1) Data Introduction • The Land-Use Scene Classification Dataset is an image dataset built to classify land-use types in different regions based on Landsat satellite imagery.

2) Data Utilization (1) Characteristics of the Land-Use Scene Classification Dataset: • The images are collected from a diverse range of geographic environments, including urban, rural, coastal, and forested areas, making the dataset suitable for evaluating domain generalization performance. • It is based on low-resolution Landsat satellite images, yet designed to effectively distinguish various terrain and structural patterns even with limited spatial resolution.

(2) Applications of the Land-Use Scene Classification Dataset: • Development of land-use classification models: The dataset can be used to train deep learning models that automatically classify land-use types such as residential areas, roads, and farmlands from satellite imagery. • GIS-based land-use change analysis: It can support geographic information system (GIS) research to analyze land-use pattern changes over time and infer spatial utilization trends.

This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats. Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format). Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices: As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq This dataset was created to depict approximate tree canopy cover for all land within the City of Austin's "full watershed regulation area." Intended for planning purposes and measuring citywide percent canopy. Definition: Tree canopy is defined as the layer of leaves, branches, and stems of trees that cover the ground when viewed from above. Methods: The 2022 tree canopy layer was derived from satellite imagery (Maxar) and aerial imagery (NAIP). Images were used to extract tree canopy into GIS vector features. First, a “visual recognition engine” generated the vector features. The engine used machine learning algorithms to detect and label image pixels as tree canopy. Then using prior knowledge of feature geometries, more modeling algorithms were used to predict and transform probability maps of labeled pixels into finished vector polygons depicting tree canopy. The resulting features were reviewed and edited through manual interpretation by GIS professionals. When appropriate, NAIP 2022 aerial imagery supplemented satellite images that had cloud cover, and a manual editing process made sure tree canopy represented 2022 conditions. Finally, an independent accuracy assessment was performed by the City of Austin and the Texas A&M Forest Service for quality assurance. GIS professionals assessed agreement between the tree canopy data and its source satellite imagery. An overall accuracy of 98% was found. Only 23 errors were found out of a total 1,000 locations reviewed. These were mostly omission errors (e.g. not including canopy in this dataset when canopy is shown in the satellite or aerial image). Best efforts were made to ensure ground-truth locations contained a tree on the ground. To ensure this, location data were used from City of Austin and Texas A&M Forest Service databases. Analysis: The City of Austin measures tree canopy using the calculation: acres of tree canopy divided by acres of land. The area of interest for the land acres is evaluated at the City of Austin's jurisdiction including Full Purpose, Limited Purpose, and Extraterritorial jurisdictions as of May 2023. New data show, in 2022, tree canopy covered 41% of the total land area within Austin's city limits (using city limit boundaries May 2023 and included in the download as layer name "city_of_austin_2023"). 160,046.50 canopy acres (2022) / 395,037.53 land acres = 40.51% ~41%. This compares to 36% last measured in 2018, and a historical average that’s also hovered around 36%. The time period between 2018 and 2022 saw a 5 percentage point change resulting in over 19K acres of canopy gained (estimated). Data Disclaimer: It's possible changes in percent canopy over the years is due to annexation and improved data methods (e.g. higher resolution imagery, AI, software used, etc.) in addition to actual in changes in tree canopy cover on the ground. For planning purposes only. Dataset does not account for individual trees, tree species nor any metric for tree canopy height. Tree canopy data is provided in vector GIS format housed in a Geodatabase. Download and unzip the folder to get started. Please note, errors may exist in this dataset due to the variation in species composition and land use found across the study area. This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by the City of Austin for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness. Data Provider: Ecopia AI Tech Corporation and PlanIT Geo, Inc. Data derived from Maxar Technologies, Inc. and USDA NAIP imagery

This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

The dataset used in this study is designed to facilitate the monitoring and detection of Harmful Algal Blooms (HABs) using Synthetic Aperture Radar (SAR) remote sensing and deep learning models. The dataset includes Sentinel-1 SAR C-band (TIF), Sentinel-2 MSI (TIF), and Water indices (TIF) that were utilized as input dataset in the deep learning model. The dataset used in this study originates from external sources and is not the property of the authors. If reused, proper attribution to the original sources is required in accordance with their respective citation guidelines. The authors have modified the dataset for research purposes.

A GIS dataset of around Cape Denison and part of George V land created from two IKONOS satellite images.
Layers created from digitising directly from the imagery include: mapping extent, continent, building, refuge, coastline, reef, offshore rocks, sea, snow, sheet, island, birds, rock, moraine, sea ice, lakes
- The mapping extent layer represents the edge of the IKONOS imagery.
- The continent layer represents the land mass shown in IKONOS imagery. It was generated using the digitised coastline and bounded by lines that represent the edge of the image.
- The snow spatial data represents the snow cover in January 2001
- The sheet ice spatial data represents the ice extent in January 2001
- The penguin spatial data represents the penguin colony extents, based on guano deposits.
- The rock spatial data represents the exposed bare rock

This dataset contains polygon features representing dust-impacted areas originating from eastern Iran and affecting parts of Southwest Asia between 2000 and 2023. Dust plumes were identified through visual interpretation of sub-daily MODIS Terra/Aqua true-color composite (RGB) imagery using NASA’s EOSDIS Worldview platform. On-screen digitizing in a Geographic Information System (GIS) environment was used to delineate the spatial extent of each dust event based on plume dispersion from identified dust hotspots. To enhance temporal accuracy, imagery from both Terra (~10:30 local time) and Aqua (~13:30 local time) satellites was used, improving detection during daylight hours. Spatial and temporal clustering of adjacent dust hotspots was performed to group them into individual dust events. For multi-day events, the largest visible extent, captured before plume dissipation, was selected as the final footprint. Each polygon in the dataset is linked to metadata attributes, including the date of occurrence, total affected area, and a unique event ID corresponding to the clustered dust hotspots. The data are provided in standard GIS-compatible formats and include spatiotemporal attributes suitable for environmental, climatological, and hazard-related analyses.

This six inch pixel resolution black and white aerial photography was flown April 22, 2000, at a negative scale of 1" = 833, flying at an altitude of 5000 feet. The files are provided in JPEG2000, an open format supported by most GIS and CAD software packages. Its intended usage for viewing is 1" = 100. The photography has been orthorectified to meet National Map Accuracy Standards for its capture scale. The images are georeferenced to the Illinois State Plane, Eastern Zone, using the NAD83 HARN horizontal datum. The data set is tiled for dissemination into many separate tiles. Each tile is a section in the Public Land Survey System. The first two digits are the township, the next two are the range and the final two are the section.

1) Data Introduction • The Satellite Images of Hurricane Damage Dataset is The Satellite Images of Hurricane Damage Dataset is a binary image classification computer vision dataset based on satellite images taken in Texas, USA, after Hurricane Harvey in 2017. Each image is labeled as either ‘damage’ (indicating structural damage) or ‘no_damage’ (indicating no damage), allowing for automatic identification of building damage in disaster scenarios.

2) Data Utilization (1) Characteristics of the Satellite Images of Hurricane Damage Dataset: • The dataset is composed of real satellite images taken immediately after a natural disaster, providing a realistic and reliable training environment for the development of automated disaster response and recovery systems.

(2) Applications of the Satellite Images of Hurricane Damage Dataset: • Development of disaster damage recognition models: This dataset can be used to train deep learning-based AI models that automatically classify whether buildings have been damaged based on satellite imagery. These models can contribute to decision-making in rescue prioritization and damage extent analysis. • Geospatial risk prediction systems: By integrating with GIS systems, the dataset can help visualize damage-prone areas on maps, supporting real-time decisions and resource allocation optimization during future disasters.

Dataset

This dataset was created by Rajaraman Ekambaram

Contents

It contains the following files:

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.

This app is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsThis layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireNote about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

This is a landing page. To access the datasets, expand the RELATED DATASETS section below, and follow the link to the dataset you require. \r \r --------------------------------------\r \r The Remote Sensing Organisational Unit as part of the Water Group, within the NSW Department of Climate Change, Energy, the Environment and Water (NSW DCCEEW) is dedicated to harnessing the power of satellite earth observations, aerial imagery, in-situ data, and advanced modelling techniques to produce cutting-edge remote sensing information products. Our team employs a multi-faceted approach, integrating remote sensing data captured by satellites operating at various temporal and spatial scales with on-the-ground observations and key spatial datasets, including land-use mapping, weather data, and ancillary verification datasets. This synthesis of diverse information sources enables us to derive critical insights that significantly contribute to water resource planning, policy formulation, and advancements in scientific research.\r \r Drawing upon satellite imagery from reputable sources such as NASA, the European Space Agency, and commercial providers like Planet and SPOT, our team places a special emphasis on leveraging Landsat and Sentinel satellite imagery. Renowned for their archived, calibrated, and consistent datasets, these sources provide a significant advantage in our pursuit of delivering accurate and reliable information. To ensure the robustness of our information products, we implement thorough validation processes, incorporating semi-automation techniques that facilitate rapid turnaround times.\r \r Our operational efficiency is further enhanced through strategic interventions in our workflows, including the automation of processes through efficient computing scripts and the utilization of Google Earth Engine for cloud computing. This integrated approach allows us to maintain high standards of data quality while meeting the increasing demand for timely and accurate information.\r \r Our commitment to providing high-quality, professional, and technically accurate Remote Sensing - Geographic Information System (RS-GIS) data packages, maps, and information is underscored by our recognition of the growing role of technology in information transfer and the promotion of information sharing. Moreover, our dedication to ensuring the currency of RS-GIS methods, interpretation techniques, and 3D modelling enables us to continually deliver innovative products that align with evolving client expectations. Through these efforts, our team strives to contribute meaningfully to the advancement of remote sensing applications for improved environmental understanding and informed decision-making.\r \r -----------------------------------\r \r Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.\r \r \r \r \r

This is a collection of Digital Surface Models and Highest Hit rasters covering selected U.S. Forest Service and adjoining lands in the Southwest Region, encompassing Arizona and New Mexico. The data are presented in a time-enabled format, allowing the end-user to view available data year-by-year, or all available years at once, within a GIS system. The data encompass varying years, varying resolutions, and varying geographic extents, dependent upon available data as provided by the region. DSM and Highest Hit rasters represent elevation of Earth's surface, including its natural and human-made features, such as vegetation and buildings.The data contains an attribute table. Notable attributes that may be of interest to an end-user are:lowps: the pixel size of the source raster, given in meters.highps: the pixel size of the top-most pyramid for the raster, given in meters.beginyear: the first year of data acquisition for an individual dataset.endyear: the final year of data acquisition for an individual dataset.dataset_name: the name of the individual dataset within the collection.metadata: A URL link to a file on IIPP's Portal containing metadata pertaining to an individual dataset within the image service.resolution: The pixel size of the source raster, given in meters.Terrain-related imagery are primarily derived from Lidar, stereoscopic aerial imagery, or Interferometric Synthetic Aperture Radar datasets. Consequently, these derivatives inherit the limitations and uncertainties of the parent sensor and platform and the processing techniques used to produce the imagery. The terrain images are orthographic; they have been georeferenced and displacement due to sensor orientation and topography have been removed, producing data that combines the characteristics of an image with the geometric qualities of a map. The orthographic images show ground features in their proper positions, without the distortion characteristic of unrectified aerial or satellite 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 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.Tribal lands have been masked from this public service in accordance with Tribal agreements.