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opencc-by-4.0Nov 2023View details →

Biodiversity impact assessment considering land use intensities and fragmentation

The data provide supplementary information for the paper entitled "Biodiversity impact assessment considering land use intensities and fragmentation". Coverage of the characterization factors<ul><li>5 species groups: plants, amphibians, birds, mammals, and reptiles</li><li>5 broad land use types: cropland, pasture, plantations, managed forests, and urban areas</li><li>3 land use intensities: minimal, light, intense (sometimes, intensity levels had to be merged because the data did not allow to differentiate between them)</li><li>825 terrestrial ecoregions of the world (according to WWF / Olson et al. 2001)</li></ul> FilesMain data<ul><li>CF.csv: characterization factors (CFs) for ecoregions and 5 species groups</li></ul>Taxonomically aggregated data<ul><li>CF_kingdom.csv: CFs aggregated from 5 species groups to plant and animal kingdoms</li><li>CF_domain.csv: CFs aggregated from plant and animal kingdoms to the domain of Eukaryota</li></ul>Spatially aggregated data<ul><li>CF_country.csv: CFs aggregated from ecoregions to countries</li><li>CF_global.csv: CFs aggregated from ecoregions to the globe</li></ul>Taxonomically and spatially aggregated data<ul><li>CF_kingdom_country.csv: CFs aggregated to countries and plant and animal kingdoms</li><li>CF_domain_country.csv: CFs aggregated to countries and the domain of Eukaryota</li><li>CF_kingdom_global.csv: CFs aggregated to the globe and plant and animal kingdoms</li><li>CF_domain_global.csv: CFs aggregated to the globe and the domain of Eukaryota</li></ul> UnitsCFs for land occupation: PDF/m2CFs for land transformation: PDF⋅yr/m2 Columns<ul><li>realm: 2-letter code to identify one of 8 biogeographical realms</li><li>biome: ID to identify one of 14 biomes</li><li>eco_id: ID to identify the ecoregion, combining numbers for the realm, biome, and ecoregion within each biome nested within each realm</li><li>eco_name: ecoregion name</li><li>species_group: species group</li><li>kingdom: kingdom as a taxonomic rank</li><li>habitat_id: ID to link to the land use type and intensity as used in land_use.tif. An ID with .5 represents a merged land use class considering the two habitats with the IDs when rounding the value both up and down.</li><li>habitat: land use type and intensity</li><li>CF_*: characterization factor</li><li>*_occ*: land occupation</li><li>*_tra*: land transformation</li><li>*_avg*: average approach</li><li>*_mar*: marginal approach</li><li>*_reg: regional relative species loss</li><li>*_glo: global relative species loss</li><li>*_rsd: relative standard deviation as a measure of spatial uncertainty due to aggregation (only concerns country and globally aggregated CFs)</li><li>quality_*: data quality, distinguishing between original estimates and the use of proxies</li><li>objectid: object id of the country</li><li>iso3cd: iso3 code of the country</li><li>romnam: romanized name of the country</li><li>m49code: M49 code of the country, a standard code used by the United Nations</li><li>weighting: aspect based on which the CFs were weighted (only concerns globally aggregated CFs)</li></ul> Data quality<ul><li>original: original estimate (for globally aggregated CFs: mostly original estimates, proxies only considered in areas with current land use)</li><li>proxy_intensity: intensity level was missing; CF was derived from another CF of the same ecoregion and land use type but different intensity level and scaled to the right intensity level</li><li>proxy_type: land use type was missing; CF was derived from the average regional CFs for light use in the same biome and the ecoregion-specific GEP and scaled to the right intensity level if needed</li><li>proxy_gep: global extinction probability (GEP) was missing (only concerns CFs for global relative species loss); GEP estimated based on average GEP per area unit in the same biome and the ecoregion area</li><li>proxy_partial: some species groups were missing but not all (only concerns taxonomically aggregated CFs); aggregation done based on partly original estimates and partly proxies</li><li>proxy_neighbours: country was missing (only concerns country-aggregated CFs); values were estimated based on the average of the three nearest neighbouring countries</li><li>proxy: proxies were considered even in areas without current land use (only concerns globally aggregated CFs)</li></ul>Note: proxies in country-aggregated CFs apply to at least one of the ecoregions overlapping with the country and not necessarily all ecoregions Land use type and intensity dataRaster file: land_use.tifSpatial resolution: 0.08333333, 0.08333333  (x, y)Spatial extent: -180, 180, -90, 90  (xmin, xmax, ymin, ymax)Coordinate reference system: WGS 84 (EPSG:4326) Codes<ol><li>Primary_vegetation_Minimal  (incl. sparse/no vegetation)</li><li>Cropland_Intense</li><li>Cropland_Light</li><li>Cropland_Minimal</li><li>Managed_forest_Intense</li><li>Managed_forest_Light</li><li>Managed_forest_Minimal</li><li>Pasture_Intense</li><li>Pasture_Light</li><li>Pasture_Minimal</li><li>Plantation_Intense</li><li>Plantation_Light</li><li>Plantation_Minimal</li><li>Urban_Intense</li><li>Urban_Light</li><li>Urban_Minimal</li></ol>

opencc-byOct 2024View details →

Land surface temperature (heatmaps) derived from earth observation data to assess thermal behaviour of 3 European cities: Milano, Logroño and Athens.

Next tables present the detail description of the datasets developed in REACHOUT to characterize heat phenomena at city level by providing an assessment of the land surface temperature (heatmaps) of three European cities: Milan, Logroño and Athens. TECNALIA is the responsible partner for these datasets. There is a wide range of methods that can be used to characterise the thermal behaviour of a city, each of them with its advantages and disadvantages. One of these methods uses the land surface temperature that is obtained from remote sensing observations. Although thermal indices are considered more suitable when characterising thermal comfort, still the LST can provide a useful information about the behaviour of a citiy’s surfaces and materials. This has implications for several applications such as urban energy efficiency or urban environmental health.  The input data used by the current version of the dataset came from Landsat 8. All the images acquired since 2013 by this satellite for Milan, Logroño and Athens were downloaded and processed to characterise not only the current (2019-2023) thermal behaviour of the city, but also its evolution considering the last seven 5-year windows. -    2013-2017 -    2014-2018 -    2015-2019 -    2016-2020 -    2017-2021 -    2018-2022 -    2019-2023 The input data used in this dataset come from Landsat 8 downloaded from <a href="https://earthexplorer.usgs.gov/">Earth Explorer (usgs.gov)</a>. The format of this dataset is organized in two ZIP format files: -    LANDSAT_8_L2SP_000000-milan_LST_peak.zip -    LANDSAT_8_L2SP_000000-logrono_LST_peak.zip -    LANDSAT_8_L2SP_000000-athens_LST_peak.zip Each of these zip files contain seven TIF images that represent the peak LST map according to the images of the above mentioned seven periods. The peak LST is obtained after getting the Annual Cycle Parameters of each of the periods and selecting a 30-day window centred on the day that the city reaches the maximum LST. The values of the images are in degree Celsius and nodata value is -9999.

Fig. 2 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 2. Linear relationship (solid line) and 95 % confidence interval (gray area) between habitat quality predicted by the BART model (x-axis) and shell height (H in millimeters, y-axis), derived from the linear mixed model.

opencc-by-4.0Dec 2021View details →

Fig. 4. Partial dependence plot for topographic Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 4. Partial dependence plot for topographic Fig. 5. Partial dependence plot for terrain roughness wetness index (TWI). index (tri).

Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 6. Partial dependence plot for pH water (phh2o). Fig. 7. Partial dependence plot for silt content (SLT).

Fig. 3. Partial dependence plot for BIO17 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 3. Partial dependence plot for BIO17 = Precipitation of Driest Quarter; gray area = 95 % confidence interval.

opencc-by-4.0Jun 2022View details →

Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series - Data and Material

This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it. Study area: Conguillío National Park, Chile Study period: 2016-2020 Description of datasets: conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation. evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile. Details about the methodology and results describing this dataset can be found in the following publication: Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.102862

opencc-by-4.0Mar 2018View details →

Summary for policymakers of the assessment report on land degradation and restoration of the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services: Figure SPM.1

The purpose of Figure  SPM.1 is to support the statement that land degradation occurs just about everywhere in the world (i.e. it is ‘pervasive’), takes many forms, and that examples of successful restoration are also widespread. The figure consists of a backdrop map of the world from a multiple land degradation perspective, showing the level of uncertainty between studies, overlaid with dots representing all the places specifically mentioned in the eight chapters of the main Assessment Report on Land Degradation and Restoration, including case studies of both degradation and restoration. Around the map are brief notes regarding the main forms of degradation encountered.

opencc-by-4.0Jun 2023View details →

PYFOREST-Land-Use-Assessment-Statstics

Results data from the Master's Capstone project PYFOREST. Forest cover and deforestation statistical analysis by political boundary data sets are provided.  See attached metadata for additional information, or refer to the <a href="https://github.com/cp-PYFOREST">GitHub project repository.</a>

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Data from: Bryophyte community assembly on young land uplift islands – dispersal and habitat filtering assessed using species traits

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publicMar 2021View details →

Output data from: From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions

This repository includes output data from the following article: Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A.H., Remane, I.A.D., Grinand, C., 2020. From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. Land Degrad Dev.; 32: 49– 65. <a href="https://doi.org/10.1002/ldr.3704">https://doi.org/10.1002/ldr.3704</a> This study aimed at characterizing and mapping the underlying factors (human or climatic) in land productivity changes over the 2000-2016 period, in order to assess land degradation in Mozambique. The methodology is based on remote sensing methodology. Land productivity change were first analyzed using MODIS NDVI time-series (2000–2016), and a two-step framework was then used to understand the main factors of these productivity changes, using climate times series (CHIRPS data for rainfall and CRU data for temperature), Land Use and Land Cover Change (LULCC) maps (Laurel project LULCC map), and ground knowledge. This repository includes raster data of annual land productivity change over the 2000-2016 period, annual land productivity climate factors, potential land productivity decrease factors and potential productivity increase factors. Data are available as GeoTIFF raster files at 250 m resolution in the UTM 37S projection (EPSG: 32737).

opencc-zeroMay 2020View details →

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Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect

In the context of growing societal demands for land based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested those hypotheses by investigating the bidirectional relations between changes in cropland area and intensity, using a global cross-country panel dataset over 1961-2016. We used a cointegration approach with additional tests to disentangle long and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, palm oil and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. Staple cereals such as wheat and rice manifested significant land sparing. In low-income countries, intensification driven by increases in total factor productivity was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yields increases for key tropical commodity crops. Our study design could allow addressing other complex long and short run causal dynamics in land and social-ecological systems.

opencc-by-4.0Mar 2017View details →

Integrative assessment of climate change-related impacts and risks on urban land

Shapefile data set estimating trends of mean annual terrestrial surface air temperature (°C) and mean annual total precipitation (mm) and several heat indicators for urban land, characterised by clusters of local spatial autocorrelation in regard to the age of urban area and the coefficient of variation of urban area extent over time.

opencc-by-4.0Jan 2024View details →

Data for: Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties

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Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).

Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 5. Partial dependence plot for terrain roughness index (tri).

Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance

Fig. 7. Partial dependence plot for silt content (SLT).