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128 results for “landscape mapping”
Data from: Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast
<p>This directory contains data used in the paper "Classification and mapping of low-statured 'shrubland' cover types in<br> post-agricultural landscapes of the US Northeast" published in the journal International Journal of Remote Sensing. No data was collected specifically for this study; instead, we made use of publicly available LiDAR data and LANDSAT imagery.</p>
Annual maps of swidden agriculture landscape derived from MODIS vegetation index in northern Laos during 2001-2020
<p>This document (Word) is a brief introduction about the resultant maps of swidden agricultural landscape in northern Laos during 2001-2020 derived from the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) time-series products using a threshold method. For more information about the dataset, one can refer to the paper entitled “Swidden agriculture landscape mapping using MODIS vegetation index time series and its spatio-temporal dynamics in northern Laos” published in Remote Sensing. The format of this dataset (swidden agriculture landscape) is raster (.tif) with an attribute value of 1. It has a spatial resolution of 250m×250m and covers eleven provinces (including Bokeo, Borikhamxay, Huaphanh, Luangnamtha, Luangprabang, Oudomxay, Phongsaly, Vientiane, Xayaboury, Xaysomboon and Xieng-khuang) and one prefecture (Vientiane, Figure 1). The geographic projection is WGS_1984_UTM_Zone_48N.</p>
Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance
<p><strong>Processed datasets for publication at <em>BMC Genomics</em>.</strong></p> <p>We exposed human primary monocytes from healthy donors (n=6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2hr LPS; LPS2) and two models of endotoxin tolerance: repeated stimulations (6+6hr; LPS6:6) and prolonged exposure to endotoxin (24hr LPS; LPS24). Another subset of monocytes was left untreated (naïve; UT). We performed total RNA-seq and ATAC-seq for monocytes across these treatment conditions.</p> <p>The following processed datasets include the raw and normalised count data for each gene or ATAC peak, and the bedGraph and bigWig format for genome-wide signal data. [ bigWig files for RNA-seq (monocytes_*_mean.bw); bigWig files for ATAC-seq (Monocyte_ATAC_*_mean_normalised.by.1/size.factor.bw); bedgraph files for eRNA visualization (*RPKM_FR.bedgraph.gz); raw and normalised count files for ATAC-seq and RNA-seq data (Monocyte.featureCounts_RNAseq.txt.gz; Monocyte_filtered_log2.normalized_RNAseq_counts.txt.gz; Meta.file.txt.gz; Monocyte_raw.counts_ATACseq.txt.gz.).]</p> <p><strong>Data curator:</strong></p> <p>Ping Zhang</p> <p> </p>
Data from: Participatory mapping reveals biocultural and nature values in the shared landscape of a Nordic UNESCO Biosphere Reserve
Open the record for dataset details and reuse information.
High-resolution mapping of the period landscape reveals polymorphism in cell cycle frequency tuning
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Day of burning maps and burn severity landscape metrics in the southwestern United States 2002-2020
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Landscape Age Map for Hog Island, Northampton, Co., Virginia. 1852-1985
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Data from: Mapping Tasmania's cultural landscapes: using habitat suitability modelling of archaeological sites as a landscape history tool
Aim: Understanding past distributions of people across the landscape is key to understanding how people used, affected and related to the natural environment. Here we use habitat suitability modelling to represent the landscape distribution of Tasmanian Aboriginal archaeological sites and assess the implications for patterns of past human activity. Location: Tasmania, Australia Methods: We developed a RandomForest 'habitat suitability' model of site records in the Tasmanian Aboriginal Heritage Register. We applied a best-effort bias correction, considered 31 predictor variables relating to climate, topography and resource proximity, and used a variable selection procedure to optimise the final model. Model uncertainty was assessed via bootstrapping and we ran an analogous MAXENT model as a cross-validation exercise. Results: The results from the RandomForest and MAXENT models are highly congruent. The strongest environmental predictors of site occurrence include distance to coast, elevation, soil clay content, topographic roughness and distance to inland water. The highest habitat suitability scores are distributed across a wide range of environments in central, northern and eastern Tasmania, including coastal areas, inland water body margins, and forests and savannas in the drier parts of Tasmania. With the exception of coastal areas much of western Tasmania has low habitat suitability scores, consistent with theories of low-density Holocene Tasmanian Aboriginal settlement in this region. Main conclusions: Our modelling suggests Tasmanian Aboriginal people occupied a heterogeneity of habitats but targeted coastal areas around the whole island, and drier, less steep, and/or open forest and savanna environments in the central lowlands. The western interior was identified as being rarely used by Aboriginal people in the Holocene, with the exception of isolated pockets of habitat; yet whether this is a true reflection of Aboriginal resource use demands increased archaeological surveys, particularly in the Tasmanian Wilderness World Heritage Area.
Data from: Landscape connectivity for wildlife: development and validation of multi-species linkage maps
The ability to identify regions of high functional connectivity for multiple wildlife species is of conservation interest with respect to forest management and corridor planning. We present a method that does not require independent, field-collected data, is insensitive to the placement of source and destination sites (nodes) for modeling connectivity, and does not require the selection of a focal species. In the first step of our approach, we created a cost surface that represented permeability of the landscape to movement for a suite of species. We randomly selected nodes around the perimeter of the buffered study area and used circuit theory to connect pairs of nodes. When the buffer was removed, the resulting current density map represented, for each grid cell, the probability of use by moving animals. We found that using nodes that were randomly located around the perimeter of the buffered study area was less biased by node placement than randomly selecting nodes within the study area. We also found that a buffer of ≥ 20% of the study area width was sufficient to remove the effects of node placement on current density. We tested our method by creating a map of connectivity in the Algonquin to Adirondack region in eastern North America, and we validated the map with independently collected data. We found that amphibians and reptiles were more likely to cross roads in areas of high current density, and fishers (Pekania [Martes] pennanti) used areas with high current density within their home ranges. Our approach provides an efficient and cost-effective method of predicting areas with relatively high functional connectivity.
Data from: Incorporating interspecific competition into species-distribution mapping by upward scaling of small-scale model projections to the landscape
There are a number of overarching questions and debate in the scientific community concerning the importance of biotic interactions in species distribution models at large spatial scales. In this paper, we present a framework for revising the potential distribution of tree species native to the Western Ecoregion of Nova Scotia, Canada, by integrating the long-term effects of interspecific competition into an existing abiotic-factor-based definition of potential species distribution (PSD). The PSD model is developed by combining spatially explicit data of individualistic species' response to normalized incident photosynthetically active radiation, soil water content, and growing degree days. A revised PSD model adds biomass output simulated over a 100-year timeframe with a robust forest gap model and scaled up to the landscape using a forestland classification technique. To demonstrate the method, we applied the calculation to the natural range of 16 target tree species as found in 1,240 provincial forest-inventory plots. The revised PSD model, with the long-term effects of interspecific competition accounted for, predicted that eastern hemlock (Tsuga canadensis), American beech (Fagus grandifolia), white birch (Betula papyrifera), red oak (Quercus rubra), sugar maple (Acer saccharum), and trembling aspen (Populus tremuloides) would experience a significant decline in their original distribution compared with balsam fir (Abies balsamea), black spruce (Picea mariana), red spruce (Picea rubens), red maple (Acer rubrum L.), and yellow birch (Betula alleghaniensis). True model accuracy improved from 64.2% with original PSD evaluations to 81.7% with revised PSD. Kappa statistics slightly increased from 0.26 (fair) to 0.41 (moderate) for original and revised PSDs, respectively.
Mapping firescapes for wild and prescribed fire management: a landscape classification approach
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Mapping the Landscape of Open Source Health Economic Models: A Systematic Database Review and Analysis
<p><span>Health economic models are crucial for health technology assessment (HTA) to evaluate the value of medical interventions. Open source models (OSMs), where source code and calculations are publicly accessible, enhance transparency, efficiency, credibility, and reproducibility. This study systematically reviews databases to map the landscape of available OSMs in health economics.</span></p>
Species-level tree crown maps improve predictions of tree recruit abundance in a tropical landscape
<p>Predicting forest recovery at landscape scales will aid forest restoration efforts. The first step in successful forest recovery is tree recruitment. Forecasts of tree recruit abundance, derived from the landscape-scale distribution of seed sources (i.e. adult trees), could assist efforts to identify sites with high potential for natural regeneration. However, previous work has revealed wide variation in the effect of seed sources on seedling abundance, from positive to no effect. We quantified the relationship between adult tree seed sources and tree recruits, and predicted where natural recruitment would occur in a fragmented tropical agricultural landscape. We integrated species-specific tree crown maps generated from hyperspectral imagery and property boundaries data on individual property ownership with field data on the spatial distribution of tree recruits from five species. We then developed hierarchical Bayesian models to predict landscape-scale recruit abundance. Our models revealed that species-specific maps of tree crowns improved recruit abundance predictions. Conspecific crown area had a much stronger impact on recruitment abundance (8.00% increase in recruit abundance when conspecific tree density increases from zero to one tree; 95% CI: 0.80 to 11.57%) than heterospecific crown area (0.03% increase with the addition of a single heterospecific tree, 95% CI: -0.60 to 0.68%).Individual property ownership was also an important predictor of recruit abundance: the best performing model had varying effects of conspecific and heterospecific crown area on recruit abundance, depending on individual property ownership. We demonstrate how novel remote sensing approaches and cadastral data can be used to generate high-resolution and landscape-level maps of tree recruit abundance. Spatial models parameterized with field, cadastral, and remote sensing data are poised to assist decision support for forest landscape restoration.</p>
African Digital Research Repositories: Mapping the Landscape
<p>This data set accompanies the text at doi <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. // Correspondence: JH: <a href="mailto:info@africarxiv.org">info@africarxiv.org</a>, SK: <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a></p> <p><strong>Visual Map: </strong><a href="https://kumu.io/access2perspectives/african-digital-research-repositories"><strong>https://kumu.io/access2perspectives/african-digital-research-repositories</strong></a><strong> <br> Dataset: </strong><a href="https://tinyurl.com/African-Research-Repositories"><strong>https://tinyurl.com/African-Research-Repositories</strong></a><br> <strong>Archived at </strong><a href="https://info.africarxiv.org/african-digital-research-repositories/"><strong>https://info.africarxiv.org/african-digital-research-repositories/</strong></a><strong> <br> Submission form: </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a></p> <p> </p> <p><strong>Licensing</strong>: Text and Visual Map – CC-BY-SA 4.0 // Dataset – CC0 (Public Domain) // The licensing of each database is determined by the database itself</p> <p>Preprint doi: <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. <br> Data set doi: <a href="http://doi.org/10.5281/zenodo.3732172">10.5281/zenodo.3732172</a> // available in different formats (pdf, xls, ods, csv)</p> <p> </p> <p><strong><a href="https://info.africarxiv.org">AfricarXiv</a> in collaboration with the <a href="https://www.internationalafricaninstitute.org">International African Institute </a>(IAI) presents an interactive map of African digital research literature repositories. This drew from IAI’s earlier work from 2016 onwards to identify and list Africa-based institutional repositories that focused on identifying repositories based in African university libraries. Our earlier resources are available at </strong><a href="https://www.internationalafricaninstitute.org/repositories"><strong>https://www.internationalafricaninstitute.org/repositories</strong></a><strong>.</strong></p> <p><strong>The interactive map extends the work of the IAI to include organizational, governmental, and international repositories. It also maps the interactions between research repositories. In this dataset, we focus on institutional repositories for scholarly works, as defined by Wikipedia contributors (March 2020).</strong><br> </p> <p><strong>Objective</strong></p> <p>The map of African digital repositories was created as a resource to be used in activities addressing the following aims:</p> <ol> <li> <p>Improving the discoverability of African research and publications </p> </li> <li> <p>Enhance the interoperability of existing and emerging African repositories</p> </li> <li> <p>Identify ways through which digital scholarly search engines can enhance the discoverability of African research</p> </li> </ol> <p>We promote the dissemination of research-based knowledge from African repositories as part of a bigger landscape that also includes online journals, research data repositories, and scholarly publishers to enhance the interconnectivity and accessibility of such repositories across and beyond the African continent and to contribute to a more granular understanding of the continent’s scholarly resources. </p> <p> </p> <p><strong>Data archiving and maintenance</strong></p> <p>The map and corresponding dataset are hosted on the AfricArXiv website under ‘Resources’ at <a href="https://info.africarxiv.org/african-digital-research-repositories/">https://info.africarxiv.org/african-digital-research-repositories/</a>. The listing is not exhaustive and therefore we encourage any repositories relevant for the African continent not listed here to the <strong>submission form at </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a>, or to notify the International African Institute (email <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a>). Both AfricArXiv and IAI will continue to maintain the list of repositories as a resource for African researchers and other stakeholders including international African studies communities.</p>
Data from: Classification and mapping of low-statured 'shrubland' cover types in post-agricultural landscapes of the US Northeast
<p>Novel plant communities reshape landscapes and pose challenges for land cover classification and mapping that can constrain research and stewardship efforts. In the US Northeast, emergence of low-statured woody vegetation, or 'shrublands', instead of secondary forests in post-agricultural landscapes is well-documented by field studies, but poorly understood from a landscape perspective, which limits the ability to systematically study and manage these lands. To address gaps in classification/mapping of low-statured cover types where they have been historically rare, we developed models to predict 'shrubland' distributions at 30m resolution across New York State (NYS), using machine learning and model ensembling techniques to integrate remote sensing of structural (airborne LIDAR) and optical (satellite imagery) properties of vegetation cover. We first classified a 1m canopy height model (CHM), derived from a "patchwork" of available LIDAR coverages, to define shrubland presence/absence. Next, these non-contiguous maps were used to train a model ensemble based on temporally-segmented imagery to predict 'shrubland' probability for the entire study landscape (NYS). Approximately 2.5% of the CHM coverage area was classified as shrubland. Models using Landsat predictors trained on the classified CHM were effective at identifying shrubland (test set AUC=0.893, real-world AUC=0.904), in discriminating between shrub/young forest and other cover classes, and produced qualitatively sensible maps, even when extending beyond the original training data. After ground-truthing, we expect these shrubland maps and models will have many research and stewardship applications including wildlife conservation, invasive species mitigation and natural climate solutions. Overall our results compared favorably in terms of accuracy with existing LULC products, suggesting that incorporation of airborne LiDAR, even from a discontinuous patchwork of coverages, can improve LULC classification of historically rare but increasingly prevalent 'shrubland' habitats across broader areas.</p>
Data set to Remote sensing-supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem
<p>This data set is part of the article Wraase et al. (2022): Remote sensing -supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem. Remote sensing in Ecology and Conservation. (https://doi.org/10.1002/RSE2.303)</p> <p>The repository contains a Readme file ("readme.txt") and two additional folders labeled: “input data” and “script”.<br> <br> The first folder contains 13 data files further divided into three subfolders “cca_analysis”, “main_modelling_prc_texture_idx” and “vectors”. Data formats are .csv format for all tables, .rds files for model objects from R and .shp format for all vector data.</p> <p>The second folder contains all 31 R-scripts necessary to do the analysis, as described in the article. Additionally, the folder is further categorized into five subfolders equivalent to the main analysis operations: “cca_analysis”, “landsat_temp_modelling”, “main_modelling_prc”, “maxent” and “texture_idx”.</p>
Supplementary material 1 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
List of the 19 socio-demographic and ecological parameters used for the study area clustering and their values for each municipality of the study area.
Supplementary material 2 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
Eigenvalue, percentage of explained variablity and accumulated percentage of explained variability of the five factors (with an eingenvalue higher than 1) result of the PCA analysis and used for the HCA analysis.
Supplementary material 3 from: Palomo-Campesino S, Palomo I, Moreno J, González J (2018) Characterising the rural-urban gradient through the participatory mapping of ecosystem services: insights for landscape planning. One Ecosystem 3: e24487. https://doi.org/10.3897/oneeco.3.e24487
Factors' loadings for each of the 19 socio-demographic and ecological variables analysed. Values in bold are statistically significant at P>0.05.
Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB
<p>The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:</p> <p><strong>"Mapping forests with different levels of naturalness using machine learning and landscape data mining"</strong> (under review)</p> <p>Abstract:</p> <p><em>To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.</em></p> <p>This database was compiled from the following sources:</p> <p>1. <strong>HCVF</strong>. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip">https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip</a></p> <p>2. <strong>NMD</strong>. National Land Cover Data. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/">https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/</a></p> <p>3. <strong>DEM</strong>. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.</p> <p>source: <a href="https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/">https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/</a></p> <p>4. <strong>GFC</strong>. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.</p> <p>source: <a href="https://glad.earthengine.app">https://glad.earthengine.app</a></p> <p>5. <strong>LIGHTS</strong>. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).</p> <p>source: <a href="https://doi.org/10.6084/m9.figshare.9828827.v2">https://doi.org/10.6084/m9.figshare.9828827.v2</a></p> <p>6. <strong>POPULATION</strong>. Total Population in Sweden. Statistics Sweden.</p> <p>source: <a href="https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/">https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/</a></p> <p> </p> <p>To learn more about the GRASS GIS database structure, see:</p> <p><a href="https://grass.osgeo.org/grass82/manuals/grass_database.html">https://grass.osgeo.org/grass82/manuals/grass_database.html</a></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.