Geographic Dataset. [https://www.sciencebase.gov/catalog/item/59e105a1e4b05fe04cd000df]. Barrett, Kyle, Nathan P. Nibbelink, John C. Maerz; Identifying Priority Species and Conservation Opportunities Under Future Climate Scenarios: Amphibians in a Biodiversity Hotspot. Journal of Fish and Wildlife Management 1 December 2014; 5 (2): 282–297. [https://doi.org/10.3996/022014-JFWM-015]. Conservation International, International Union for the Conservation of Nature, NatureServe. 2004. Global Amphibian Assessment Factsheet. [https://www.natureserve.org/sites/default/files/amphibian_fact_sheet.pdf]. Environmental Protection Agency. 2014. Mean Amphibian Species Richness: Southeast. EnviroAtlas Factsheet. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/MeanAmphibianSpeciesRichness.pdf]. Erwin, K. J., Chandler, H. C., Palis, J. G., Gorman, T. A., & Haas, C. A. (2016). Herpetofaunal Communities in Ephemeral Wetlands Embedded within Longleaf Pine Flatwoods of the Gulf Coastal Plain. Southeastern Naturalist, 15(3), 431–447. [https://www.jstor.org/stable/26454722]. Sutherland and deMaynadier. 2012. Model Criteria and Implementation Guidance for a Priority Amphibian and Reptile Conservation Area (PARCA) System in the USA. Partners in Amphibian and Reptile Conservation, Technical Publication PARCA-1. 28 pp. [https://parcplace.org/wp-content/uploads/2017/08/PARCA_System_Criteria_and_Implementation_Guidance_FINAL.pdf]. U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. TIGER/Line Shapefile, 2023, U.S. Current State and Equivalent National. 2023. [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html].