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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

A set of PYTHON programs to implement image processing of ground and aerial images by offering via graphical user interface (GUI) 1) plot-level metrics extraction through a series of algorithms for image conversion, band math, radiometric/geometric calibrations, segmentation, masking, adaptive region of interest (ROI), gridding, heatmap, and batch process, 2) GIS interface for GeoTIFF pixels to Lat/Lon, UTM conversion, read/write shapefile, Lat/Lon to ROI, grid to polygon, and 3) utility GUI functions for zooming, panning, rotation, images to video, file I/O, and histogram. Resources in this dataset:Resource Title: IMAP: Image Mapping & Analytics for Phenotyping. File Name: IMAP.zip

High resolution (10 meter) land surface temperature (LST) from September 1, 2022 is mapped for the seven-county metropolitan region of the Twin Cities. The goal of the map is to show the heat differences across the region and is not intended to show the maximum temperature that any specific area can reach. The raster dataset was computed at 30 meters using satellite imagery from Landsat 9 and downscaled to 10 meters using Copernicus Sentinel-2. These datasets were integrated using techniques modified from Ermida et al. 2020 and Onačillová et al. 2022). Open water was removed using ancillary data from OpenStreetMap and 2020 Generalized Land Use for the Twin Cities (Metropolitan Council).

First, Landsat 9 imagery taken at 11:59 am CDT on September 01, 2022 was processed into 30-meter resolution LST (based on Ermida et al. 2020). At this time, the air temperature was 88° F at the Minneapolis-St. Paul International Airport (NOAA). A model predicting LST based on spectral indices of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI) was created and applied to 10-meter Sentenel-2 imagery. Sentinel-2 imagery was also taken on September 1, 2022, and this resulted in a 10-meter downscaled LST image (based on Onačillová et al. 2022). To account for anomalies in NDVI on the primary image date of September 1 (e.g., recently harvested agricultural fields), maximum NDVI occurring between July 1, 2022 and September 1, 2022 was used for both Landsat and Sentinel image processing. Water bodies were removed for all processing steps (OpenStreetMap 2023, Metropolitan Council 2021).

This dataset is an update to the 2016 LST data for the Twin Cities Region (Metropolitan Council).

The code to create and processes this dataset is available at: https://github.com/Metropolitan-Council/extreme.heat

Sources:
Ermida, S.L., Soares, P., Mantas, V., Göttsche, F.-M., Trigo, I.F., 2020. Google Earth Engine open-source code for Land Surface Temperature estimation from the Landsat series. Remote Sensing, 12 (9), 1471; https://doi.org/10.3390/rs12091471.

Metropolitan Council. 2021. Generalized Land Use 2020. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-generl-lnduse2020

Metropolitan Council. 2017. Land Surface Temperature for Climate Vulnerability Analysis. Minnesota Geospatial Commons. https://gisdata.mn.gov/dataset/us-mn-state-metc-env-cva-lst2016

NOAA, National Oceanic and Atmospheric Administration, National Centers for Environmental Information, station USW00014922. September 1, 2022.

Onačillová, K., Gallay, M., Paluba, D., Péliová, A., Tokarčík, O., Laubertová, D. 2022. Combining Landsat 8 and Sentinel 2 data in Google Earth Engine to derive higher resolution land surface temperature maps in urban environment. Remote Sensing, 14 (16), 4076. https://doi.org/10.3390/rs14164076.

OpenStreetMap contributors. 2023. Retrieved from https://planet.openstreetmap.org on April 12, 2023.

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

This is a satellite image data product made available from the 1995 declassification of intelligence imagery acquired by the first generation of United States photo-reconnaissance satellites, including the systems code-named CORONA, ARGON and LANYARD. More than 860,000 images of the Earth's surface, collected between 1960 and 1972 have been declassified. The images were used to produce maps and charts for the Department of Defense and other Federal Government mapping programs.

Nearly all of the imagery from these systems was collected using black and white film. There is a very limited amount of infrared film and high definition colour aerial film that was tested as part of the KH-4B missions and yielded poor spatial resolution performance. The original film is being preserved by the National Archives and Records Administration (NARA) and is stored at the National Archives at College Park, MD.

The US Geological Survey (USGS) originally delivered these images as negatives and prints to customers, but now USGS offers them as scanned images in *.tiff file formats. The images are not geo-referenced and require a Geographical Information System (GIS) or image processing package to achieve this.

This CORONA image dataset was captured as part of the KH-4B mission which photographed the earth's surface from September 1967 to May 1972. The ground resolution for these images is six feet (1.8 metres).

This image is identified as DS1107-2154DA071, which is coded to provide the following information:

DS = Dataset Name (2 characters) MMMM = Revolution (4 characters) C = Camera Type (1 character) FFF = Frame number (3 characters)

This image file is named

This is one of eight image files cropped and extracted from the original file. This was done to improve spatial accuracy, during geo-referencing of each image, and reduce file size. The photo covers an area, which follows along the Syrian/Turkish border and extends east into the Sirnak Province of Turkey. The image starts just east of the Ras al Ayn settlement in Syria and extends east towards the settlement of Beytussebap, Turkey. Beytussebap is about 85km east of the River Tigris. Neither Ras al Ayn or Beytussebap can be seen on the image, but both are within 10km to the west and east respectively.

This image covers the upland area between Lake Baraj to the settlement of Hasantepe (western edge of the image). The town of Cilesiz, Turkey can be seen in the southwest corner of the image.

As this image was captured during the summer month of August in 1969, there is very little cloud cover present; ground visibility is close to 100 percent.

This image has been primarily used as part of research to locate and map archaeological sites (Tells) in this part of the Jazira region of Syria. The image also provides an important snapshot of the northern Jazira landscape in 1969. Landscape features and settlements in the region can also be mapped and compared with those from images captured recently to track and compare changes to land-use and settlement patterns in the region.

A geospatial image-based eye movement dataset called GeoEye, is a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images.

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

Input data:

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

Methods:

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

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

Data set format:

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

Data set for download:

A. Three data layers for Odisha, India:

OD_pre_binary.tif

OD_post_binary.tif

OD_inundation.tif

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

B. Three data layers for West Bengal, India:

WB_pre_binary.tif

WB_post_binary.tif

WB_inundation.tif

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

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

BNG_L_pre_binary.tif

BNG_L_post_binary.tif

BNG_L_inundation.tif

BNG_U_pre_binary.tif

BNG_U_post_binary.tif

BNG_U_inundation.tif

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

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

The global Remote Sensing Image Processing Platform market is poised for substantial growth, projected to reach approximately USD 2542 million by 2025, with a Compound Annual Growth Rate (CAGR) of 6.1% anticipated between 2019 and 2033. This robust expansion is fueled by an increasing demand for sophisticated geospatial data analysis across critical sectors. Environmental Monitoring is a significant driver, with platforms being essential for tracking climate change, deforestation, pollution, and natural disasters, necessitating timely and accurate analysis of satellite and aerial imagery. Similarly, the Agriculture & Land Use segment is leveraging these platforms for precision farming, crop health monitoring, yield prediction, and optimized land management, thereby enhancing agricultural productivity and sustainability. The Meteorology & Climate Research sector relies heavily on remote sensing data for weather forecasting, climate modeling, and understanding atmospheric phenomena. The "Others" application segment, encompassing defense, urban planning, infrastructure development, and resource management, further contributes to the market's upward trajectory. The market's growth is further propelled by technological advancements in image acquisition, processing algorithms, and cloud computing, enabling faster and more efficient analysis of vast datasets. The integration of Artificial Intelligence (AI) and Machine Learning (ML) is revolutionizing remote sensing image processing, allowing for automated feature extraction, object detection, and predictive analytics. The market is segmented by type into Image Preprocessing Platforms, crucial for data correction and enhancement; Image Analysis Platforms, which extract meaningful information; and "Others," catering to specialized functionalities. Key players such as ESRI, Hexagon, and NV5 Geospatial Software are at the forefront, offering innovative solutions. North America currently leads in market share, driven by significant investments in geospatial technologies and applications in environmental and defense sectors. However, the Asia Pacific region is expected to witness the fastest growth due to rapid industrialization, increasing adoption of remote sensing in agriculture and disaster management, and growing government initiatives for smart city development and land mapping. This comprehensive report provides an in-depth analysis of the global Remote Sensing Image Processing Platform market, covering market dynamics, trends, key players, and future outlook. The study period encompasses 2019-2033, with a base year of 2025 and a forecast period from 2025-2033, building upon historical data from 2019-2024. The market is projected to reach a significant valuation in the tens of millions by 2033.

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc.

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Geography

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Administrative Services

Temporal Coverage: 1994

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994.

Other: none

Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing)

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

This project maps the conversion from mid-20th century (1946-71) flood and sprinkler irrigation to sprinkler irrigation (center-pivot and other sprinkler), and other land types (fallow, crop, and flood remaining flood) in Montana, by 2019.

Over the past 50 years, many producers in Montana have made changes to their irrigation practice and infrastructure in an effort to increase irrigation efficiency, defined as the ratio of water consumed by crops to water diverted or pumped (consumed water ÷ diverted water). Changes in the method of irrigation, especially conversion from flood to sprinkler irrigation, may have significant on-farm benefits such as reduced labor and increased production. Conversion can have both beneficial and adverse impacts on streamflow and aquatic ecosystems depending on local site-specific hydrogeologic conditions and how irrigation water is managed. As part of the Montana Water Center’s effort to better understand the effects of increased irrigation efficiency in Montana (Lonsdale et al. 2020), historic conversion from flood to sprinkler irrigation was analyzed using available agricultural statistics, maps from state and federal sources, and an independent Geographic Information Systems (GIS) analysis.

The first Resource in this HydroShare Collection, "Conversion from Flood to Sprinkler Irrigation in Montana between Mid-20th Century and 2019", presents the GIS analysis and maps the amount and spatial distribution of conversion from flood to sprinkler irrigation, between the mid-20th century and 2019. Historic mid-20th century irrigation was mapped in detail from 1943-1965 by the State Engineer’s Office and from 1966-1971 by the Montana Water Resources Board—the predecessor of the Montana Department of Natural Resources and Conservation (DNRC). A scanned and georeferenced version of the Water Resources Surveys (WRS) was compared with maps of contemporary irrigated land (Montana Department of Revenue’s 2019 Final Land Unit Classification—DORFLU2019) to estimate the area of land converted from flood to sprinkler irrigation. Prior to GIS analysis, both datasets were edited to ensure valid comparison between irrigated field mapping conducted at the two points in time. To estimate the amount of conversion from flood to sprinkler irrigation, and other uses, the GIS layers (WRS flood and sprinkler 1946-1971 and DOR-FLU 2019) were overlain in ArcGIS; then the clipping erase functions were used to select the WRS flood and sprinkler parcels that were shown as sprinkler irrigated in 2019. Additional conversion classes were also mapped that represent the changes from WRS flood and sprinkler to cropland, hayland and fallow, and WRS sprinkler- remaining- sprinkler and flood remaining flood. Details of the analysis are provided in Appendix C. of the main report and which is located within HydroShare Resource: https://www.hydroshare.org/resource/15392cb3617b4519af6ae8972f603502/data/contents/Appendix_C._Methods_and_data_for_GIS_mapping_of_conversion_from_flood_to_sprinkler_irrigation.pdf

The second Resource in this Collection," Uncertainty analysis of irrigation conversion polygon areas", provides files used in the uncertainty analysis of polygon areas resulting from overlaying/clipping/erase GIS operations that map the irrigation system conversions from mid-20th century to 2019.There are several sources of uncertainty in the conversion mapping results. The first is that the analysis only accounts for changes that occurred between the WRS 1946-71 and DORFLU2019; it is possible that additional flood irrigation developed between the two points in time may have also been converted to sprinkler. Another source of uncertainty is due to GIS processing and overlay/clip/erase functions that create “sliver” polygons of apparent change due to misalignment of the WRS 1946-71 and DORFLU2019 layers (i.e., co-registration error). This was evaluated using the spatially distributed probabilistic (SDP) method of Leonard and others (2020) and found to be small—generally less than one percent of the area of conversion polygons. Digitizing error was evaluated indirectly and found to be about ±12 percent of the reported area values. The values sum in quadrature to provide an overall estimate of error in polygon area of 12%. Conversion from flood to sprinkler polygon areas presented in the main report, and associated error statistics, apply to the whole dataset at the statewide scale. For use at the basin scale (for example, HUC4 Upper Yellowstone, the end user should review the uncertainty estimate for specific conversion polygons and refine if necessary. Please see Appendix D. Uncertainty analysis.pdf for details of the analysis. All citations are included in the References.txt file and in the main report.

Neural Network training dataset for classification of Images with presence and absence of sargassum

In May 2013, the Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey’s (USGS) Southwest Biological Science Center (SBSC) acquired airborne multispectral high resolution data for the Colorado River in Grand Canyon in Arizona, USA. The imagery data consist of four bands (blue, green, red and near infrared) with a ground resolution of 20 centimeters (cm). These data are available to the public as 16-bit geotiff files. They are projected in the State Plane (SP) map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). The assessed accuracy for these data is based on 91 Ground Control Points (GCPs), and is reported at 95% confidence as 0.64 meters (m) and a Root Mean Square Error (RMSE) of 0.36m. The airborne data acquisition was conducted under contract by Fugro Earthdata Inc. using two fixed wing aircraft from May 25th to 30th, 2013 at altitudes between 2440 meters to 3350 meters above mean sea level. The data delivered by Fugro Earthdata Inc. were checked for smear, shadow extent and water clarity as described for previous image acquisitions in Davis (2012). We then produced a corridor-wide mosaic using the best possible tiles with the least amount of smear, the smallest shadow extent, and clearest, most glint-free water possible. During the mosaic process adjacent tiles sometimes had to be spectrally adjusted to account for differences in date, time, sun angle, weather, and environment. We used the same method as described in Davis (2012) for the spectral adjustment. A horizontal accuracy assessment was completed by Fugro Earthdata Inc. using 188 GCPs provided by GCMRC. The GCPs were marked during the image acquisition with 1m2 diagonally alternated black and white plastic panels centered on control points throughout the river corridor in the GCMRC survey control network (Hazel and others, 2008). The Root Mean Square Error (RMSE) accuracy reported by Fugro Earthdata Inc. is 0.17m Easting and 0.15m Northing, or better, depending on the acquisition zone. The 16-bit image data are stored as four band images in embedded geotiff format, which can be read and used by most geographic information system (GIS) and image-processing software. The TIFF world files (tfw) are provided, however they are not needed for many software to read an embedded geotiff image. The image files are projected in the State Plane (SP) 2011, map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). A complete detailed description of the methods can be found in the associated USGS Data Series 1027 for these data, https://pubs.er.usgs.gov/publication/ds1027.

Geoscience Australia is distributing the Australian Geographic Reference Image (AGRI), a national mosaic which provides a spatially correct reference image at a 2.5 metre resolution across Australia. Geoscience Australia developed AGRI to address the need for a higher resolution reference image, of known accuracy, over the entire Australian continent.

Geoscience Australia developed AGRI between July 2009 and June 2011. The project was made possible by a combination of new data from Japan's Advanced Land Observing Satellite (ALOS) which produced panchromatic observations at 2.5 metre resolution; new `full pass' processing techniques for rectification of satellite imagery developed in the Cooperative Research Centre for Spatial Information and included in the Barista software; Geoscience Australia's expertise in Geodesy and Global Positioning System, and the capabilities of the Australian Spatial industry in GIS database design, field survey and image processing.

Pixel size: 2.5 metres for UTM Zone mosaic and 0.0001 deg (~10 m) for Australia continental mosaic.

File format: Generic BIL files with ER Mapper and ENVI ASCII header for UTM Zone mosaic. ECW compressed files for both Zone mosaic and Continental mosaic.

File size: UTM Zone 49 contrast balanced mosaic - 75GB UTM Zone 50 contrast balanced mosaic -296GB UTM Zone 51 contrast balanced mosaic - 376GB UTM Zone 52 contrast balanced mosaic - 354GB UTM Zone 53 contrast balanced mosaic - 381GB UTM Zone 54 contrast balanced mosaic - 472GB UTM Zone 55 contrast balanced mosaic - 474GB UTM Zone 56 contrast balanced mosaic - 377GB Continental contrast balanced mosaic - 145GB

Epoch: 25/4/2010, 27/6/2010, 15/7/2010, 7/7/2007, 22/5/2007, 16/6/2010, 18/5/2010, 17/1/2010, 4/6/2010, 6/5/2010, 20/11/2009, 30/3/2007, 15/5/2007, 29/11/2006, 22/10/2009, 25/10/2010/, 19/7/2008, 8/8/2009, 21/12/2008, 8/5/2009, 10/1/2010, 23/4/2008, 11/6/2009, 16/8/2010, 28/3/2009, 10/11/2008, 30/8/2009, 14/4/2009, 25/5/2007, 1/8/2009, 13/9/2008, 29/7/2008, 5/4/2010, 3/7/2009, 10/2/2007, 7/3/2010, 22/4/2010, 22/12/2009, 3/7/2007, 8/7/2009, 18/11/2007, 23/5/2009, 12/6/2010, 28/10/2010, 14/5/2010, 14/8/2010, 28/8/2009, 15/12/2009, 2/8/2010, 19/5/2010, 19/6/2009, 1/5/2008, 3/1/2009, 31/3/2007, 18/2/2009, 26/10/2010, 24/9/2009, 26/8/2009, 15/6/2010, 17/8/2010, 3/6/2010, 2/8/2009, 22/5/2010, 8/9/2010, 22/6/2009, 24/8/2009, 29/7/2010, 30/9/2010, 17/7/2010, 15/6/2009, 2/7/2009, 19/7/2009, 20/6/2009, 22/8/2009, 11/3/2010, 13/5/2010, 9/7/2008, 1/5/2010, 30/6/2009, 20/10/2010, 3/8/2009, 8/7/2010, 25/10/2010, 8/8/2009, 10/10/2009, 30/7/2010, 13/8/2009, 30/11/2009, 16/6/2009, 5/4/2010, 23/7/2010, 6/5/2009, 23/11/2009, 9/3/2009, 29/3/2010, 16/10/2010, 2/5/2010, 29/12/2008, 18/1/2010, 22/9/2010, 9/10/2010, 23/1/2010, 27/9/2010, 29/5/2010, 30/4/2010, 2/10/2010, 19/10/2010

This data is available under Australian Creative Commons 3.0: http://creativecommons.org/licenses/by/3.0/au/

Details of the processing characteristics are available from Technical Report, GeoCat #72657

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 ...

This dataset contains binary geotiff masks/classifications of six Arctic deltas for channels, lakes, land, and other small water bodies (see methods). Tiff files can be opened with any image viewer, but use of georeferencing data attached to the imagery will require a GIS platform (e.g., QGIS). Dataset includes individually classified scene masks for Colville (2014), Kolyma (2014), Lena (2016), Mackenzie (2014), Yenisei (2013), and Yukon (2014). We also provide .mat files for each delta that include a 2D array of the mosaicked images that is cropped to include only the area used in our analyses (see Piliouras and Rowland, 2020, Journal of Geophysical Research - Earth Surface), as well as the X (easting) and Y (northing) arrays for georeferencing, with coordinates in UTMs.

Through application of a nearest-neighbor imputation approach, mapped estimates of forest carbon density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product contains the following 8 raster maps: total forest carbon in all stocks, live tree aboveground forest carbon, live tree belowground forest carbon, forest down dead carbon, forest litter carbon, forest standing dead carbon, forest soil organic carbon, and forest understory carbon. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004. Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

This repository contains data and codes that support the findings of the study.- PPD-EPC open dataset with the enriched spatial analyses scores and UPRN.- Batch Geocoding Notebook of PPD-EPC dataset with GeoPy - Here API- PyQGIS codes for proximity, terrain, and visibility spatial analyses.- Jupyter Notebook of Machine Learning algorithms for mass property valuation.

This is a collection of Canopy Height 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. The data represents the height of vegetation above ground, measured in meters.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.Canopy Height data are primarily derived from Lidar 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 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.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

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

Data acquisition

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

Data collection

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

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

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

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

Value of the data

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

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

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

Data description

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

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

Experimental design, materials and methods

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

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

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

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

Acknowledgments

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

References

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

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

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

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

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

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