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115 results for “land use and land cover”
Land Use and Cover Maps from Landsat, Mawas, Central Kalimantan, Indonesia, 1994-2019
This dataset contains annual land use/cover (LUC) maps at 30 m resolution across Mawas, Central Kalimantan, Indonesia. There are six files, each representing a five-year interval over the period 1994-2019. An additional file for 2015 was created for accuracy assessment. A high-quality and low-cloud coverage image from Landsat 5 or Landsat 8 over each 5-year period was selected or composited for the January-August timeframe. Investigators used their knowledge to manually identify training polygons in these images for five LUC classes: peat swamp forest, tall shrubs/ secondary forest, low shrubs/ferns/grass, urban/bare land/open flooded areas, and river. Pixel values of Landsat Tier 1 surface reflectance products and selected indices were extracted for each LUC and used to predict LUC classes across the Mawas study area using the Classification and Regression Trees (CART) method. These data can be used to evaluate the relationship between fire occurrence and land cover type in the study site.
Land Cover and Land Use Classification for the State of New Hampshire, 1996-2001
The New Hampshire Geographically Referenced Analysis and Information Transfer System (GRANIT) land cover data set provides a land cover and land use product at 30-m resolution with 23 individual classes across the state. The classification is based largely on the analysis of 12 Landsat Thematic Mapper (TM and ETM+) images. Over 1,400 new classification training site data points were collected to supplement 1,200 archived sites from previous projects. The classification represents a snapshot in time from 1996 to 2001. This time range spans the dates of the most recent acquisitions of a TM scene for each region of the state and the dates of the most recent field data collection.
Land Use and Land Cover Change Projection in the ABoVE Domain
This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.
NASA Web-Enabled Landsat Data 5 year Land Cover Land Use Change Product V001
WELDLCLUC.015 was decommissioned on December 2, 2019. The Web-Enabled Landsat Data (WELD) 5-year Land Cover Land Use Change (LCLUC) is a composite of 30 meter (m) land use land change product for the contiguous United States (CONUS). The data were generated from five years of consecutive growing season WELD weekly composite inputs from April 15, 2006, to November 17, 2010. WELD data are created using Landsat Thematic Mapper Plus (ETM+) Terrain Corrected data. This product includes data about tree cover loss and bare ground gain, which are composited over the five year period. WELD LCLUC is distributed in Hierarchical Data Format 4 (HDF4).The WELD project is funded by the National Aeronautics and Space Administration (NASA) and is a collaboration between the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the South Dakota State University (SDSU) Geospatial Sciences Center of Excellence (GSCE). Known Issues* WELD Version 1.5 known issues can be found in the WELD Version 1.5 User Guide.Improvements/Changes from Previous Version* Version 1.5 is the original version.
Annual Land Use and Urban Land Cover: Ethiopia, Nigeria, and South Africa, 2016-2020
This dataset provides a two-tier annual Land Use (LU) and Urban Land Cover (LC) product suite over three African countries, Ethiopia, Nigeria, and South Africa, across a 5-year period of 2016-2020. Remote sensing data sources were used to create 30-m resolution LU maps (Tier-1), which were then utilized to delineate urban boundaries for 10-m resolution LC classes (Tier-2). Random Forest machine learning classifier models were trained on reference data for each tier and country (but one model was trained across all years); models were validated using a separate reference data set for each tier and country. Tier-1 LU maps were based on the 30-m Landsat time series, and Tier-2 urban LC maps were based on the 10-m Sentinel-2 time series. Additional data sources included climate, topography, night-time light, and soils. The overall map accuracy was 65-80% for Tier-1 maps and 60-80% for Tier-2 maps, depending on the year and country. The data are provided in cloud optimized GeoTIFF (COG) format.
ISLSCP II Historical Land Cover and Land Use, 1700-1990
The Historical Land Cover and Land Use data set was developed to provide the global change community with historical land use estimates. The data set describes historical land use changes over a 300-year historical period (1700-1990).Testing against historical data is an important step for validating integrated models of global environmental change. Owing to long time lags in the climate and biogeochemical systems, these models should aim to simulate the land use dynamics for long periods, i.e., spanning decades to centuries. Developing such models requires an understanding of past and current trends and is therefore strongly data dependent. For this purpose, a historical database of the global environment has been developed: HYDE. Historical statistical inventories on agricultural land (census data, tax records, land surveys, etc) and different spatial analysis techniques were used to create a geographically-explicit data set of land use change, with a regular time interval. The data set can be used to test integrated models of global change. Continental-scale historical data were used for that period.
LBA-ECO CD-06 Ji-Parana River Basin Land Use and Land Cover Map, Brazil: 1999
This data set provides a land use/land cover map of the Ji-Parana River Basin in the state of Rondonia, Brazil produced from the digital classification of eight Landsat 7-ETM+ scenes from 1999 acquired from the Tropical Rain Forest Information Center (TRFIC) at Michigan State University. Nine land cover classes covering the Ji-Parana Basin were identified. There is one GeoTiff file with this data set.
LBA-ECO CD-06 Land Use/Land Cover Time Series, Ji-Parana Basin, Brazil: 1986-2001
This data set contains four land use/land cover maps (1986, 1992, 1996 and 2001) for the Ji-Parana River Basin, derived from the digital classification of 8 Landsat images obtained from The Tropical Rain Forest Information Center (TRFIC).
LBA-ECO ND-30 Fractional Cover of Mixed Land Use Ranches, Para and Rondonia, Brazil
This data set contains images of fractional cover estimates of photosynthetic vegetation (PV) canopy, nonphotosynthetic vegetation (NPV), and exposed soils (S) derived from Landsat images (30-m resolution) obtained for two ranches in the Brazilian Amazon from 1996 to 2002. The Fazenda Vitoria ranch is located in eastern Para near the city of Paragominas and is a mosaic of primary forest, logged forest, secondary forest, and pasture with moderately dissected topography. The Fazenda Nova Vida ranch is located in the state of Rondonia in western Amazonia and is a mosaic of primary forest, logged forest, and pastures. For Fazenda Vitoria, two dry-season Landsat images were obtained, subset, and analyzed. For Nova Vida three dry-season images and one end-of-wet-season image were obtained, subset, and analyzed. Spectral mixture analysis, which decomposes individual satellite pixels into constituent cover fractions of surface materials, was used with a general probabilistic modeling approach to derive subpixel cover fractions of PV, NPV, and S. There are six GeoTIFF (.tif) files with this data set.
LBA-ECO LC-01 Landsat TM Land Use/Land Cover, Northern Ecuadorian Amazon: 1986-1999
This data set contains Landsat TM imagery for the years 1986, 1989, 1996, and 1999, that have been classified into four land use/land cover (LULC) classes: Forest, Non-Forest Vegetation, Urban/Barren, and Water; and a fifth class of Clouds/Shadows. The areas of interest were the four Intensive Study Areas (ISA) of the University of North Carolina's Carolina Population Center (CPC) Ecuador Projects: Eastern Intensive Study Area; Northern Intensive Study Area; Southern Intensive Study Area, and Southwestern Intensive Study Area. These areas are in the Northern Ecuadorian Amazon, in the area known as the northern Oriente of Ecuador. The resolution of the data is 30 meters. There are 12 image files (.tif) with this data set.
Code and data used for findings and figures in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region"
<p><span>This is the code and data used in the manuscript "Land cover change-climate interactions amplified the diminishment of spring ecosystem productivity in the Arctic-Boreal region" to generate all findings and figures.</span></p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm
<p>Using the uploaded code in preparation of the Flood vulnerability maps.</p><p> </p>
Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing
<p>This dataset contains continental (Africa) land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources. The dataset contains seven classes, prepared annually from 2000 to 2015, using high‐resolution Landsat 7 images (ETM+) and analyzed by Google Earth Engine cloud computing method. The model that generated the LULC classification was evaluated for predictive accuracy across classes as well as overall accuracy. The model achieved an overall accuracy of 88% with class-specific user’s and producer’s accuracies ranged from 84-94% and 79-96% respectively (Midekisa et al., 2017).</p> <p> </p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>The used data in the paper</p>
Investigating the Combined Impact of Climate Change and Land Use/Land Cover on Flood Vulnerability Using a Machine Learning Algorithm.
<p>Flood points and Meteorological data</p>
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