The Remote Sensing Division is responsible for providing data to support the Coastal Mapping Program, Emergency Response efforts, and the Aeronautical Survey Program through the use of remotely sensed data. NOAA Coastal Mapping Remote Sensing Data includes metric-quality aerial photographs from film and digital cameras, orthomosaics, and Light Detection and Ranging (lidar). The predecessors to...

This dataset is regional maps of natural grasslands, grassy woodlands and derived grassland of the Monaro region based on multi-temporal satellite imagery.

This dataset represents a high resolution urban land cover classification map across the southern California Air Basin (SoCAB) with a spatial resolution of 60 cm in urban regions and 10 m in non-urban regions. This map was developed to support NASA JPL-based urban biospheric CO2 modeling in Los Angeles, CA. Land cover classification was derived from a novel fusion of Sentinel-2 (10-60 m x 10-60 m) and 2016 NAIP (60 cm x 60 cm) imagery and provides identification of impervious surface, non-photosynthetic vegetation, shrub, tree, grass, pools and lakes.

Land Cover Classes in .tif file: 0: Impervious surface 1: Tree (mixed evergreen/deciduous) 2: Grass (assumed irrigated) 3: Shrub 4: Non-photosynthetic vegetation 5: Water (masked using MNDWI/NDWI)

Google Earth Engine interactive app displaying this map: https://wcoleman.users.earthengine.app/view/socab-irrigated-classification

A portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Support from the Earth Science Division OCO-2 program is acknowledged. Copyright 2020. All rights reserved.

Data Description

This dataset is linked to the publication "Recursive classification of satellite imaging time-series: An application to land cover mapping". In this paper, we introduce the recursive Bayesian classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. To reproduce the results presented in the paper, the RBC-SatImg folder and the code in the GitHub repository RBC-SatImg are required.

The RBC-SatImg folder contains:

• Sentinel-2 time-series imagery from three key regions: Oroville Dam (CA, USA) and Charles River (Boston, MA, USA) for water mapping, and the Amazon Rainforest (Brazil) for deforestation detection.
• The RBC-WatData dataset with manually generated water mapping labels for the Oroville Dam and Charles River regions. This dataset is well-suited for multitemporal land cover and water mapping research, as it accounts for the dynamic evolution of true class labels over time.
• Pickle files with output to reproduce the results in the paper, including:
  • Instantaneous classification results for GMM, LR, SIC, WN, DWM
  • Posterior results obtained with the RBC framework

The Sentinel-2 images and forest labels used in the deforestation detection experiment for the Amazon Rainforest have been obtained from the MultiEarth Challenge dataset.

Folder Structure

The following paths can be changed in the configuration file from the GitHub repository as desired. The RBC-SatImg is organized as follows:

• `./log/` (EMPTY): Default path for storing log files generated during code execution.
• `./evaluation_results/`: Contains the results to reproduce the findings in the paper, including two sub-folders:
  • `./classification/`: For each test site, four sub-folders are included as:
    • `./accuracy/`: Each sub-folder corresponding to an experimental configuration contains pickle files with balanced classification accuracy results and information about the models. The default configuration used in the paper is "conf_00."
    • `./figures/`: Includes result figures from the manuscript in SVG format.
    • `./likelihoods/`: Contains pickle files with instantaneous classification results.
    • `./posteriors/`: Contains pickle files with posterior results generated by the RBC framework.
  • `./sensitivity_analysis/`: Contains sensitivity analysis results, organized by different test sites and epsilon values.
• `./Sentinel2_data/`: Contains Sentinel-2 images used for training and evaluation, organized by scenarios (Oroville Dam, Charles River, Amazon Rainforest). Selected images have been filtered and processed as explained in the manuscript. The Amazon Rainforest images and labels have been obtained from the MultiEarth dataset, and consequently, the labels are included in this folder instead of the RBC-WatData folder.
• `./RBC-WatData/`: Contains the water labels that we manually generated with the LabelStudio tool.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Helmer, E. H., Ramos, O., del MLopez, T., Quinonez, M., and Diaz, W. (2002). Mapping the forest type and Land Cover of Puerto Rico, a component of the Caribbean biodiversity hotspot. Caribbean Journal of Science, (Vol. 38, Issue 3/4, pp. 165-183)Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J. (2023). Comprehensive Quality Assessment of Optical Satellite Imagery Using Weakly Supervised Video Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2124-2134)Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual Land Cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261Pesaresi, M. and Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EAStehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. In International Journal of Remote Sensing (Vol. 35, pp. 4923-4939). https://doi.org/10.1080/01431161.2014.930207USDA National Agricultural Statistics Service Cropland Data Layer (2023). Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed 2024). USDA-NASS, Washington, DC.U.S. Geological Survey (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mU.S. Geological Survey (2023). Landsat Collection 2 Known Issues, accessed March 2023 at https://www.usgs.gov/landsat-missions/landsat-collection-2-known-issuesWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAYang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M., and Xian, G. (2018). A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies (https://www.sciencedirect.com/science/article/abs/pii/S092427161830251X), (pp. 108-123)Zhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of Land Cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011 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.

Abstract Among the various characteristics of the Brazilian territory, one is foremost: the country has the second largest forest reserve on the planet, accounting for approximately 10% of the total recorded global forest formations. In this scenario, seasonally dry tropical forests (SDTF) are the second smallest forest type in Brazil, located predominantly in non-forested biomes, such as the Cerrado and Caatinga. Consequently, correct identification is fundamental to their conservation, which is hampered as SDTF areas are generally classified as other types of vegetation. Therefore, this research aimed to monitor the Land Use and Coverage in 2007 and 2016 in the continuous strip from the North of Minas Gerais to the South of Piauí, to diagnose the current situation of Brazilian deciduous forests and verify the chief agents that affect its deforestation and regeneration. Our findings were that the significant increase in cultivated areas and the spatial mobility of pastures contributed decisively to the changes presented by plant formations. However, these drivers played different roles in the losses/gains. In particular, it was concluded that the changes occurring to deciduous forests are particularly explained by pastured areas. The other vegetation types were equally impacted by this class, but with a more incisive participation of cultivation.

We (Intelligent Mining and Analysis of Remote Sensing big data, IMARS) create a large-scale annotated dataset (Globe230k) for land use/land cover (LULC) mapping, which is annotated on Google Earth image of 1 m spatial resolution. Globe230k is annotated by numerous experts and students major in survey and mapping after necessary training, through visual interpretation on very high-resolution images, as well as in-situ field survey, under the guidance of the organized annotation pipeline. Globe230k has three superiorities: 1) Large scale: the Globe230k includes 232,819 annotated images with the size of 512x512 and spatial resolution of 1 m, with more than 3x1010 annotated pixels, and it includes 10 first-level categories. 2) Rich diversity: the annotated images are sampled from worldwide regions, with coverage area of over 60,000 km2, indicating a high variability and diversity. Besides, in order to ensure the category balance, we intentionally give more chance to the rare categories to be sampled, such as wetland, ice/snow, etc. 3) Multi-modal: Globe230k not only contains RGB bands, but also include other important features for Earth system research, such as Normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical-vertical polarization (VV) bands, vertical-horizontal polarization (VH) bands, which can facilitate the multi-modal data fusion research.(This part will updating soon). The image patches and their corresponding annotated patches are respectively stored in "patch_image.rar" and "patch_label.rar" file. The RGB image is in forms of ".jpg", with size of 512x512, the pixel value is ranged from 0-255. The annotated patches is in forms of ".png", also with size of 512x512, the pixel value is ranged from 1-10, which respectively represent 1#cropland, 2#forest, 3#grass, 4#shrubland, 5#wetland, 6#water, 7#tundra, 8#impervious, 9#bareland, 10#ice/snow. The total 232,819 pairs are officially divided into training set, validation set, and test set, based on ratio of 7:1:2, which can be find in "train.txt","val.txt","test.txt" file. Based on this division, the official baseline accuracy of several state-of-the-art semantic segmentation can be found in the related arcticle (https://spj.science.org/doi/10.34133/remotesensing.0078). We hope it can be used as a benchmark to promote further development of global land cover mapping and semantic segmentation algorithm development.

The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific Child Page is to document a series of derived remote sensing products for turbidity and fluorescent dissolved organic matter (fDOM) based on Sentinel 2 (S2) A/B Multispectral Imager (MSI) imagery acquired between June 1, 2019 and May 31, 2021 for the SSJRD. These remote sensing products were developed using S2 A/B Level 1C input data with less than 25% cloud cover over the SSJRD. Each image in the archive was atmospherically corrected to Level 2 remote sensing reflectance with the open source ACOLITE software package. The turbidity and fDOM products were developed using machine learning to generate SSJRD – specific models based on S2 A/B remote sensing reflectance and in situ measurements collected at USGS continuous monitoring stations. The specific products presented herein consists of 154 Geographic Tagged Image File Format (GeoTIFF) files, with one folder of 77 turbidity files and one folder of 77 fDOM files. Each GeoTIFF file has the following naming convention: AA_BBBBBB_yyyy_mm_dd_CCCCCC_xxxx.tif, where AA indicates the sensor (S2) that acquired the data, BBBBBB indicates the tile identifying the remote sensing image used, yyyy_mm_dd indicates the year, month and day that the image was acquired, CCCCCC indicates the spatial area (SFBDelta) and xxxx indicates the water quality parameter (turbidity or fDOM).

This dataset provides monthly global land surface water mapping using water indices derived from Earth observation satellite data. It was generated using imagery from two satellite missions, Landsat-8 and Sentinel-2. The dataset includes three water indices: Normalized Difference Water Index (NDWI2), Modified Normalized Difference Water Index (MNDWI), and Water Index 2015 (WI2015).

A pixel was classified as water if the average index value within each month exceeded 0, and as non-water otherwise. The dataset has a spatial resolution of 300 m, covering the period from January 2019 to December 2021. Each dataset is provided in GeoTIFF format, which can be visualized and analyzed using GIS software. This dataset can be used for hydrological studies, flood monitoring, and water resource management.

Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground. Upon completion, students will be able to: Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.

Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colorado and the Four Corners Region of the United States (Rockwell and others, 2021). Spectral index (e.g. band-ratios) results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These results may indicate the presence of near-surface pyrite, which can be a potential non-point source of acid rock drainage. Map data, in ERDAS IMAGINE (.img) thematic raster format, represent pixel values with mineral and vegetation group classifications, and can be queried in most image processing and GIS software packages. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improve ...

description: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.; abstract: The Ouray National Wildlife Refuge (ONWR) was established in 1960 as an inviolate sanctuary for migratory birds and any other management purpose. In 2000, the Refuge published a Comprehensive Conservation Plan in accordance with the 1997 National Wildlife Refuge Improvement Act. The plan shifted the Refuge s emphasis toward ecosystem-based management of all resident and migratory species. Refuge and Regional staff asked that a detailed and accurate vegetation map be developed for planning and for managing the Refuge effectively. The Bureau of Reclamation s Remote Sensing and Geographic Information Group (RSGIS) was contracted by US Fish and Wildlife Service to map vegetation and land-use classes at ONWR using remote sensing and GIS technologies originally developed for the National Park Service s Vegetation Mapping Program. The diverse vegetation and complicated land-use history of Ouray National Wildlife Refuge presented a unique challenge to mapping vegetation at the plant association level of the US National Vegetation Classification. To meet this challenge, the project consisted of two linked phases: (1) vegetation classification and (2) digital vegetation map production. To classify the vegetation, we sampled representative plots located throughout the 14,025-acre (5676 ha) project area. Analysis of the plot data using ordination and clustering techniques yielded 58 distinct plant associations. To produce the digital map, we used a combination of new color-infrared aerial photography and fieldwork to interpret the complex patterns of vegetation and land-use at ONWR. Eighty-one map units were developed and the vegetation units matched to the corresponding plant associations. The interpreted map data were converted to a GIS database using ArcInfo. Draft maps created from the vegetation classification were field-tested and revised before an independent ecologist conducted an assessment of the map s accuracy. The accuracy assessment revealed an overall database accuracy of 75.2%. Products developed for the Ouray National Wildlife Refuge Vegetation Mapping Project include the final report, vegetation key, map accuracy assessment results and contingency table, and photo interpretation key; spatial database coverages of the vegetation map, vegetation plots, accuracy assessment sites, and flight line index; digital photos (scanned from 35mm slides) of each vegetation type; graphics of all spatial database coverages; Federal Geographic Data Committee-compliant metadata for all spatial database coverages and field data. 12 In addition, the Refuge and USFWS copies of this report contain original aerial photographs of the project area; digital data files and hard copy data sheets of the observation points, vegetation field plots, and accuracy assessment sites; original slides of each vegetation type.

Supporting information for: REMAP: An online remote sensing application for land cover classification and monitoringcsv and json files for implementing land cover classifications using the remap, the remote ecosystem assessment and monitoring pipeline (https://remap-app.org/)Nearmap aerial photograph courtesy of Nearmap Pty Ltd.For further information see:Murray, N.J., Keith, D.A., Simpson, D., Wilshire, J.H., Lucas, R.M. (accepted) REMAP: A cloud-based remote sensing application for generalized ecosystem classifications. Methods in Ecology and Evolution.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

In response to the necessity for enhanced wetland inventories, this study aimed to assess effectiveness of employing high spatial resolution SPOT imagery (1.5m resolution by Planet Lab) for classifying and detecting wetlands in Atlin, British Columbia, Canada. Utilizing the Random Forest Classifier, featured for its capability in handling high-dimensional spatial data, the research aims to contribute to the local understanding of wetland status through advanced raster analysis. The application of the Random Forest Classifier yielded an overall classification accuracy of 86%, underscoring the method's applicability for wetland delineation in Atlin. The generated wetland map, featuring a 10m spatial resolution, integrates topographic, vegetative, and textural indices, presenting a valuable tool for assessing the variable importance in wetland classification. Despite its high accuracy, the study acknowledges the irreplaceable value of field assessments for comprehensive wetland evaluation by ecological uniqueness of wetlands. This research not only demonstrates the potential of high-resolution SPOT imagery in environmental monitoring but also encourages further application of machine learning techniques in the preservation and management of critical wetland ecosystems.

Considerable work has been carried out both in Australia and overseas on the spatial variations in recharge and discharge. This work usually relates point measurements and modelled recharge to spatial units such as land management units or soil units. There is also a considerable literature on the use of remote sensing and geographic information systems in mapping hydrological features including recharge and discharge zones.This report brings together much of this work. It describes the various data that can be used at a national or finer scale as inputs to GIS-based models for spatial distribution of recharge and discharge characteristics. These include remotely sensed satellite imagery and common remote sensing algorithms; digital elevation models; geophysical data (airborne electromagnetics, gamma-ray radiometrics); water table surface elevation mapping; climate; soil, regolith and geology; and vegetation and land cover. This report also describes various mapping frameworks that could be adapted to provide a spatial context for mapping the distribution of recharge-discharge characteristics including Atlas of Australian Soils, Hydrogeomorphic Units, Hydrogeological Landscapes and Groundwater Flow Systems.It is recommended that remote sensing and GIS methods be developed for mapping recharge and discharge zones usign a variety of data from a variety of sources. Each dataset needs to be characterised in terms of its resolution, availability and ease of use. When combined with companion reports that review recharge and discharge estimation studies in Australia, these reports will provide the basis for the subsequent phase of the project, aimed at demonstrating methodologies for recharge/discharge estimation in data poor areas.

The global digital mapping aerial photography market is experiencing robust growth, driven by increasing demand for high-resolution imagery across diverse sectors. The market size in 2025 is estimated at $2.5 billion, projecting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This expansion is fueled by several key factors. The surge in the adoption of unmanned aerial vehicles (UAVs or drones) for data acquisition offers cost-effective and efficient solutions compared to traditional manned aircraft. Furthermore, advancements in sensor technology, such as the development of higher-resolution cameras and LiDAR systems, are enabling the creation of more detailed and accurate maps. The increasing need for precise geospatial data in various applications, including urban planning, infrastructure development, precision agriculture, and environmental monitoring, significantly contributes to market growth. Government initiatives promoting the use of geospatial technologies and the growing adoption of cloud-based platforms for data processing and analysis also play crucial roles. However, several restraints influence market growth. High initial investment costs associated with acquiring advanced aerial photography equipment and specialized software can be a barrier to entry for smaller companies. Data processing and analysis can be complex and time-consuming, requiring skilled personnel and sophisticated software. Moreover, regulatory hurdles and safety concerns related to UAV operations in certain regions can hinder market expansion. Despite these challenges, the market's growth trajectory remains positive, with significant opportunities for players focusing on innovative solutions and expanding into new applications and geographical markets. Segmentation reveals strong growth across both linear and area array sensors, with the unmanned aircraft segment leading the charge due to its cost-effectiveness and flexibility.

The series of indian Remote sensing satellites like IRS-1A,IRS-1B,IRS-1C,IRS-1D,IRS-P4,IRS-P6,IRS-P5 with spatial resolution ranging from 360m to 2.5m and also with pancromatic and multispectral imaging capability,catering to the needs of the country in managing its natural resources. Today, IRS data is being used for a diverse range of applications such as crop acreage and production estimation of major crops, drought monitoring and assessment based on vegetation condition, flood risk zone mapping and flood damage assessment, hydro-geo-morphological maps for locating underground water resources, irrigation command area status monitoring, snowmelt run-off estimation, land use and land cover mapping, urban planning, biodiversity characterization, forest survey, wetland mapping, environmental impact analysis, mineral prospecting, coastal studies, integrated surveys for developing sustainable action plans and so on.

This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five categories of ground surface inundation; in addition to not-water (class 0) and water (class 1), the DSWE algorithm distinguishes pixels that are less distinctly inundated (class 2: “moderate confidence”), comprise a mixture of vegetation and water (class 3: “potential wetland”), or are of marginal validity (class 4: “water or wetland - low confidence”). Class 9 is applied to classify clouds, shadows and hill shade. Two additional documents accompany the raster image files and XML metadata. The first provides a key representing the general location of each raster file. The second file includes all Google Earth Engine Javascript code, which can be used online (https://code.earthengine.google.com/) to replicate the monthly DSWE map time series for Cambodia, or for any other location on Earth. The code block includes comments to explain how each step works. These data support the following publication: These data support the following publication: Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a Surface Water Extent Model in Cambodia using Cloud-Based Remote Sensing: Remote Sensing, v. 12, no. 6, p. 984, https://doi.org/10.3390/rs12060984.

A digital orthophoto is a georeferenced image prepared from aerial imagery, or other remotely-sensed data in which the displacement within the image due to sensor orientation and terrain relief has been removed. Orthophotos combine the characteristics of an image with the geometric qualities of a map. Orthoimages show ground features such as roads, buildings, and streams in their proper positions, without the distortion characteristic of unrectified aerial imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages, also known as orthomaps, can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols, whereas on an orthoimage all details appear just as in original aerial or satellite imagery.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 GeoServiceFor complete information, please visit https://data.gov.