Find research datasets worth reusing

openOpenJan 2020View details →

Land Cover, 2005, for Town of Burlington, Massachusetts - Vector

This is a seven-category land-cover map of Burlington, Massachusetts. The seven categories are: bare soil, coniferous trees, decidous trees, grass, impervious surface, water, and wetlands. Note: Complete metadata is available within the downloaded zip file. This metadata can be viewed with ESRI ArcGIS software, and can be exported to FGDC and ISO metadata formats.

openCC (other)Feb 2018View details →

PIE LTER Land Cover (2005), Plum Island Sound estuary, Massachusetts - Raster

This is a seven-category land-cover map of the Plum Island Sound estuary, Massachusetts. The seven categories are: water, tidal flats and soils, Spartina alterniflora, Spartina patens, trees, grass, impervious surface. These medium resolution true color images are considered the new "basemap" for the Commonwealth by MassGIS. The photography for the entire commonwealth was captured in April 2005 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format.

openCC (other)Feb 2018View details →

PIE LTER Land Cover (2013), Plum Island Sound estuary, Massachusetts - Raster

This is a seven-category land-cover map of the Plum Island Sound estuary, Massachusetts. The seven categories are: water, tidal flats and soils, Spartina alterniflora, Spartina patens, trees, grass, impervious surface. These medium resolution true color images are considered the new "basemap" for the Commonwealth by MassGIS. The photography for the entire commonwealth was captured in April 2013 when deciduous trees were mostly bare and the ground was generally free of snow. Image type is 4-band (RGBN) natural color (Red, Green, Blue) and Near infrared in 8 bits (values ranging 0-255) per band format.

openCustomSep 2019View details →

Land Cover Classification for Hog Island, VA, 2018

Image processing/classification of Sentinel-2 imagery (2018 Summer scene) was used to produce a land cover classification of Hog Island, Virginia. Using Random Forests classifier the accuracy obtained was 86%. The different cover types include: 1. Trees 2. Shadow 3. Spartina patens (Sprobolus pumilus) 4. SeaWater 5. Brackish 6. Morella cerifera 7. Beach Sand 8. Inter Tidal 9. Water

opencc-by-4.0Dec 2019View details →

Copernicus Global Land Service: Land Cover 100m: epoch 2016: Africa demo (deprecated)

This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518025">this global dataset</a> instead. Demonstration land cover maps over Africa at 100m resolution for epoch year 2016, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations. The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data). For the consolidated epoch 2016, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2015) and pastor (2017) data. The layers with the probability  of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) year (epoch 2018).   <a href="https://africa.lcviewer.vito.be/2016">View the maps and area statistics</a> <a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a> <a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a>

opencc-by-4.0Jan 2020View details →

Snow depth map and land cover map from satellite photogrammetry (Pleiades) in Tuolumne, California.

snow_depth_20170501_pleiades.tif Snow depth from the difference of digital elevation models (DEMs) calculated from Pléiades images. The snow-on DEMs were acquired on 2017-05-01. The snow-off DEMs were acquired on 2017-08-08 and 2017-08-13.   land_cover_20170813-08_pleiades.tif land_cover_20170501_pleiades.tif Land cover calculated from multi-spectral Pléiades images acquired on 2017-05-01 (snow-on) and 2017-08-08 and -13 (snow-off). Classes are snow (1), forest (2), bare rock/low vegetation (3), lake (4).

opencc-by-4.0Jun 2020View details →

Multispectral and augmented Landsat data with land cover labels

Benchmark set at 77.1% O.A at: https://doi.org/10.1117/1.JRS.14.048503 The dataset consists of 60,000 images, corresponding to Landsat patches of 33x33 pixels with 102 bands. Randomly selected from Mexico (country). Each patch is labeled with one of 12 Land Use and Vegetation classes according to the classification described at https://doi.org/10.3390/rs6053923. The zip file contains 12 folders numbered 1-12 and each contains 5,000 .npy python files (can be loaded with the NumPy library). The labeled classes correspond to the following identifier. 1, Temperate Coniferous forest 2, Temperate Decidius Forest 3, Temperate Mixed Forest 4, Tropical Evergreen Forest 5, Tropical Deciduous Forest 6, Scrubland 7, Wetland Vegetation 8, Agriculture 9, Grassland 10, Water body 11, Barren Land 12, Urban Area To build that dataset, we take the information of the National Continuum of Land Use and Vegetation series number 5 generated by the National Institute of Statistics and Geography from Mexico (INEGI) from The National Commission for the Knowledge and Use of Biodiversity (CONABIO) web page (http://geoportal.conabio.gob.mx/metadatos/doc/html/usv250s5ugw.html). The file used for this dataset construction is the shape format file with geographic coordinates located in http://www.conabio.gob.mx/informacion/gis/maps/geo/usv250s5ugw.zip. Later, a transformation to Albers equal-area conic projection was done with the followings parameters: Fake east: 2500000.0 Fake North: 0.0 Origin longitude: -102.0º Origin latitude: 12.0º First standard parallel: 17.5º Second standard parallel: 29.5º Linear unit: Meter (1.0) Reference ellipsoid: GRS80 Once the data was projected, using the classes identified in the National Continuum of Land Use and Vegetation, correspondence was applied to the classes identified in https://doi.org/10.3390/rs6053923, these classes being: Agriculture, Barren land, Grassland, Scrubland, Temperate coniferous forest, Temperate deciduous forest, Temperate mixed forest, Tropical deciduous forest, Tropical evergreen forest, Urban area, Waterbody and Wetland vegetation. Once the information layer was generated with the 12 classes indicated above, the reference layer was rasterized. Thus, a national grid of 1,975,940 regions of 1 x 1 kilometers was generated and the percentage of pixels of the dominant class in each corresponding 1 km region was associated. A total of cells with 70% or more pixels from one dominant class corresponds to 1,640,827 which represents a total of 83% of the Mexican territory. That means, only 17% of cells have less than 70% of their pixels from one dominant class. Then, 5000 regions were randomly selected from each land cover class at the national level. For this random selection only were selected the regions in which cells have 70% or more of their pixels from one dominant class. The above, for looking to have consistent and reliable data for the automatic classification task. This random selection generates a total of 60,000 regions selected. Image patches were extracted from the selected regions in the sample. The image used is the result of the application of multiple time series analysis algorithms on a cube of image data with mainly Tier 1 (T1) quality and a few Tier 2 (T2) as described in https: // www. usgs.gov/land-resources/nli/landsat/landsat-collection-1. An Open Data Cube (ODC, https://www.opendatacube.org/) was constructed from 3,515 Landsat 5 and 7 images corresponding to the year 2011, which is the same reference year of the National Continuum of Land Use and Vegetation Series 5. From the analysis of the ODC images, the Geomedian (https://doi.org/10.1109/TGRS.2017.2723896) was calculated, which generated a national cloud-free mosaic from 2011, pixels at 30 meters resolution and 6 spectral bands (blue, green, red, nir, swir 1, swir 2). Finally, 15 spectral indices were calculated for each pixel in the image. This resulted in 15 national mosaics from the analysis of the time series of each pixel available for the year 2011 using all the combinations of normalized difference indices, which were possible with the 6 bands that were incorporated into the data cube, with which resulted in 102 information channels. Since Landsat images have a resolution of 30 meters, we have images of 33 pixels x 33 pixels for each region of 1 km x 1 km. The 102 channels in the patches correspond to: Geomedian Bands (6): blue, green, red, nir, swir 1, swir 2 Geomedian Based Indexes (15): evi, bu, sr, arvi, ui, ndbi, ibi, ndvi, ndwi, mndwi, nbi, brba, nbai, baei, bi Geomedian Based Tasseled cap transformation (6): brightness, greenness, wetness, fourth, fifth, sixth 2011 Landsat Time Analysis Series by Pixel (red-swir 1)/(red+swir 1); (5):    min, mean, max, std, median (red-nir)/( red+nir); (5): min, mean, max, std, median (swir 1-swir 2)/( swir 1+swir 2); (5): min, mean, max, std, median (nir-swir 2)/(nir+swir 2); (5): min, mean, max, std, median (nir-swir 1)/( nir+swir 1); (5): min, mean, max, std, median (red-swir 2)/( red+swir 2); (5): min, mean, max, std, median (green-swir 2)/(green+swir 2); (5): min, mean, max, std, median (green-swir 1)/(green+swir 1); (5): min, mean, max, std, median (green-red)/(green+red); (5): min, mean, max, std, median (green-nir)/(green+nir); (5): min, mean, max, std, median (blue-swir 2)/(blue+swir 2); (5): min, mean, max, std, median (blue-swir 1)/(blue+swir 1); (5): min, mean, max, std, median (blue-red)/(blue+red); (5): min, mean, max, std, median (blue-nir)/(blue+nir); (5): min, mean, max, std, median (blue-green)/( blue+green); (5): min, mean, max, std, median

opencc-by-4.0Jul 2020View details →

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

This package supplements the following paper entitled “Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm” published with Science China Earth Sciences.

opencc-by-4.0Jul 2020View details →

Raw Data of "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"

The raw data of manuscript "Runoff and erosive responses to different land-cover types in semiarid environment: Scale effects and controlling factors"

opencc-zeroJul 2020View details →

Data from: Anthropogenic noise does not surpass land cover in explaining habitat selection of Greater Prairie-Chicken (Tympanuchus cupido)

Over the last century, increasing human populations and conversion of grassland to agriculture have had severe consequences for numbers of Greater Prairie-Chickens (Tympanuchus cupido). Understanding Greater Prairie-Chicken response to human disturbance, including the effects of anthropogenic noise and landscape modification, is vital for conserving remaining populations because these disturbances are becoming more common in grassland systems. Here, we evaluate the effect of low-frequency noise emitted from a wind energy facility on habitat selection. We used the Normalized Difference Soundscape Index, a ratio of human-generated and biological acoustic components, to determine the impact of the dominant acoustic characteristics of habitat relative to physical landscape features known to influence within-home range habitat selection. Female Greater Prairie-Chickens avoided wooded areas and row crops but showed no selection or avoidance of wind turbines based on the availability of these features across their home range. Although the acoustic environment near the wind energy facility was dominated by anthropogenic noise, our results show that acoustic habitat selection is not evident for this species. In contrast, our work highlights the need to reduce the presence of trees which have been historically absent from the region, as well as decrease the conversion of grassland to row crop agriculture. Our findings suggest physical landscape changes surpass altered acoustic environments in mediating Greater Prairie-Chicken habitat selection.

opencc-zeroAug 2021View details →

Data from: Topography, more than land cover, explains genetic diversity in a Neotropical savanna treefrog

Aim: Effective conservation policies rely on information about population genetic structure and the connectivity of remnants of suitable habitat. The interaction between natural and anthropogenic discontinuities across landscapes can uncover the relative contributions of different barriers to gene flow, with direct consequences for decision-making in conservation. Therefore, we aimed to quantify the relative roles of land cover and topographic variables on the population genetic differentiation and diversity of a stream-breeding savanna treefrog (Bokermannohyla ibitiguara) across its range. Location: Serra da Canastra mountain range, Cerrado of Minas Gerais State, Brazil. Methods: We collected and extracted DNA samples from 12 populations within and outside a strictly protected park, and used 17 microsatellite markers to assess genetic structure, among-population differentiation, and within-population diversity measures. We incorporated landscape data derived from digital models and satellite images to create connectivity matrices to correlate with genetic differentiation using Mantel tests. We used generalized linear models and path analyses to assess the roles of each landscape variable in shaping genetic diversity in this species. Results: Populations within and outside the park boundaries belonged to four genetic clusters. Most populations showed evidence of limited gene flow, with significant genetic differentiation, except for those within the park, which also had higher levels of allelic richness and heterozygosity. However, genetic differentiation among populations in this landscape was primarily explained by topographic complexity. Likewise, within-population genetic measures were best explained by models including elevation and topographic complexity, and not the amount of natural habitat or gallery forests. Main conclusions: Our results underscore that topography may be a strong historical factor shaping genetic structure among amphibian populations. Therefore, effective conservation strategies for endangered amphibians should avoid focusing exclusively on habitat suitability, and incorporate topographic complexity, which seems to be a key factor for the fauna of the extremely threatened Brazilian savanna.

opencc-by-4.0Dec 2019View details →

Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2016: Globe

Consolidated epoch 2016 from the Collection 3 of annual, global 100m land cover maps. Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a>  <a href="https://doi.org/10.5281/zenodo.3518036">2017</a>  <a href="https://doi.org/10.5281/zenodo.3518038">2018</a>  <a href="https://doi.org/10.5281/zenodo.3939050">2019</a> Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets. The maps include <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a> <a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a> <a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a>

opencc-zeroJan 2021View details →

Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania

Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth, and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs.

opencc-by-4.0Jan 2021View details →

Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River

This dataset accompanies the manuscript entitled "Cumulative impacts of land cover change and dams on the land-water interface of the Tocantins River". It contains shapefiles and geoTIFF files.

opencc-zeroDec 2015View details →

Data from: Exploiting Poisson additivity to predict fire frequency from maps of fire weather and land cover in boreal forests of Québec, Canada

Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.

opencc-zeroDec 2018View details →

Data from: Concordance in wetland physicochemical conditions, vegetation, and surrounding land cover is robust to data extraction approach

Concordance among wetland physicochemical conditions, vegetation, and surrounding land cover may result from the influence of land cover on the sources of plant propagules, on physicochemical conditions, and their subsequent determination of growing conditions. Alternatively, concordance may result if differences in climate, soils, and species pools are spatially confounded with differences in human population density and land conversion. Further, we expect that land cover within catchment boundaries will be more predictive than land cover in symmetrical buffers if runoff is a major pathway. We measured concordance between land cover, wetland vegetation and physicochemical conditions in 48 prairie pothole wetlands, controlling for inter-wetland distance. We contrasted land-cover data collected over a four-year period by multiple extraction approaches including topographically-delineated catchments and nested 30 m to 5,000 m radius buffers. After factoring out inter-wetland distance, physiochemical conditions were significantly concordant with land cover. Vegetation was not significantly concordant with land cover, though it was strongly and significantly concordant with physicochemical conditions. More, concordance was as strong when land cover was extracted from buffers <500 m in radius as from catchments, indicating the mechanism responsible is not topographically constrained. We conclude that local landscape structure does not directly influence wetland vegetation composition, but rather that vegetation depends on physicochemical conditions in the wetland (which are affected by surrounding land cover) and on regional factors such as the vegetation species pool and geographic gradients in climate, soil type, and land use.

opencc-zeroDec 2018View details →

Data from: Wildfire activity and land use drove 20th-century changes in forest cover in the Colorado front range

Recent shifts in global forest area highlight the importance of understanding the causes and consequences of forest change. To examine the influence of several potential drivers of forest cover change, we used supervised classifications of historical (1938–1940) and contemporary (2015) aerial imagery covering a 2932‐km2 study area in the northern Front Range (NFR) of Colorado and we linked observed changes in forest cover with abiotic factors, land use, and fire history. Forest cover in the NFR demonstrated broad‐scale changes 1938–2015 and overall cover increased 7.8%, but there was notable spatial variability and many sites also experienced Forest Loss. Recent (1978–2015) wildfire was the largest single driver of Forest Loss, with fires burning 14.3% of the total study area. Recently burned areas showed net losses of 36.9% forest cover. Reasons for Forest Gain were more complex, with elevation, past mining density, fire history, and topographic heat load index being the strongest predictors of increases in forest cover. Historical mining activity is one of the dominant anthropogenic impacts in ecosystems in the NFR and it had a complex, non‐linear relationship with 20th‐century changes in forest cover. Subalpine stands originating after stand‐replacing fires circa mid‐1800s to early 1900s showed some of the greatest gains in forest cover, indicative of slow and continuous post‐fire recovery through the 20th century. We also investigated factors such as land ownership, road density, forest management activities, and development intensity, which played detectable, but more minor roles in observed change. Twentieth‐century changes in forest cover throughout the NFR are a result of ecological disturbances and anthropogenic influences operating at varying timescales and overlaid upon variability in the abiotic environment.

opencc-zeroDec 2015View details →

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.

opencc-by-4.0Nov 2023View details →

Best learned models : Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features

Best learned models (Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models) for each region based on the classification data set DS-A with seed 0 (see description <a href="https://zenodo.org/deposit/7099785">here</a>). Models: GP non spatial, GP spatial (sum), GP spatial (product),RF non spatial, RF spatial, MLP non spatial, MLP spatial, LTAE non spatial, LTAE spatialFor further details see section VI-C of the pre-print article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.The implementation of the models is available in the <a href="https://gitlab.cesbio.omp.eu/belletv/land_cover_southfrance_gp">open source repository</a>.