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36 results for “landcover”
Massachusetts Historical Landcover and Census Data 1640-1999
An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).
Circumarctic Landcover Units
<p>Landcover units have been derived from Copernicus Sentinel-1 und Sentinel-2 data acquired between 2016 and 2024 for the Arctic tundra biome and selected adjacent areas. 23 units of which 20 represent different vegetation characteristics and soil conditions are differentiated. The units have been identified with K-means over a representative transect and in a second step retrieved across the entire Arctic north of the treeline. The description of the units is based on several thousand samples from vegetation surveys and soil probes.</p> <p>The units are supplied at 10m resolution in 17 irregular tiles. Auxiliary data for quality information and input acquisition dates are supplied in a separate file (polygons with attributes). Units are documented in detail in the product user guide (PUG) and in Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024.</p> <p>For separation of artificial surfaces it is recommended to combine the units with: Bartsch, A., Widhalm, B., von Baeckmann, C., Efimova, A., Tanguy, R., & Pointner, G. (2023). Sentinel-1/2 derived Arctic Coastal Human Impact dataset (SACHI) (v2.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.10160636" rel="noopener">https://doi.org/10.5281/zenodo.10160636</a></p>
Coastal landcover change and the associated biomass trends in the mid-Atlantic sea-level rise hotspot
Climate change is driving worldwide landscape reorganization. In the coastal ecosystem, climate-driven sea level rise is forcing landward marsh migration and forest die-off, with potentially large consequences on coastal carbon balance. Here we used 30 m resolution Landsat images to study coastal landcover change from 1984 to 2020, and analyzed the Normalized Difference Vegetation Index (NDVI, a proxy of plant biomass) trend between 1984 and 2020 in the mid-Atlantic sea level rise hotspot. Our study region stretches across the entire Chesapeake Bay and the Delaware Bay to encompass all areas between 0-5m above sea level (total area ~12,500 km2). Specifically, the data package includes 3 raster datasets derived from the Landsat images. All datasets cover the identical mid-Atlantic region and have identical spatial resolution of 30 m. The two landcover datasets, named as 'Landcover_year1984.tif' and'Landcover_year2020.tif', respectively refer to landcover map in 1984 and 2020. Each of the maps has 7 landcover classes differentiated by different integers, and they are: water (0), farmland (1), urban area(2), upland forest (3), transition forest (4), marsh (5) and sandbar (6). Both landcover maps were generated using a combination of random forest classification and manual delineation, and the resultswere validated with high-resolution aerial photos and satellite images with an overall mapping accuracybeyond 90%. The third raster dataset, named as 'NDVItrend_1984to2020.tif', is the NDVI trend map. The value of each 30 by 30 m pixel in the map represents the slope of the NDVI trendline estimated using annual peak-growing season NDVI images acquired between 1984 and 2020. Negative values in the dataset represent decreases of NDVI (i.e. biomass loss, or ecosystem browning) from 1984 and 2020,whereas positive values correspond to an increase of NDVI (i.e. biomass gain, or ecosystem greening)between 1984 and 2020. The data package is completed.
A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19
<p><strong>Overview</strong></p> <p>This dataset is the repository for the following paper submitted to <em>Data in Brief</em>:</p> <p>Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. <em>Data in Brief</em> (submitted: December 2023).</p> <p>The <em>Data in Brief</em> article contains the supplement information and is the related data paper to:</p> <p>Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. <em>Journal of Arid Environments</em> (revision submitted: December 2023).</p> <p><strong>Description/abstract</strong></p> <p>The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant. </p> <p><strong>Folder structure</strong></p> <p>The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files: </p> <p>“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.</p> <p>“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.</p> <p>“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).</p> <p>“yield_productivity” contains .csv files of yield information for all countries listed above.</p> <p>“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).</p> <p>“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.</p> <p>“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”. </p> <p><strong>Code structure</strong></p> <p>1_MODIS_NDVI_hdf_file_extraction.R </p> <p><br>This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.</p> <p><br>2_MERGE_MODIS_tiles.R</p> <p><br>In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory__MODIS/merged").</p> <p><br>3_CROP_MODIS_merged_tiles.R</p> <p><br>Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. <br>The repository provides the already clipped and merged NDVI datasets.</p> <p><br>4_TREND_analysis_NDVI.R</p> <p><br>Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.<br>To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique. </p> <p><br>5_BUILT_UP_change_raster.R</p> <p><br>Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022. </p> <p><br>6_POPULATION_numbers_plot.R</p> <p><br>For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration. </p> <p><br>7_YIELD_plot.R</p> <p><br>In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R. </p> <p><br>8_GLDAS_read_extract_trend</p> <p><br>The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). <br>Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.<br>From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).<br>From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables. </p> <p><br>(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.</p> <p>___</p> <p>Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael Kempf</p> <p>___</p> <p><strong>Acknowledgements</strong></p> <p><span><span><span><span>I would like to thank three </span></span></span></span><span><span><span><span><span>anonymous</span></span></span></span></span><span><span><span><span> reviewers for their constructive comments and suggestions that sharpened the paper in the Journal of Arid Environments. I am particularly grateful to the Swiss National Science Foundation (SNSF/SNF) to fund my research project </span></span></span></span><span><span><span><span><em><span>EXOCHAINS - Exploring Holocene Climate Change and Human Innovations across Eurasia</span></em></span></span></span></span><span><span><span><span> at the University of Basel under grant number </span></span></span></span><span><span><span><span>TMPFP2_217358.</span></span></span></span></p> <p> </p> <p><span><span><span><span>__</span></span></span></span></p> <p><br>All data underlying the results of this article are publicly available on the internet:</p> <p>GLDAS Noah Land Surface Model L4 data: NASA's Earth Science Data Systems (ESDS) Program, https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 09th December 2023); </p> <p><br>Country borders: https://www.geoboundaries.org (last accessed 7th of March 2023) and Natural Earth https://www.naturalearthdata.com/ (last accessed 5th of December 2023);</p> <p><br>FAOstats (Food and Agriculture Organisation of the United Nations: https://www.fao.org/faostat/en/#data/QCL (last accessed 7th of March 2023);</p> <p><br>Global Human Settlement Layer datasets (GHSL): https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 7th of March 2023);</p> <p><br>Population development: <br>FAO, https://www.fao.org/countryprofiles/index/en/?iso3=JOR (last accessed 4th of March 2023); <br>the Worldbank, https://www.worldbank.org/en/home (last accessed: 04th of March 2023); <br>Worlddata.info, https://www.worlddata.info/asia/palestine/populationgrowth.php (last accessed 4th of March 2023);</p> <p><br>Water demand and population numbers (Tab. 1): https://www.fao.org/faostat/en/#data/OA; https://databank.worldbank.org/reports.aspx?source=world-development-indicators# (last accessed 13th of December 2023);</p> <p><br>MODIS: Earthdata server of the United States Geological Survey (USGS), MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006, https://lpdaac.usgs.gov/products/mod13q1v061/ (last accessed 7th of March 2023).</p> <p><br>Competing interests statement:<br>The author declares no conflict of interest.<br>The author has no relevant financial or non-financial interests to disclose.<br>Data availability: All data underlying the analyses are freely available on the internet and where applicable, sources are cited in the text.<br>Ethical approval: This article does not contain any studies with human participants performed by any of the authors.<br>Informed consent: This article does not contain any studies with human participants performed by any of the authors.</p>
Circumpolar Landcover Units
<p><strong>This record is obsolete.</strong></p> <p><strong>A new version has been published under <a href="https://zenodo.org/records/14235736">https://zenodo.org/records/14235736</a></strong></p> <p>Landcover units have been derived from Copernicus Sentinel-1 und Sentinel-2 data acquired between 2016 and 2022 for the Arctic tundra biome. 23 units of which 20 represent different vegetation characteristics and soil conditions are provided. The units have been identified with K-means over a representative transect and in a second step retrieved across the entire Arctic north of the treeline. The description of the units is based on several thousand samples from vegetation surveys and soil probes.</p> <p>The units are supplied at 10m resolution in nine irregular tiles. Auxiliary data for quality information and input acquisition dates are supplied in a separate shape file. The majority within 1kmx1km areas is supplied as csv (centre points). Units are documented in detail in Bartsch, A., Efimova, A., Widhalm, B., Muri, X., von Baeckmann, C., Bergstedt, H., Ermokhina, K., Hugelius, G., Heim, B., and Leibman, M.: Circumarctic land cover diversity considering wetness gradients, Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, 2024.</p>
Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Landcover Class Aggregation data 1996
First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.
Data from: Remote sensing and landcover in ring-necked pheasant research: A review of data sources and scales
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Landcover change analysis of the McKenzie Basin for the Maps and Locals (MALS) project.
This dataset was developed for use in an analysis of landcover change in the McKenzie Basin for the Maps and Locals (MALS) project. MALs is funded by LTER Social Science Supplement grants of the National Science Foundation.
1989 General Landcover Map of the Eastern Shore of Virginia
This generalized landcover classification of the Eastern Shore of Virginia focuses on major landforms and habitats of the Virginia Coast Reserve LTER. The classification is based on a Landsat 5 Thematic Mapper (TM) satellite image taken on July 25, 1989 at 15:14 GMT (11:14 EDT), which is roughly concurrent with mid-tide water levels as predicted for Wachapreague, VA (L=8:25, H=14:52 EDT). The classification divides the landscape into three components: water (code 1); salt marsh (code 2); and upland/mainland areas (code 3). The primary purpose of this dataset is to provide a background map layer over which VCRLTER researchers and students can plot individual research site locations for figures intended for publications and presentations.
JCURA 2024 PyLC Image Testing Set and Landcover Masks
<p>This is the image and landcover mask testing set created for the 2024 JCURA project "Mountains of Confusion: Evaluating Image Enhancement to Improve AI Landscape Classification" by Larissa Bron. </p> <p>The Python Landscape Classification tool (PyLC) [https://github.com/scrose/pylc] was trained using 95 images from two researcher's work, [Fortin (2018)](https://dspace.library.uvic.ca/items/0a911eb0-53bf-4a82-a75a-8b6949c28edd) and [Jean et al. (2015)](https://ieeexplore.ieee.org/document/7045940), and tested with 19 images that were a combination of 11 images from Fortin and Jean withheld from training and 8 images from the Landscapes in Motion project [(Higgs et al., (2020))](https://friresearch.ca/publications/advances-visual-applications-visualizing-quantifying-landscape-change-sw-alberta-using). This core set of training and testing images was used to train the colour models of PyLC with repeat images, then these images were grayscaled and added to training the grayscale models of PyLC with historic images. </p> <p>For this JCURA project, the testing image set was updated to: 1) Incorporate images from more geographic areas, and 2) Remove images with reference land cover masks with large errors. </p> <p>The files included are 24 test images (.jpg, .tiff, .tif) and 24 manually annotated land cover masks (.png). File names indicate: Researcher_ImageIdentifier_Time where time is whether the image is a historic capture or a repeat. </p>
Orinoco fires and lightning strikes, landcover
<p>Lightning strike correlation with wildfire hotspots. Landcover analysis for Inverbosques current or prospective holdings.</p>
Landcover map for the central region of the Yukon-Kuskokwim Delta, Alaska
<p>Climate change is causing an intensification in tundra fires across the Arctic, including the unprecedented 2015 fires in the Yukon-Kuskokwim (YK) Delta. The YK Delta contains extensive surface waters (∼33% cover) and significant quantities of organic carbon, much of which is stored in vulnerable permafrost. Inland aquatic ecosystems act as hot-spots for landscape CO<sub>2</sub> and CH<sub>4</sub> emissions and likely represent a significant component of the Arctic carbon balance, yet aquatic fluxes of CO<sub>2</sub> and CH<sub>4</sub> are also some of the most uncertain. We measured dissolved CH<sub>4 </sub>and CO<sub>2</sub> concentrations (n = 364), in surface waters from different types of waterbodies during summers from 2016 to 2019. We used Sentinel-2 multispectral imagery to classify landcover types and area burned in contributing watersheds. We develop a model using machine learning to assess how waterbody properties (size, shape, and landscape properties), environmental conditions (O<sub>2</sub>, temperature), and surface water chemistry (dissolved organic carbon composition, nutrient concentrations) help predict in situ observations of CH<sub>4 </sub>and CO<sub>2</sub> concentrations across deltaic waterbodies. CO<sub>2</sub> concentrations were negatively related to waterbody size and positively related to waterbody edge effects. CH<sub>4</sub> concentrations were primarily related to organic matter quantity and composition. Waterbodies in burned watersheds appeared to be less carbon limited and had longer soil water residence times than in unburned watersheds. Our results illustrate the importance of small lakes for regional carbon emissions and demonstrate the need for a mechanistic understanding of the drivers of greenhouse gasses in small waterbodies.</p>
Data for: Effects of landcover on mesocarnivore density along an urban to rural gradient
<p>Human development has major implications for wildlife populations. Urban-exploiter species can benefit from human subsidized resources, whereas urban-avoider species can vanish from wildlife communities in highly developed areas. Therefore, understanding how the density of different species varies in response to landcover changes associated with human development can provide important insight into how wildlife communities are likely to change and provide a starting point for predicting the consequences of those changes. Here, we estimated the population density of five common mesocarnivore species (coyote (<em>Canis</em> <em>latrans</em>), bobcat (<em>Lynx</em> <em>rufus</em>), red fox (<em>Vulpes</em> <em>vulpes</em>), raccoon (<em>Procyon</em> <em>lotor</em>), and Virginia opossum (<em>Didelphis</em> <em>virginiana</em>)) along an urban to rural gradient in the greater Fayetteville Area, Northwest Arkansas, USA between November 2021, and March 2022. At each study site, we applied the Random Encounter Model (REM) to data from motion-triggered cameras to calculate the density of our five focal species. Coyote density ranged from 0–3.47 with a mean of 0.4 individuals/km2. Raccoon density ranged from 0–93.26 with a mean of 4.2 individuals/ km<sup>2</sup>. Bobcat density ranged from 0–8.87 with a mean of 0.33 individuals/km<sup>2</sup>. Opossum density ranged from 0–27.35 with a mean of 0.76 individuals/km<sup>2</sup>. Red fox density ranged from 0–0.73, with a mean of 0.02 individuals/km<sup>2</sup>. We used generalized linear models to evaluate the density of each species against environmental and anthropogenic variables. Coyotes and raccoons occurred in the greatest densities in areas with high anthropogenic noise levels, suggesting that both species are synanthropic and able to co-exist in areas of high human activity. Alternatively, Virginia opossum and red fox densities were greatest in open, developed areas (lawns, golf courses, cemeteries, and parks) and were absent (red fox) or rare (opossum) in natural areas. We found no evidence that bobcat density varied along the urban to rural gradient studied, but this lack of evidence may have been driven by the small spatial scale of many of our sites in relation to space needs of this wide-ranging species. The density estimates we report based on game camera data of unmarked animals were consistent with reports from the literature for these same species derived from traditional methods, providing additional support to the REM as a viable, non-invasive method to calculate density of unmarked species.</p>
Texas Statewide Landcover, Ecological Systems, and Percentage Canopy Classifications (10-meter Resolution) (2021)
<p>Statewide landcover, ecosystem, and percentage canopy cover classifications for Texas were mapped at a spatial resolution of 10-meters. Classifications were run for 16 zones across the state corresponding to available cloud-free multitemporal Sentinel-2 satellite imagery for each zone. For each zone, RandomForest classifications were run using data stacks comprised of spectral bands from three dates (winter, early growing season, late growing season/leaf-off) of imagery, as well as multiple vegetation indices (NDVI, EVI2, MSAVI2). Over 50,000 training points were selected from ground trips and high-resolution aerial image surveys to run the entire pixel-based classification. The overlapping zones were merged using a feathering algorithm to produce a single statewide land-cover classification map. The landcover mapping results were further refined using multiple spatial masks (e.g., urban, water, crop) along with logical rulesets and ancillary data. To map ecological systems, the land-cover classification was then intersected with an enduring features dataset derived primarily from soil map-unit polygons (gSSURGO) and other geophysical variables. Additionally, we produced a statewide percentage canopy cover map at a 10-meter spatial resolution using multiple techniques. For the western 2/3 of the Texas, a nested machine learning approach was used (<a href="https://doi.org/10.1016/j.rse.2020.111748">Sunde et al., 2020</a>), and for the eastern 1/3 of the state, a combination of LiDAR derived training data and machine learning was used.</p> <p>This dataset includes four items:</p> <ol> <li><strong>"TX_10m_landcover_2021.zip" - Statewide landcover classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_ecoclass_map_2021.zip" - Statewide ecological systems classification for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_10m_canopy_cover_2021.zip" - Statewide percentage canopy cover map for Texas (10-meter spatial resolution)</strong></li> <li><strong>"TX_lu_ecoclass_key.xlsx" - Table containing keys for the mapped landcover, canopy, and ecological systems classes</strong></li> </ol> <p>(To facilitate display of the datasets within ESRI software, .lyr files are included in the respective archive folders)</p> <p><em>This work was funded by the Texas A&M Forest Service.</em></p>
Data for: Effects of landcover on mesocarnivore density along an urban to rural gradient
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Human-dominated landcover corresponds to spatial variation in Mourning Dove reproductive output across the United States.
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Landcover map for the central region of the Yukon-Kuskokwim Delta, Alaska
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Southern Appalachia NLCD Landcover
Satellite imagery (mosaicked +ETM and TM imagery satellite imagery from late 2005 through mid-2006) were downloaded from the web for spring, leafoff, and leafon conditions (Fig. 2). From these layers we derived tasseled cap bands 1,2,3. Other data used included digital elevation models (DEMs), DEM slope calculations, and 2006 NAPP DOQQs (used for training sets). We classified imagery using classification and regression tree method (CART) using a combination of Landsat TM imagery and ancillary data. The specific CART program used was See5, which implements a gain ratio criterion in tree development and pruning (Quinlan, 1993). We used boosting and cross-validation to improve classification accuracy (boosting) and estimate accuracy (cross-validation).
Dataset related to publication: Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States
<p>Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States</p> <p>Kátia Fernandes and Sen g. Young</p> <p>doi: 10.3389/fenvs.2024.1433920</p> <p>Abstract</p> <p>Satellite detection of active fires have contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions and how to better manage the use of fires as a tool. In this study we use 12 years (2012-2023) of active fire data combined with landcover information in the South-Central United States to derive a monthly, <strong>open access dataset of categorized fires.</strong> This is done by calculating a fire predominance index used to rank fire prone land covers, which are then grouped into four main landscapes: grassland, forest, wildland and crop fires. County level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it is found that during climatological fire peak-month, SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation, and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK) we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fires response to precipitation anomalies can be attributed to the use of prescribed burning, which are normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires on the other hand, are set to efficiently burn residue and practiced more intensely in drier years than in wetter, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.</p>
Iowa herptile detection histories and landcover metrics
<p>Predictions of species occurrence allow land managers to focus conservation efforts on locations where species are most likely to occur. Such analyses are rare for herpetofauna compared to other taxa, despite increasing evidence that herptile populations are declining because of land cover change and habitat fragmentation. Our objective was to create predictions of occupancy and colonization probabilities for 15 herptiles of greatest conservation need in Iowa. From 2006–2014, we surveyed 295 properties throughout Iowa for herptile presence using timed visual-encounter surveys, coverboards, and aquatic traps. Data were analyzed using robust design occupancy modeling with landscape-level covariates. Occupancy ranged from 0.01 (95% CI = -0.01, 0.03) for prairie ringneck snake (<em>Diadophis punctatus arnyi</em>) to 0.90 (95% CI = 0.898, 0.904) for northern leopard frog (<em>Lithobates pipiens</em>). Occupancy for most species correlated to landscape features at the 1-km scale. General patterns of species' occupancy included the negative effects of agricultural features and the positive effects of water features on turtles and frogs. Colonization probabilities ranged from 0.007 (95% CI = 0.006, 0.008) for spiny softshell turtle (<em>Apalone spinifera</em>) to 0.82 (95% CI = 0.62, 1.0) for western fox snake (<em>Pantherophis ramspotti</em>). Colonization probabilities for most species were best explained by the effects of water and grassland landscape features. Predictive models had strong support (AUC > 0.70) for six out of 15 species (40%), including all three turtles studied. Our results provide estimates of occupancy and colonization probabilities and spatial predictions of occurrence for herptiles of greatest conservation need across the state of Iowa.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.