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65 results for “Land cover change”
Data from: Understanding patterns of land-cover change in the Brazilian Cerrado from 2000 to 2015
Clearing tropical vegetation impacts biodiversity, the provision of ecosystem services, and thus ultimately human welfare. We quantified changes in land cover from 2000 to 2015 across the Cerrado biome of northern Minas Gerais state, Brazil. We assessed the potential biophysical and social-economic drivers of the loss of Cerrado, natural regeneration and net cover change at the municipality level. Further, we evaluated correlations between these land change variables and indicators of human welfare. We detected extensive land cover changes in the study area, with the conversion of 23,446 km2 and the natural regeneration of 13,926 km2, resulting in a net loss of 9,520 km2. The annual net loss (-1.2% per year) of the cover of Cerrado is higher than that reported for the whole biome in similar periods. We argue that environmental and economic variables interact to underpin rates of conversion of Cerrado, most severely affecting more humid Cerrado lowlands. While rates of Cerrado regeneration are important for conservation strategies of the remaining biome, their integrity must be investigated given the likelihood of encroachment. Given the high frequency of land abandonment in tropical regions, secondary vegetation is fundamental to maintain biodiversity and ecosystem services. Finally, the impacts of Cerrado conversion on human welfare likely vary from local to regional scales, making it difficult to elaborate land use policies based solely on social-economic indicators.
ALCC: Temporally consistent annual land cover maps over China from 1985 to 2022 based on an ensemble change detection method
<p><span><span>We develope <span><span> </span>a consistent annual land cover product for China (ALCC) from 1985 to 2022. First, change areas for each year was detected baesd on an ensemble change detection method combing CCDC, BFASTm, and Chow Test. Then, stable training samples were derived from both CLCD and CLUDs. The Random Forest classifier was locally trained and used to classify change areas year by year. Finally, ALCC was generated by updating land cover classification results for change areas and remaining class label the same as the base map forthe unchanged areas. ALCC achieved a mean overall accuracy of 81.11±0.67% nationwide and exceeded 72.00% across seven geographical regions.</span></span></span></p>
Carbon storage response to land use/land cover changes and SSP-RCP scenarios simulation: A case study in Yunnan Province, China
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Preserving the woody plant tree of life in China under future climate and land-cover changes
<p><span>The tree of life (TOL) is severely threatened by climate and land-cover changes. Preserving the TOL is urgent, but has not been included in the post-2020 global biodiversity framework. Protected areas (PAs) are fundamental for biological conservation. However, we know little about the effectiveness of existing PAs in preserving the TOL of plants and how to prioritize PA expansion for better TOL preservation under future climate and land-cover changes. Here, using high-resolution distribution maps of 8732 woody species in China and phylogeny-based Zonation, we find that current PAs perform poorly in preserving the TOL </span><span>both at the present and in the 2070s</span><span>. The geographical coverage of TOL branches by current PAs is ca. 9%, and < 3% of the identified priority areas for preserving the TOL are currently protected. Interestingly, the geographical coverage of TOL branches by PAs will be improved from 9% to 52–79% by the identified priority areas for PA expansion. Human pressures in the identified priority areas are high, leading to high costs for future PA expansion. We thus suggest that besides nature reserves and national parks, other effective area-based conservation measures should be considered. Our study argues for the inclusion of preserving the TOL in the post-2020 conservation framework and provides references for decision-makers </span><span>to preserve the Earth's evolutionary history.</span></p>
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
<p><strong>Paper Accept by WACV 2024</strong></p> <p>[paper, supp] [<a href="https://arxiv.org/abs/2309.01907">arXiv</a>]</p> <p><strong>Overview</strong></p> <p>Synthetic datasets, recognized for their cost effectiveness, play a pivotal role in advancing computer vision tasks and techniques. However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models. This complexity is compounded by the difficulties associated with real remote sensing datasets, including limited data acquisition and high annotation costs, which amplifies the need for high-quality synthetic alternatives. To address this, we present SyntheWorld, a synthetic dataset unparalleled in quality, diversity, and scale. It includes 40,000 images with submeter-level pixels and fine-grained land cover annotations of eight categories, and it also provides 40,000 pairs of bitemporal image pairs with building change annotations for building change detection task. We conduct experiments on multiple benchmark remote sensing datasets to verify the effectiveness of SyntheWorld and to investigate the conditions under which our synthetic data yield advantages.</p> <pre><strong>Description</strong> ------------ This dataset has been designed for land cover mapping and building change detection tasks. File Structure and Content: --------------------------- 1. **1024.zip**: - Contains images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `small-pre-images`: Images with a minor off-nadir angle difference compared to post-event images. - `big-pre-images`: Images with a large off-nadir angle difference compared to post-event images. - `cd_mask`: Ground truth for the building change detection task. 2. **512-1.zip**, **512-2.zip**, **512-3.zip**: - Contains images of size 512x512 with a GSD of 0.3-0.6m. - `images` and `ss_mask` folders: Used for the land cover mapping task. - `images` folder: Post-event images for building change detection. - `pre-event` folder: Images for the pre-event phase. - `cd-mask`: Ground truth for building change detection. Land Cover Mapping Class Grep Map: ---------------------------------- class_grey = { "Bareland": 1, "Rangeland": 2, "Developed Space": 3, "Road": 4, "Tree": 5, "Water": 6, "Agriculture land": 7, "Building": 8, }</pre> <p><strong>Reference</strong></p> <p>@misc{song2023syntheworld,<br> title={SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection}, <br> author={Jian Song and Hongruixuan Chen and Naoto Yokoya},<br> year={2023},<br> eprint={2309.01907},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p> <p> </p>
Large carnivore expansion in Europe is associated with human population density and land cover changes
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Data from: Understanding patterns of land-cover change in the Brazilian Cerrado from 2000 to 2015
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Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range
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Integrating stakeholders’ perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania
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Data from: Differential vulnerability of key threatened mammals to climate and land cover changes in the Central Himalayas
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Preserving the woody plant tree of life in China under future climate and land-cover changes
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Correlation Analysis and Simulation Modeling of Land use Land Cover change and its Link with Land surface temperature
<p>The uploaded data is related to LULC modeling. Data consist of driving variables and correlation analysis between LST and NDVI in different LULC classes.</p>
Data and calculations associated with "Tracking cropland transitions: a comparative analysis of U.S. land cover change data"
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Global Mangrove Loss Extent, Land Cover Change, and Loss Drivers, 2000-2016
This dataset provides estimates of the extent of mangrove loss, land cover change, and its anthropogenic or climatic drivers in three time periods: 2000-2005, 2005-2010, and 2010-2016. Landsat-based Normalized Difference Vegetation Index (NDVI) anomalies were used to determine loss extent in each period. The drivers of mangrove loss were determined by examining land cover changes using a random forest machine learning technique that considered change from mangrove to wet soil, dry soil, and water at each loss pixel. A series of decision trees used several global-scale land-use datasets to identify the ultimate driver of the mangrove loss. Loss drivers include commodity production (agriculture, aquaculture), settlement, erosion, extreme climatic events, and non-productive conversion. Maps of loss extent per period, mangrove land cover changes, and loss drivers are provided for each of 39 mangrove holding nations.
CMS: Mangrove Canopy Characteristics and Land Cover Change, Tanzania, 1990-2014
This data set provides canopy height, land cover change, and stand age estimates for mangrove forests in the Rufiji River Delta in Tanzania. The estimates were derived from a canopy height model (CHM) using TanDEM-X imagery and Polarimetric SAR interferometry (Pol-InSAR) techniques. Landsat imagery circa 1990 and circa 2014 was used to estimate stand age between 1994 and 2014 and for forest land cover change modeling.
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.
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>
Millennium Ecosystem Assessment: MA Rapid Land Cover Change
The Millennium Ecosystem Assessment: MA Rapid Land Cover Change provides data and information on global and regional land cover change in raster format for agriculture (cropland increase and disease), deforestation (forest mask and hotspots), desertification (hotspot areas of degraded land and degradation types), and fires (most frequent and exceptional fires). Urbanization data are in vector format for cities with the highest rate of change (1995-2015), largest cities (2000), and the overlay of these two data layers for the year 2000. This assessment identified the need to synthesize what is known about areas of rapid land cover change around the world in order to evaluate how the provision of ecosystem goods and services has changed.
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.
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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
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.