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528 results for “Land cover”
Very high resolution Land Cover maps OAL-UK (Catterline)
<p>Surface features are produced as a result of internal deformation of active landslides, and are continuously created and destroyed by the movement. The detailed mapping of the evolution of bare ground patches and vegetation cover pattern over time provides useful insights on the mass movements and the most vulnerable areas. Monitoring the evolution of the latter, in turn, could help to describe the benefits of NBS past their implementation by observing a reduction of vulnerable areas and the increase of the extent of stable vegetation as a result of the interventions.<br> The preliminary analysis of satellite data on OAL-UK was devoted to the analysis of a time series of very high resolution multispectral satellite data (<1 m) in the period prior to the implementation of NBS. The goal was to explore the potential of remote sensing to describe the pattern and extent of vegetation cover and plant cover regeneration, as well as to observe the self-organisation of landslide scars - i.e. re-distribution of bare ground patches over time.</p> <p>The dataset includes Land Cover maps obtained by Worldview 2 satellite images acquired between 2011 and 2016 in different seasons, notably on 29 Jun 2011, 22 April 2014, 08 June 2014 and 22 Feb 2016.</p> <p>After the pre-processing phase, Principal Component Analysis (PCA) was applied on the pansharpened multispectral bands to reduce the dimensionality of each dataset (pansharpened multispectral bands), while retaining as much as possible its information content. (Richardson, 2009).</p> <p>A machine learning unsupervised classification (K-means) was applied to the first three PC found by PCA. K-means is an unsupervised classification algorithm that groups objects into k groups based on their characteristics. The mean spectral reflectance values of the samples assigned to each cluster was analyzed to identify typical spectral profiles of land features as well as eventual similarities between the classes and successively used to assign labels to the unsupervised classes. In cases of spectral similarity between two or three clusters, thy were merged in a unique class. </p> <p>The results of k-means classification were analyzed to define typical spectral profiles of land cover features found in OAL-UK. First, the mean and standard deviation of the spectral bands of each cluster and image were calculated and compared with spectral libraries of land features. This led to identify 11 land cover classes with typical and recurrent spectral profiles within the OAL: </p> <p>1. Bare soil</p> <p>2. urban materials and sea foam (uniform spectra)</p> <p>3. water saturated soil</p> <p>4. water logged soil/vegetation</p> <p>5. vegetation (grass)</p> <p>6. dense vegetation (shrubs)</p> <p>7. vegetation with exposed soil</p> <p>8. soil with sparse vegetation</p> <p>9. mixed wet soil/vegetation</p> <p>10. Water</p> <p>11. Turbid Water</p>
Land cover classification OAL-DE (H2020 OPERANDUM)
<p>Land cover classification performed on a Planetscope image acquired in May 2021. The method was based on a supervided classification using Support Vector Machine. The training dataset was constructed using orthophoto images available for the study area in March-May 2021. </p>
A COMPARATIVE STUDY OF ALGORITHMS FOR LAND COVER CHANGE
A COMPARATIVE STUDY OF ALGORITHMS FOR LAND COVER CHANGE SHYAM BORIAH*, VARUN MITHAL*, ASHISH GARG*, VIPIN KUMAR*, MICHAEL STEINBACH*, CHRIS POTTER**, AND STEVE KLOOSTER*** Abstract. Ecosystem-related observations from remote sensors on satellites offer huge potential for understanding the location and extent of global land cover change. This paper presents a comparative study of three time series based algorithms for detecting changes in land cover. The techniques are evaluated quantitatively using forest fire ground truth from the state of California for 2000–2009. On relatively high quality data sets, all three schemes perform reasonably well, but their ability to handle noise and natural variability in the vegetation data differs dramatically. In particular, one of the algorithms significantly outperforms the other two since it accounts for variability in the time series.
NLCD 1992/2001 Retrofit Land Cover Change Product
Developments in mapping methodology, new sources of input data, and changes in the mapping legend for the 2001 National Land Cover Database (NLCD2001) will confound any direct comparison between NLCD2001 and National Land Cover Dataset 1992 (NLCD1992). Users are cautioned that direct comparison of these two independently created land cover products is not recommended. This NLCD 1992/2001 Retrofit Land Cover Change Product was developed to offer users more accurate direct change analysis between the two products. The NLCD 1992/2001 Retrofit Land Cover Change Product uses a specially developed methodology to provide land cover change information at the Anderson Level I classification scale (Anderson et al., 1976*), relying on decision tree classification of Landsat satellite imagery from circa 1992 and 2001. Unchanged pixels between the two dates are coded with the NLCD01 Anderson Level I class code, while changed pixels are labeled with a "from-to" land cover change value. Additional details about this product are available in the metadata included in the multi-zone downloadable zip file. This product is designed for regional application only and is not recommended for local scales.
SMAPVEX16 Manitoba Land Cover Classification Map V001
This data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.
National Land Cover Database 2006 (NLCD2006)
National Land Cover Database 2006 (NLCD2006) is a 16-class land cover classification scheme that has been applied consistently across the conterminous United States at a spatial resolution of 30 meters. NLCD2006 is based primarily on the unsupervised classification of Landsat Enhanced Thematic Mapper+ (ETM+) circa 2006 satellite data. NLCD2006 also quantifies land cover change between the years 2001 to 2006. The NLCD2006 land cover change product was generated by comparing spectral characteristics of Landsat imagery between 2001 and 2006, on an individual path/row basis, using protocols to identify and label change based on the trajectory from NLCD2001 products. It represents the first time this type of 30 meter resolution land cover change product has been produced for the conterminous United States.
SMAPVEX08 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
SMAPVEX08 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery and of data obtained in the field as part of the Soil Moisture Active Passive Validation Experiment 2008 (SMAPVEX08).
National Land Cover Data set 1992 (NLCD1992)
National Land Cover Dataset 1992 (NLCD1992) is a 21-class land cover classification scheme that has been applied consistently across the lower 48 United States at a spatial resolution of 30 meters. NLCD92 is based primarily on the unsupervised classification of Landsat Thematic Mapper (TM) circa 1990's satellite data. Other ancillary data sources used to generate these data included topography, census, and agricultural statistics, soil characteristics, and other types of land cover and wetland maps. NLCD1992 is the only NLCD dataset that can be downloaded by state and by user defined area from the MRLC Consortium Viewer.
Land Cover Classification, Snow Cover, and Fractional Snow-Covered Area Maps from Maxar WorldView Satellite Images V001
This data set includes: (1) fine-scale snow and land cover maps from two mountainous study sites in the Western U.S., produced using machine-learning models trained to extract land cover data from WorldView-2 and WorldView-3 stereo panchromatic and multispectral images; (2) binary snow maps derived from the land cover maps; and (3) 30 m and 465 m fractional snow-covered area (fSCA) maps, produced via downsampling of the binary snow maps. The land cover classification maps feature between three and six classes common to mountainous regions and integral for accurate stereo snow depth mapping: illuminated snow, shaded snow, vegetation, exposed surfaces, surface water, and clouds. Also included are Landsat and MODSCAG fSCA map products. The source imagery for these data are the Maxar WorldView-2 and Maxar WorldView-3 Level-1B 8-band multispectral images, orthorectified and converted to top-of-atmosphere reflectance. These Level-1B images are available under the NGA NextView/EnhancedView license.
SMAPVEX12 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.
SMAPVEX12 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery as part of the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12). Images from the RADARSAT-2, Système Pour l'Observation de la Terre (SPOT-4), and DMC International Imaging Ltd (DMCii) of the study area were retrieved for the summer of 2012. The land use classification image provides information about vegetation present in the study area at a resolution of 20 meters.
CLASIC07 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.
Global Land Cover Characterization Program
The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community. The project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program. Funding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme. The data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set. [Summary provided by the USGS.]
SMAPVEX16 Manitoba Land Cover Classification Map V001
This data set contains land cover classification data collected for the Soil Moisture Active Passive Validation Experiment 2016 Manitoba (SMAPVEX16 Manitoba) campaign.
CLASIC07 Land Cover Classification Map V001
This data set consists of land cover classification data derived from satellite imagery as part of the Cloud and Land Surface Interaction Campaign 2007 (CLASIC07). ResourceSat-1 AWiFS images of the study area were retrieved for the period of April through August 2007. The land use classification image provides information about vegetation present in the study area at a resolution of 56 meters.
Original data for resident and Delphi surveys and green land cover assessment
<p>This repository contains raw survey data, Delphi analysis responses, and green space quantification GIS data for the Friendly Area Neighborhood in Eugene, Oregon.</p>
Classification of land cover for Selenga Basin -Buryat Republic, Russia and Mongolia, 1990, 2020.V1.3
<p>!Readme<br> Classification of land cover for Selenga Basin -Buryat Republic, Russia and Mongolia, 1990, 2020.<br> Image classifications were performed in the Google Earth Engine environment. Pixel-based image composites were produced with Landsat 4,5 for the period I (1988-1991) and ESA Copernicus Sentinel-2 for period II (2019-2020). Original bands were complemented with simple seasonality metrics (e.g., minimum, maximum, standard deviation) calculated across phenology periods (from April to October). Training and validation data were collected during the field trip, interpretation of very-high-resolution imagery and dense optical Landsat and Sentinel-2.<br> Selenga_basin_1990_cl_v1.3.tif-image classification for Buryat (Russia) and Mongolian part of Selenga basin circa 1990<br> Selenga_basin_2020_cl_v1.3.tif-image classification for Buryat (Russia) and Mongolian part of Selenga basin circa 2020<br> Classification catalog<br> 1-Forest<br> 2-Cropland<br> 3-Grassland<br> 4-Impervious, bare<br> 5-Water<br> For more details, please consult Alexander Prishchepov alpr@ign.ku.dk, prialign@gmail.com</p>
Essential Urban Land Cover Category Dataset - Samples
<p>This dataset, titled "Essential Urban Land Cover Category Dataset - Samples", contains sample data for five representative cities produced in the manuscript "Large-scale High-resolution Essential Urban Land Cover Category (EULCC) Mapping Using a Semantic-Augmented and Noise-Tolerant Approach". The raster data values range from 1 to 9, representing different essential urban land cover categories as follows:</p> <table> <tbody> <tr> <td>Values</td> <td>Classes</td> </tr> <tr> <td>1</td> <td>Building</td> </tr> <tr> <td>2</td> <td>Tree</td> </tr> <tr> <td>3</td> <td>Grass/Shrub</td> </tr> <tr> <td>4</td> <td>Parking lot</td> </tr> <tr> <td>5</td> <td>Road</td> </tr> <tr> <td>6</td> <td>Water</td> </tr> <tr> <td>7</td> <td>Barren</td> </tr> <tr> <td>8</td> <td>Agriculture</td> </tr> <tr> <td>9</td> <td>Others</td> </tr> </tbody> </table> <p> </p>
Covariate layers (100 m) for soil mapping Canada --- DTM, precipitation, snow probs, land cover
<p>Compilation and harmonization of environmental covariates for PSM in Canada Developed for: Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada.</p> <p>Layers (see: <a href="https://docs.google.com/spreadsheets/d/1uK7asX85vrEkGDlwo0aDcKsVs9Becr8psqwqjQTBkTQ/edit#gid=2007548409">CanSIS explanation of names</a>):</p> <ul> <li>DTM and DTM derivatives at 100 m (200 m, 400 m and 800 m)</li> <li>Downloaded precipitation and snow probability maps,</li> <li>Land cover and admin units,</li> </ul> <p>Prepared by: Robert A. MacMillan (<a href="mailto:bobmacm@gmail.com">bobmacm@gmail.com</a>; LandMapper Environmental Solutions Inc.) and Tom Hengl (EnvirometriX Ltd)</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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