Bottom classification based on ArcGIS 10.2 maximum likelihood raster classification, converted from raster to polygon.Data collected using QTC (Quester Tangent Corporation) bottom classification technology which collects and analyzes acoustic returns, and provides a framework for classifying the seabed into different regions based on their acoustic characteristics. Data is then post processed using QTC IMPACT software. These data were collected and processed in 2004.Equipment:Knudsen 320BP Dual Frequency Echosounder ( 28kHz/200kHz)Quester Tangent QTC View Seabed Classification SystemQuester Tangent QTC Impact Post-Processing Funding to compile these datasets provided by BOEM under cooperative agreement number: M14AC00007. Data processing and compilation was executed by Maryland Geological Survey.The views expressed herein are those of the authors and do not necessarily reflect the views of the Bureau of Ocean Energy Management (BOEM) or any of its sub-agencies. This geodatabase was created to provide planners and managers access to data about aggregate resources off the coast of Maryland. This geodatabase should not be used for navigational purposes.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_OffshoreOceanResources/FeatureServer/5

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The products are designed with the goal of facilitating ecologically-based natural resources management and research. The vector (polygon) map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and allows much of the data to be accessed concurrently. Each map unit has multiple photo attachments viewable easily from within the geodatabase, linked to their actual location on the ground. The Geographic Information System (GIS) format of the map allows user flexibility and will also enable updates to be made as new information becomes available (such as revised NVC codes or vegetation type names) or in the event of major disturbance events that could impact the vegetation. Unlike previous vegetation maps created by SODN, the map for Saguaro National Park was not created via in-situ mapping. Instead, we employed a remote sensing approach aided by our robust field dataset. The final version of the map was created in summer 2016. The map was created using the image-classification toolbox included in the spatial analyst extension for ArcMap (ESRI 2017). Using these tools, we performed a supervised classification with the maximum-likelihood classifier. This tool uses a set of user-defined training samples (polygons) to classify imagery by placing pixels with the maximum likelihood into each map class. We used a pixel size equivalent to the coarsest raster included in the classification, 30 meters.

The field surveys were conducted for NOAA by SAIC on board M/V Atlantic Surveyor. Full reports and associated files are available for download at https://data.noaa.gov/dataset. Sheet H11649 was surveyed from August 2007 to November 2007. Sheet H11650 was surveyed from September 2007 to November 2007. Sheet H11872 was surveyed from July 2008 to December 2008. Sheet H11873 was surveyed from October 2008 to December 2008. Bottom class polygons with CMECS classifications based on ARC GIS 10.2 maximum likelihood raster classification performed on compiled side scan sonar mosaic raster. Side scan data collected by SAIC and provided by NOAA. Funding to compile these datasets provided by BOEM under cooperative agreement number: M14AC00007. Data processing and compilation was executed by Maryland Geological Survey.The views expressed herein are those of the authors and do not necessarily reflect the views of the Bureau of Ocean Energy Management (BOEM) or any of its sub-agencies. This geodatabase was created to provide planners and managers access to data about aggregate resources off the coast of Maryland. This geodatabase should not be used for navigational purposes.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://geodata.md.gov/imap/rest/services/Geoscientific/MD_OffshoreOceanResources/FeatureServer/6

Bottom classification based on ArcGIS 10.2 maximum likelihood raster classification, converted from raster to polygon. Data collected using QTC (Quester Tangent Corporation) bottom classification technology which collects and analyzes acoustic returns, and provides a framework for classifying the seabed into different regions based on their acoustic characteristics. Data is then post processed using QTC IMPACT software. These data were collected and processed in 2004. Equipment: Knudsen 320BP Dual Frequency Echosounder ( 28kHz/200kHz) Quester Tangent QTC View Seabed Classification System Quester Tangent QTC Impact Post-Processing

Funding to compile these datasets provided by BOEM under cooperative agreement number: M14AC00007. Data processing and compilation was executed by Maryland Geological Survey.

This image classification of forest cover in the MAV was created using Google Dynamic World (https://www.nature.com/articles/s41597-022-01307-4 - https://dynamicworld.app/) to determine what was classified as forest. This dataset is a result of an automated land classification for every Sentinel image that is released. The code used for this process is as follows. ee.ImageCollection('GOOGLE/DYNAMICWORLD/V1') \ .filterBounds(geometry) \ .filterDate(oldstartDate, oldendDate) \ .select('label') \ .mode() \ .eq(1) \ .updateMask(urban) We selected the Dynamic World dataset and filtered by our area of interest by the extents of the Lower Mississippi Joint Venture boundary (i.e. Mississippi Alluvial Valley and West Gulf Coastal Plain ecological bird conservation regions (BCRs).We filtered the dataset based on a start and end date which is the first of 2021 and the last day of 2021.With this dataset each class has a band that represents probability of that pixel having complete coverage of that class (https://developers.google.com/earth-engine/datasets/catalog/GOOGLE_DYNAMICWORLD_V1#bands)Data accuracy was assessed at @82% accuracy and data resolution is 10m. Each image has a ‘label’ band with a discrete classification of LULC, but also 9 probability bands with class-specific probability scores generated by the deep learning model on the basis of the pixel’s spatial context. To generate an annual LULC composite comparable with WC and Esri, we calculated the mode of the predicted LULC class in the ‘label’ band of all DW images for 2020.Michael Mitchell with Ducks Unlimited Southern Regional Office led the development of this effort, in coordination and collaboration with Lower Mississippi Valley Joint Venture staff.

The division designation of "special use lands" is for the protection of scenic, historic, archeological, scientific, biological, recreational, or other special resource values warranting additional protections or other special requirements. Special use land designations originate from an area or management plan, or are made at the director's discretion to address a certain need. Before a designation is made, however, other agencies and the public are given a chance to comment on the proposal. This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Special Use Land category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction. Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://www.dnr.state.ak.us/las/LASMenu.cfm Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

The data involved in this paper is from https://www.planet.com/explorer/. The resolution is 3m, and there are 3 main bands, RGB. Since the platform can only download a certain amount of data after applying for an account in the form of education, and at the same time the data is only retained for one month, we chose 8 major cities for the study, 2 images per city. we also provide detailed information on the data visualization and classification results that we have tested and retained in a PPT file called paper, we also provide detailed information on the data visualization and classification results of our tests in a PPT file called paper-result, which can be easily reviewed by reviewers. At the same time, reviewers can also download the data to verify the applicability of the results based on the coordinates of the data sources provided in this paper.The algorithms consist of three main types, one is based on traditional algorithms including object-based and pixel-based, in which we tested the generalization ability of four classifiers, including Random Forest, Support Vector Machine, Maximum Likelihood, and K-mean, in the form of classification in this different way. In addition, we tested two of the more mainstream deep learning classification algorithms, U-net and deeplabV3, both of which can be found and applied in the ArcGIS pro software. The traditional algorithms can be found by checking https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/the-image-classification-wizard.htm to find the running process, while the related parameter settings and Sample selection rules can be found in detail in the article. Deep learning algorithms can be found at https://pro.arcgis.com/en/pro-app/latest/help/analysis/deep-learning/deep-learning-in-arcgis-pro.htm, and the related parameter settings and sample selection rules can be found in detail in the article. Finally, the big model is based on the SAM model, in which the running process of SAM is from https://github.com/facebookresearch/segment-anything, and you can also use the official Meta segmentation official website to provide a web-based segmentation platform for testing https:// segment-anything.com/. However, the official website has restrictions on the format of the data and the scope of processing.

The division designation of "special use lands" is for the protection of scenic, historic, archeological, scientific, biological, recreational, or other special resource values warranting additional protections or other special requirements. Special use land designations originate from an area or management plan, or are made at the director's discretion to address a certain need. Before a designation is made, however, other agencies and the public are given a chance to comment on the proposal.

This shape file characterizes the geographic representation of land parcels within the State of Alaska contained by the Special Use Land category. It has been extracted from data sets used to produce the State status plats. This data set includes cases noted on the digital status plats up to one day prior to data extraction.

Each feature has an associated attribute record, including a Land Administration System (LAS) file-type and file-number which serves as an index to related LAS case-file information. Additional LAS case-file and customer information may be obtained at: http://dnr.alaska.gov/projects/las/ Those requiring more information regarding State land records should contact the Alaska Department of Natural Resources Public Information Center directly.

Wildfire hazard potential (WHP) is an index that depicts the relative potential for wildfire that would be difficult for suppression resources to contain, based on wildfire simulation modeling. This dataset produced by the USDA Forest Service, Fire Modeling Institute in 2020 shows WHP at a spatial resolution of 270 meters across the entire conterminous United States, classified into five WHP classes of very low, low, moderate, high, and very high. Areas mapped with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions, based primarily on 2014 landscape conditions. This WHP dataset is based on outputs of wildfire simulation modeling published in 2020.Starting with the 2020 version, the WHP dataset is integrated with the Wildfire Risk to Communities project. The 2020 dataset is the first version to include Alaska and Hawaii. There is a spatially-refined, 30-m resolution version of the WHP as part of the downloadable Wildfire Risk to Communities data, and related datasets that depict other components of wildfire hazard and risk to homes.This 2020 version supersedes all previous versions of Wildfire Hazard Potential (2018, 2014) or Wildland Fire Potential (2012, 2010, 2007). We generally do not advise direct comparisons between versions because changes can reflect improvements in methodology at all stages of the WHP calculation in addition to actual land cover changes.For more information and to download the raster data, please visit the Wildfire Hazard Potential website.Map author: Greg Dillon, USDA Forest Service, Rocky Mountain Research Station, Fire Modeling InstituteThis 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.

The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. Utilizing multivariate statistical analysis of X-ray maps, these methods effectively extract quantitative textural, chemical, and modal parameters from selected thin sections or micro-domains in volcanic samples whose constituents can show peculiar textures due to the magmatic processes involved. In this study, we have processed X-ray maps of major elements from the 2021 basaltic lava rocks of Pacaya volcano (Guatemala) through the Quantitative X-ray Map Analyzer (Q-XRMA) software. The processing strategy is based on the sequential application of the Principal Components Analysis and the supervised Maximum Likelihood Classification algorithms that allow us distinguishing among rock constituents (mineral phases, vesicles and glasses), quantifying their modal abundances, and identifying textural and chemical variations in a simplified and quick process. Here, the capability of the software has been applied to plagioclase crystals, whose textural and chemical complexities are faithful recorders of the physical and chemical conditions and processes controlling the evolution of the magmatic system. Plagioclase displays a variable extent of disequilibrium at the core and rim, as well as growth textures developed at different degrees of undercooling. This variability makes it very difficult to establish how many crystal populations are present in a sample, and to objectively decide whether there are crystals that can be considered representative of a population. The procedure applied in this study has proved to be effective for rapidly gathering chemical and textural data on plagioclase, and quantitatively document the distribution of crystals according to their size, shape, and compositions. Results demonstrate that the chemical and textural variability of crystals can be fully discerned at microscopic scale, and thus it can be adopted as a template for interpretation of magmatic processes.

These data are high-resolution maximum likelihood classification of the seafloor offshore of Massachusetts, from Nahant to Gloucester. Approximately 127 km² of the inner shelf were mapped in the nearshore region between the 10m and 40-m isobath.

To better understand factors potentially contributing to the occurrence of rainfall-induced landslides in Puerto Rico, we evaluated the locations of landslides there following Hurricane Maria (Hughes et al., 2019) and potential contributing factors. This data release provides results of evaluations of landslide locations compared to soil classification and land cover, which involved frequency-ratio analyses (for example, Lee and Pradhan, 2006; Lee et al., 2007; He and Beighley, 2008; Lepore et al., 2012; Chalkias et al., 2014). Soil classification data were obtained from the U.S. Department of Agriculture Natural Resources Conservation Service (2018) and land cover data were obtained from the Puerto Rico Gap Analysis Program (Gould et al., 2008). The data presented herewith were produced during a study described in Hughes, K.S., and Schulz, W.H., ####, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-file Report #####. Three files are included with this data release. Data files soil_classification_results.csv and land_cover_results.csv provide results of the analyses of landslide locations compared to soil classification and land cover, respectively. A read-me file (readme.txt) provides the information contained in this summary and additional description of data available from the data files. References Chalkias, C., Kalogirou, S., and Ferntinou, M., 2014, Landslide susceptibility, Peloponnese Peninsula in South Greece: Journal of Maps, v. 10, no. 2, p. 211-222. Gould, W.A., Alarcón, C., Fevold, B., Jiménez, M.E., Martinuzzi, S., Potts, G., Quiñones, M., Solórzano, M., and Ventosa, E., 2008, The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. https://www.sciencebase.gov/catalog/item/560c3b2de4b058f706e5411e. Last accessed 12 September 2019. He, Y., and Beighley, R.E., 2008, GIS‐based regional landslide susceptibility mapping: a case study in southern California: Earth Surface Processes and Landforms, v. 33, no. 3, p. 380-393. Hughes, K.S., Bayouth García, D., Martínez Milian, G.O., Schulz, W.H., and Baum, R.L., 2019, Map of slope-failure locations in Puerto Rico after Hurricane María: U.S. Geological Survey data release: https://doi.org/10.5066/P9BVMD74. https://www.sciencebase.gov/catalog/item/5d4c8b26e4b01d82ce8dfeb0. Last accessed 12 September 2019. Lee, S., and Pradhan, B., 2006, Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia: Journal of Earth System Science, v. 115, no. 6, p. 661-672. Lee, S., Ryu, J-H., and Kim, I-S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslides v. 4, p. 327–338. Lepore, C., Kamal, S.A., Shanahan, P., and Bras, R.L., 2012, Rainfall-induced landslide susceptibility zonation of Puerto Rico: Environmental Earth Sciences, v. 66, p. 1667-1681. U.S. Department of Agriculture Natural Resources Conservation Service, 2018, Soil Survey Geographic (SSURGO) database for Puerto Rico, all regions: https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. Last accessed 12 September 2019.

This .zip file contains four products that will allow users to recreate the analyses and spatial data figures used in Sergeant et al. 2020, A classification of streamflow patterns across the coastal Gulf of Alaska: 1) Autoclass input and output files (provided as folders with multiple simple text files), 2) Classification data (.csv file) for individual watersheds, including Fundamental Daily Streamflow Statistics, landcover variables, and class membership, 3) Esri map package (.mpk file) that will allow users to recreate Figures 3 and 5 using ArcGIS and extract basic watershed-scale data such as watershed ID, drainage area, primary class assignment, and primary class membership probability (for users interested in extracting more information such as secondary class membership and landcover data, Watershed ID in this file can be cross-referenced with Watershed ID in the .csv file above), and 4) R Shiny Application (provided as a folder with app.R file and other supporting files) to create Figure 3 map and extract classification and land cover variables for individual watersheds (this is also viewable at: https://southeastakwatershedcoalition.shinyapps.io/watershed_classification/)

This .zip file contains four products that will allow users to recreate the analyses and spatial data figures used in Sergeant et al. 2020, A classification of streamflow patterns across the coastal Gulf of Alaska: 1) Autoclass input and output files (provided as folders with multiple simple text files), 2) Classification data (.csv file) for individual watersheds, including Fundamental Daily Streamflow Statistics, landcover variables, and class membership, 3) Esri map package (.mpk file) that will allow users to recreate Figures 3 and 5 using ArcGIS and extract basic watershed-scale data such as watershed ID, drainage area, primary class assignment, and primary class membership probability (for users interested in extracting more information such as secondary class membership and landcover data, Watershed ID in this file can be cross-referenced with Watershed ID in the .csv file above), and 4) R Shiny Application (provided as a folder with app.R file and other supporting files) to create Figure 3 map and extract classification and land cover variables for individual watersheds (this is also viewable at: https://southeastakwatershedcoalition.shinyapps.io/watershed_classification/)

This dataset is the 2023 version of wildfire hazard potential (WHP) for the United States. The files included in this data publication represent an update to any previous versions of WHP or wildland fire potential (WFP) published by the USDA Forest Service. WHP is an index that quantifies the relative potential for high-intensity wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed. This 2023 version of WHP was created from updated national wildfire hazard datasets of annual burn probability and fire intensity generated by the USDA Forest Service, Rocky Mountain Research Station with the large fire simulation system (FSim). Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were the primary inputs to the updated FSim modeling work and therefore form the foundation for this version of the WHP. As such, the data presented here reflect landscape conditions as of the end of 2020. LANDFIRE 2020 vegetation and fuels data were also used directly in the WHP mapping process, along with updated point locations of fire occurrence ca. 1992-2020. With these datasets as inputs, we produced an index of WHP for all of the conterminous United States at 270-meter resolution. We present the final WHP map in two forms: 1) continuous integer values, and 2) five WHP classes of very low, low, moderate, high, and very high. On its own, WHP is not an explicit map of wildfire threat or risk, but when paired with spatial data depicting highly valued resources and assets such as structures or powerlines, it can approximate relative wildfire risk to those specific resources and assets. WHP is also not a forecast or wildfire outlook for any particular season, as it does not include any information on current or forecasted weather or fuel moisture conditions. It is instead intended for long-term strategic fuels management.

Updated habitat map resulting from an integrated analysis of the dedicated 2012 survey data (CEND3/12b) for South East of Falmouth rMCZ. A new habitat map for the site was produced by analysing and interpreting the available acoustic data and the groundtruth data collected by the dedicated survey of this site. The process is a combination of two approaches, auto-classification (image analysis) and expert interpretation, as described below. The routine for auto-classification is flexible and dependent on site-specific data, allowing for application of a bespoke routine to maximise the acoustic data available. ArcGIS was used to perform an initial unsupervised classification on the supplied backscatter image. The single band backscatter mosaic was filtered and smoothed prior to the application of an Iso cluster/maximum likelihood classification routine. For further information, refer to the South-East Falmouth rMCZ Post-survey Site Report vs. 8 (Green, S. & Cooper, R., 2015).

Updated habitat map resulting from an integrated analysis of the 2013 dedicated survey data (collected on cruise CEND0613) for South-West Deeps (West) recommended Marine Conservation Zone (rMCZ).

A new habitat map for the site was produced by analysing and interpreting the available acoustic data and the ground-truth data collected by the dedicated surveys of South West Deeps West rMCZ. The process is a combination of two approaches, auto-classification (image analysis) and expert interpretation. The routine for auto-classification is flexible and dependent on site-specific data, allowing for application of a bespoke routine to maximise the acoustic data available. ArcGIS was used to perform an initial unsupervised classification on the supplied backscatter image. The single band backscatter mosaic was filtered and smoothed prior to the application of an Iso cluster/maximum likelihood classification routine. For further information, refer to the South-West Deeps (West) rMCZ Post-survey Site Report.

From NOAA:This map service displays the probability rating which covers the landcover mapping continuum of wetness from dry to water using 2010 land cover data. The layer is not a wetland classification but provides the wetland likelihood at a specific location. The rating was developed through a modelling process combining multiple GIS and remote sensing data sets including soil characteristics, elevation, existing wetland inventories, hydrographical extents and satellite imagery. The Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Map Services Notification Group (http://www.coast.noaa.gov/digitalcoast/publications/subscribe).

Evaluation of watersheds and development of a management strategy require accurate measurement of the past and present land cover/land use parameters as changes observed in these parameters determine the hydrological and ecological processes taking place in a watershed. This study applied supervised classification-maximum likelihood algorithm in ERDAS imagine to detect land cover/land use changes observed in Simly watershed, Pakistan using multispectral satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 respectively. The watershed was classified into five major land cover/use classes viz. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water. Resultant land cover/land use and overlay maps generated in ArcGIS 10 indicated a significant shift from Vegetation and Water cover to Agriculture, Bare soil/rock and Settlements cover, which shrank by 38.2% and 74.3% respectively. These land cover/use transformations posed a serious threat to watershed resources. Hence, proper management of the watershed is required or else these resources will soon be lost and no longer be able to play their role in socio-economic development of the area.