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11 results for “landscape metrics”
Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multidimensional Impacts of Landscape Change in California's Sacramento–San Joaquin Delta
<p><strong>SUMMARY</strong><br> These data represent estimated mean value, standard error, and units for a range of metrics by land cover class in the Sacramento-San Joaquin Delta. Metrics are grouped into three major categories: Agricultural Livelihoods (including metrics for gross production value, number of agricultural jobs, and annual wages per employee), Water Quality (in terms of the application rates for pesticides identified as critical pesticides, groundwater contaminants, and those posing a high or moderate risk to aquatic organisms), and Climate Change Resilience (qualitative scores representing relative tolerance for heat, drought, and flood).</p> <p><strong>DESCRIPTION</strong><br> These data were developed to facilitate projecting the net impacts of land cover change scenarios on multiple metrics of interest to the Sacramento-San Joaquin Delta, including potential benefits and trade-offs. They were used in initial analyses of scenarios representing habitat restoration and perennial crop expansion, and they are required for using the R package "DeltaMultipleBenefits", which provides the code and work flow for repeating the initial analyses or analyzing new scenarios.</p> <p>For additional details about the development and applications of these data, please see: </p> <ul> <li>Dybala KE, et al. (In review) Multiple-benefit Conservation in Practice: A Framework for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta </li> <li>Dybala KE (2023) <em>DeltaMultipleBenefits: Projecting the Multiple Benefits of Land Cover Change in the Sacramento-San Joaquin River Delta.</em> R package version 1.0.0. doi: 10.5281/zenodo.7718620. Available from: https://pointblue.github.io/DeltaMultipleBenefits </li> </ul> <p><strong>FUNDING STATEMENT</strong><br> These data were developed as part of the project "Trade-offs and Co-benefits of Landscape Change on Bird Communities and Ecosystem Services in the Sacramento–San Joaquin River Delta", funded by Proposition 1 Delta Water Quality and Ecosystem Restoration Program, Grant Agreement Number – Q1996022, administered by the California Department of Fish and Wildlife.</p> <p><strong>POINT OF CONTACT</strong><br> Kristen Dybala, Point Blue Conservation Science, kdybala@pointblue.org</p> <p><strong>SUGGESTED CITATION</strong><br> Dybala KE. 2023. Multiple-benefit Conservation in Practice: Metrics Data for Quantifying Multi-dimensional Impacts of Landscape Change in California’s Sacramento–San Joaquin Delta. doi:10.5281/zenodo.7504874.</p> <p><strong>DATA DISTRIBUTION</strong><br> Zenodo (https://doi.org/10.5281/zenodo.7504874)</p> <p><strong>PROGRESS</strong><br> Complete, but note that the accompanying manuscript has not yet undergone peer-review, and thus these data may require future revision.</p> <p><strong>UPDATE FREQUENCY</strong><br> As Needed</p> <p><strong>DATE</strong><br> These data were compiled in 2022, based on data from the Quarterly Census of Employment and Wages 2014-2020 (EDD 2022), annual County Agricultural Commissioners Reports 2014-2020 (CDFA 2022), Pesticide Use Report Data 2014-2018 (CDPR 2022), and qualitative assessments of climate change resilience (Peterson et al. 2020, DSC 2021).</p> <p><strong>Literature Cited:</strong></p> <ul> <li>CDFA. 2022. County Ag Commissioners’ Data Listing. California Department of Food & Agriculture. Available from: https://www.nass.usda.gov/Statistics_by_State/California/Publications/AgComm/index.php</li> <li>CDPR. 2022. Pesticide Use Report Data. California Department of Pesticide Regulation. Available from: https://www.cdpr.ca.gov/docs/pur/purmain.htm</li> <li>DSC. 2021. Delta Adapts: Creating a Climate Resilient Future. Public Review Draft. Delta Stewardship Council. Available from https://deltacouncil.ca.gov/delta-plan/climate-change</li> <li>EDD. 2022. Quarterly Census of Employment and Wages (QCEW). California Employment Development Department. Available from: https://data.edd.ca.gov/Industry-Information-/Quarterly-Census-of-Employment-and-Wages-QCEW-/fisq-v939</li> <li>Peterson C, Marvinney E, Dybala K. 2020. Multiple Benefits from Agricultural and Natural Land Covers in the Central Valley, CA. Migratory Bird Conservation Partnership, Sacramento, CA. Dryad Dataset doi:10.25338/B8061X</li> </ul> <p><strong>FIELD DEFINITIONS</strong></p> <ul> <li><strong>METRIC_CATEGORY: </strong>Broad grouping assigned to each METRIC; one of Agricultural Livelihoods, Water Quality, or Climate Change Resilience</li> <li><strong>METRIC: </strong>Specific metric being estimated; one of Agricultural Jobs, Annual Wages, Gross Production Value, Drought, Flood, Heat, Critical Pesticides, Groundwater Contaminant, or Risk to Aquatic Organisms</li> <li><strong>UNIT: </strong>The units in which the <strong>METRIC </strong>is estimated</li> <li><strong>CODE_NAME:</strong> The land cover class or subclass for which the <strong>METRIC </strong>is estimated</li> <li><strong>LABEL: </strong>A more user-friendly version of <strong>CODE_NAME</strong>, useful for creating figures and tables</li> <li><strong>SCORE_MEAN:</strong> The mean value of each METRIC estimated for each land cover class or subclass</li> <li><strong>SCORE_SE: </strong>The standard error of the mean</li> </ul> <p><strong>ABBREVIATION DEFINITIONS</strong></p> <ul> <li><strong>FTE: </strong>full-time equivalents; refers to converting monthly agricultural jobs data to annual estimates by dividing by 12</li> <li><strong>ha:</strong> hectares</li> <li><strong>kg: </strong>kilograms</li> <li><strong>USD: </strong>U.S. dollars</li> <li><strong>yr: </strong>year</li> </ul> <p><strong>ACCESS & USE CONSTRAINTS</strong><br> CC-by-4.0 (https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>KEYWORDS</strong></p> <ul> <li><strong>Themes:</strong> agriculture, livelihoods, economy, water quality, pesticides, climate change, resilience, multiple-benefit conservation</li> <li><strong>Place:</strong> Sacramento-San Joaquin River Delta, Central Valley, California<br> </li> </ul>
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
Forest-related landscape metrics in LandKlif project
<p>We calculated forest-related landscape metrics which have influence on insect diversity. Based on the detailed Landklif map (dataset 11560 at LandKlif database, https://www.landklif.biozentrum.uni-wuerzburg.de), we classify coniferous forest, decideous forest, mixed forest, small wood, and transitional woodland-shrub as forest features. Sub land use class and origical classification was kept as well. This dataset includes the area percentage (landscape composition) of these classes as well as edge length between forest features and non-forest features, in a scale of 100, 200, 500, 1000, 1500 meter radius around the study plots, as well as in TK 25 quadrant scale. TK 25 quadrant is common name in Germany for the topographical map unit at a scale of 1:25000 designated by four-digit numbers, which has a long history (from 1875) and is been used as unit for geographical survey and biodiversity mapping (http://maps.snsb.info/TK25/).</p> <p>Detailed Landklif map was created by combining 3 different land cover maps to create a detailed land cover map for 6 km buffer area around landklif study plots. We used ATKIS 2019 land cover as basis, added details from Invekos 2019 and Corine 2018. We categorized the land cover into 6 classes, further subcategorized them into sub land use classes. The original classification from different sources are kept. In case of overlapping, the priority goes (from high to low): natural > forest > grassland > arable > urban > water. In case of overlapping between data source: transitional woodland-shrub from Corine > Invekos > ATKIS. Areas outside of Bayern are filled with only Corine data. The coordinate system of the shapefile is ETRS89 / UTM zone 32N (EPSG:25832). This dataset is not open access due to its sensitivity but can be reached (https://www.landklif.biozentrum.uni-wuerzburg.de/Download/ShowXml.aspx?DatasetId=11560) and requested via the LandKlif database.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Patch metrics and landscape patterns of forest disturbances at the beginning of the 20th Century
<h1>Summary:</h1> <p>The database consists of a compressed .CSV file containing structural information of forest disturbance patches identified between 2002 and 2014 using the Global Forest Change Tree Cover Loss Year dataset version 1.6 (Hansen et al, 2013) available at https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html. Each row in the database represents a patch (249,149,911 in total). The columns (15) represent the structural metrics calculated for each patch, as well as the landscape patterns identified using kmeans cluster analysis. </p> <p>The methods used for building this database are published in the paper: Acil, N., Sadler, J.P., Senf, C. <em>et al.</em> Landscape patterns in stand-replacing disturbances across the world’s forests. <em>Nat Sustain</em> <strong>8</strong>, 86–98 (2025). <a href="https://doi.org/10.1038/s41893-024-01450-3">https://doi.org/10.1038/s41893-024-01450-3</a></p> <p>Aggregated global maps of the patch metrics can be visualised in <a href="https://ee-treemort-disturbances-nacil.projects.earthengine.app/view/patchmetrics2002-2014">Google Earth Engine</a> and accessed in the asset "http://projects/ee-treemort-disturbances-nacil/assets/PatchMetrics_Means_nonLU_2002-2014/". </p> <p>Some of the scripts associated with this project are hosted in <a href="https://github.com/N-Acil/GlobalForestDisturbances_PatchMetrics">GitHub</a> and <a href="https://code.earthengine.google.com/?accept_repo=users/NXA807/%20GlobalForestDisturbances_PatchMetrics">Google Earth Engine</a>.</p> <p>Additional scripts and data will be made available upon request.</p> <p> </p> <p> </p> <h1>Database structure: </h1> <h2>Patch metrics</h2> <h3>Occurrence: </h3> <p>Patch form and year were retrieved from the Global Forest Change tree cover loss year dataset version 1.6 (Hansen et al, 2013).</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>PID</strong></td> <td>Patch unique identifier in the format Tile_Year_PatchNumber (e.g. 01U_02_00000001).</td> <td> </td> <td>Characters</td> <td> </td> </tr> <tr> <td><strong>X_INT_deg</strong></td> <td>Longitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-180-180]</td> </tr> <tr> <td><strong>Y_INT_deg</strong></td> <td>Latitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-90-90]</td> </tr> <tr> <td><strong>YEAR_maj</strong></td> <td>Year of patch majority occurrence. </td> <td> </td> <td>Integer</td> <td>[2-14]</td> </tr> <tr> <td><strong>YEAR_n</strong></td> <td>Number of years over which the patch exhibited continuous growth.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> </tbody> </table> <h3>Metrics: </h3> <p>These patch and landscape metrics were calculated from the patch delineated.</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>AREA_G_ha</strong></td> <td>Patch geodesic area</td> <td>Hectares</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PERIM_G_m</strong></td> <td>Patch geodesic perimeter</td> <td>Meters</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PARA</strong></td> <td>Perimeter-area ratio</td> <td> </td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>SHAPE</strong></td> <td>Shape index</td> <td> </td> <td>Float</td> <td>>=1</td> </tr> <tr> <td><strong>ELONG</strong></td> <td>Elongation index</td> <td> </td> <td>Float</td> <td>[0-1[</td> </tr> <tr> <td><strong>FRAC</strong></td> <td>Fractal dimension index</td> <td> </td> <td>Float</td> <td>[1-2]</td> </tr> <tr> <td><strong>NN5000_T0_n</strong></td> <td>Number of patches assigned the same year within 5 km radius.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> <tr> <td><strong>NN5000_AREA_T0_perc</strong><strong><br></strong></td> <td>Percent of the total area disturbed over the period 2001-2018 within 5 km radius from the focal patch centroid.</td> <td>%</td> <td>Float</td> <td>[0-100]</td> </tr> </tbody> </table> <h3>Clusters:</h3> <p>Cluster identification was performed using AREA_G_ha, YEAR_n, SHAPE, ELONG, NN5000_T0_n and NN5000_AREA_T0_perc. </p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>CLUSTER_CODE</strong></td> <td>Code assigned to each cluster</td> <td> </td> <td>Integer</td> <td>[1-4]</td> </tr> <tr> <td><strong>CLUSTER_LABEL</strong></td> <td>Name given to the cluster identified. </td> <td> </td> <td>Character</td> <td> <ul> <li>Small-isolated</li> <li>Clustered</li> <li>Complex</li> <li>Large-multiyear</li> </ul> </td> </tr> </tbody> </table> <p> </p>
Supporting dataset for the paper : " Hydro-geomorphic metrics for high resolution fluvial landscape analysis"
<p>This repository contains all the original data supporting the results of Bernard et al., 2021: "Consistent hydro-geomorphic indicators for high resolution topographic analysis".<br> The parameter used to perform hydraulic simulations are also available.<br> </p>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
Day of burning maps and burn severity landscape metrics in the southwestern United States 2002-2020
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Data from: Local and landscape metrics identify opportunities for conserving cavity-nesting birds in a rapidly urbanizing ecoregion
Urban centers are rapidly expanding globally, resulting in regional forest-cover transformations that shift from temperate forest biomes to a heterogeneous mix of urban development, forest patches, and agriculture. Data on habitat use within remaining forest patches embedded across land use types, particularly in urban land use, are needed to optimize conservation strategies as urban growth continues. In the rapidly urbanizing southern Piedmont, USA, small pine patches have become more frequent across the landscape and are found embedded within second-growth forest, agricultural, and urban land use matrices. We used point-count surveys and N-mixture models to determine the effect of patch- and landscape-scale drivers on cavity-nesting bird abundance, including the threatened Brown-headed Nuthatch (Sitta pusilla), in pine forest patches. Model-averaged estimates suggest Brown-headed Nuthatches are more abundant in large patches in a heterogeneous matrix that includes urban residential development. Three other cavity-nesting species declined in abundance as a function of reduced canopy cover. White-breasted Nuthatches increased and Tufted Titmice decreased in abundance in response to patch area. By identifying factors that predict abundance at local and landscape scales for ecologically sensitive and generalist species, we can more effectively contribute to regional conservation efforts in urban ecosystems, extending conservation in practice beyond protected areas.
Data from: Local and landscape metrics identify opportunities for conserving cavity-nesting birds in a rapidly urbanizing ecoregion
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Data from: A comparison of individual-based genetic distance metrics for landscape genetics
A major aim of landscape genetics is to understand how landscapes resist gene flow and thereby influence population genetic structure. An empirical understanding of this process provides a wealth of information that can be used to guide conservation and management of species in fragmented landscapes, and also to predict how landscape change may affect population viability. Statistical approaches to infer the true model among competing alternatives are based on the strength of the relationship between pairwise genetic distances and landscape distances among sampled individuals in a population. A variety of methods have been devised to quantify individual genetic distances, but no study has yet compared their relative performance when used for model selection in landscape genetics. In this study, we used population genetic simulations to assess the accuracy of 16 individual-based genetic distance metrics under varying sample sizes and degree of population genetic structure. We found most metrics performed well when sample size and genetic structure was high. However, it was much more challenging to infer the true model when sample size and genetic structure was low. Under these conditions, we found genetic distance metrics based on principal components analysis were the most accurate (though several other metrics performed similarly), but only when they were derived from multiple principal component axes (the optimal number varied depending on the degree of population genetic structure). Our results provide guidance for which genetic distance metrics maximize model selection accuracy and thereby better inform conservation and management decisions based upon landscape genetic analysis.
Data from: A comparison of individual-based genetic distance metrics for landscape genetics
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