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High resolution and high cadence time series of land surface categories, land use land cover, and land use land cover changes
<p>A prototype of monthly, 10 m resolution land surface categories, land use land cover (LULC) cover, and LULC change maps derived from Sentinel-2 data over three areas within Belgium, Portugal, and Sicily for the period 2018-2020. The LULC and LULC change maps were independently validated by IIASA. All products were generated within the framework of the RapidAI4EO project, funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004356.</p> <p>The data description can be found below. The validation report of the LULC and LULC change maps can be found in validation_LULC.pdf and validation_change.pdf, respectively, and the validation dataset can be found in Lesiv <em>et al.</em> (2023).</p> <p><strong>Data description</strong></p> <p>Increasing the cadence of the land cover updates from the typical (multi-)annual to monthly cadence poses several challenges. First, several land cover types are difficult to discriminate without any knowledge of temporal dynamics. For instance, croplands are characterized by a dynamic of vegetation growth and a harvest period (i.e. cycles of bare soil, sparsely vegetated and vegetated periods). This contrasts with grasslands that often lack the harvest period resulting in a bare soil cover. Without this temporal information, it is difficult to distinguish a vegetated cropland field from grassland. Second, phenological changes may introduce a large intra-class variability and thus also confusion between classes. For example, the shedding of leaves during autumn or wilting of herbaceous vegetation in dry summer periods introduces spectral variability within land cover classes.</p> <p>To overcome these challenges, we developed a workflow with two main phases. The first phase aims to map land surface categories (LSC) at a monthly resolution. The next phase uses the resulting monthly LSC probability time series to classify land cover.</p> <p><strong><em>Land surface category (LSC)</em></strong></p> <p>These LSC represent basic, observable bio-geophysical properties (categories) of the Earth surface that can be predicted directly from individual monthly composites. LSC classes contain a set of vegetated and non-vegetated surface categories.</p> <p>Discrete LSC classification legend:</p> <table> <tbody> <tr> <td> <p>Map code</p> </td> <td> <p>Land cover class</p> </td> </tr> <tr> <td> <p>11</p> </td> <td> <p>Tree (leaf-on)</p> </td> </tr> <tr> <td> <p>12</p> </td> <td> <p>Shrubland (leaf-on)</p> </td> </tr> <tr> <td> <p>13</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>14</p> </td> <td> <p>Woody vegetation (leaf-off)</p> </td> </tr> <tr> <td> <p>15</p> </td> <td> <p>Wilted herbaceous vegetation</p> </td> </tr> <tr> <td> <p>21</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>22</p> </td> <td> <p>Water</p> </td> </tr> <tr> <td> <p>24</p> </td> <td> <p>Built-up</p> </td> </tr> </tbody> </table> <p>In order to predict the LSC, we trained a CatBoost model (Dorogush et al., 2018) using a DEM, spectral bands and vegetation indices, country, the timing (month) of the spectral data, and the pseudo-probability of a U-Net model trained to segment built-up surfaces as input. Labels were derived by post-processing the land cover labels of the ESA WorldCover product (Zanaga et al., 2021). Please note that the collection of these labels was suboptimal, likely having an impact on the LULC and change maps generated in the prototype.</p> <p><strong><em>Land use land cover</em></strong></p> <p>After predicting LSC over the three AOI’s, we trained a CatBoost model using the LSC probabilities over a window of one year, country, and the timing (month) as independent variable. The use of LSC probabilities over multiple months allows to incorporate information about dynamics, which is necessary to discriminate some classes (e.g. cropland and grassland or cropland and bare). Similar to the LSC labels, the LULC labels were derived from the ESA WorldCover product v100 (year 2020), resulting in a similar legend system.</p> <p>The use of a moving window approach to predict LULC allows to (i) incorporate temporal information that is necessary to discriminate land cover classes and (ii) is expected to lead to more consistent land cover maps. It however has the disadvantage that (i) no land cover predictions are available at the beginning and the end of the time series and (ii) the timing of the predicted land cover change is not always accurate. To resolve these issues, we applied a post-processing step that compares and integrates the LULC predictions and cleaned LSC predictions.</p> <p>Discrete LC classification legend:</p> <table> <tbody> <tr> <td> <p><strong>Map code</strong></p> </td> <td> <p><strong>Land cover class</strong></p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Tree cover</p> </td> </tr> <tr> <td> <p>20</p> </td> <td> <p>Shrubland</p> </td> </tr> <tr> <td> <p>30</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>40</p> </td> <td> <p>Cropland</p> </td> </tr> <tr> <td> <p>50</p> </td> <td> <p>Built-up</p> </td> </tr> <tr> <td> <p>60</p> </td> <td> <p>Bare/sparse vegetation</p> </td> </tr> <tr> <td> <p>80</p> </td> <td> <p>Permanent water bodies</p> </td> </tr> <tr> <td> <p>90</p> </td> <td> <p>Herbaceous wetland</p> </td> </tr> </tbody> </table> <p><strong><em>Land use land cover change </em></strong></p> <p>Monthly change maps were finally derived from the land cover maps. The pixel values within the change maps represent the percentage of pixels that changed with respect to the previous month over an area of 90x90m. The maps contain values between 0-100, with larger values assigned to larger change patches. A value of 100 indicates that all pixels within an area of 90x90m around the pixel were flagged as change.</p> <p><strong><em>Files</em></strong></p> <p>The zip files contain the following data:</p> <ul> <li>lsc.zip: land surface category maps over the three AOI’s</li> <li>lc.zip: LULC maps over the three AOI’s</li> <li>change.zip: change maps over the three AOI’s</li> </ul> <p>These maps are generated for each month over the period 2018-2020 for each of the tiles (see tiles.gpkg for an overview of all tiles). The files names use the following naming convention: “<em>tile</em>-<em>year</em>-<em>month</em>.tif”.</p> <p><strong><em>References</em></strong></p> <p>Myroslava Lesiv, Halyna Bun, & Martina Duerauer. (2023). Validation data set on land cover changes for RapidAI4EO project [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7825963 </p> <p>Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.</p> <p><em>Zanaga, D., </em><em>et al.</em><em>., 2021. ESA WorldCover 10 m 2020 v100. </em><a href="https://doi.org/10.5281/zenodo.5571936 "><em>https://doi.org/10.5281/zenodo.5571936 </em></a></p>
Data and code: Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology
<p>Zip folder conaining the data and code that support the findings of <em>Kuipers et al. (2023) Land use diversification may mitigate on-site land use impacts on mammal popultions and assemblages. Global Change Biology.</em></p> <p>The <em>Data_code.zip</em> folder contains four subfolders with the following files:</p> <ul> <li>Data_raw <ul> <li>AgriDiv_data.csv</li> <li>AgriDiv_metadata.docx</li> <li>Species_data.csv</li> <li>Species_metadata.docx</li> <li>Landscape_data.csv</li> <li>Landscape_metadata.docx</li> </ul> </li> <li>Data_derived <ul> <li>RIA_RSR_effect_sizes.csv</li> <li>MSA_effect_sizes.csv</li> </ul> </li> <li>Data_output <ul> <li>Response_estimation.csv</li> </ul> </li> <li>R_scripts <ul> <li>01_Effect_size_calculation.R</li> <li>02_Null_model_analysis.R</li> <li>03_Model_selection.R</li> <li>04_Model_analysis.R</li> <li>05_Response_estimation.R</li> <li>06_Figures.R</li> <li>README.md</li> </ul> </li> </ul>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
Land system changes on the Mediterranean basin between 2005 and 2015
<p>This is a shapefile obtained comparing the land system classification on 2005 and 2015 in order to highlight the main changes occurred. </p>
Lotic Intersite Nitrogen eXperiment II (LINX II): a cross-site study of the effects of anthropogenic land use change on nitrate uptake and retention in 72 streams across 8 different biomes (2003 – 2006).
The LINX II (Lotic Intersite Nitrogen eXperiment) project was designed to quantify the rates and mechanisms of nitrate retention in streams using stable isotope tracer additions. The study encompassed 72 stream reaches spread across 8 North American biomes. Within each biome, 9 streams were selected in three watershed land-use categories: 3 reference, 3 agricultural, and 3 urbanized. The core of the study was a 24-hour release of 15N- labeled nitrate. Prior to the isotope addition, physical, chemical and biological characteristics of the stream were measured. The measurements included, but were not limited to, dissolved nutrient concentrations, dissolved conservative tracer additions (to quantify hydraulic and hyporheic retention, velocity and discharge), standing stocks of primary uptake biota (including suspended and benthic particulate materials) as well as channel dimensions, photosynthetically active radiation, and water temperature. During the isotope release, whole stream rates of ecosystem metabolism were quantified (including quantification of re-aeration coefficients using tracer gas additions), and concentrations of 15N-labeled NO3, NH4, N2 and N2O were measured. Immediately following the isotope addition, 15N uptake by aquatic organisms was quantified by sampling biomass components on the stream bed. The data generated from these 72 stream reaches were used to develop a stream nitrogen retention model for each biome, which was expanded to entire drainage networks to predict nitrogen fluxes. The LINX II study demonstrated how biotic uptake of nitrate and denitrification increased with increasing nitrate concentrations. However, the efficiency of total uptake and denitrification actually declined with increasing nitrate concentrations (such as those seen on agricultural or urbanized streams), yielding higher rates of dissolved nitrogen exports downstream. The datasets presented here consist of the primary data collected by the LINX II study participants.
dh/dt data of the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018 "
<p>This is a dataset including dt/dt data for periods of T0–T2, T0–T1, and T1–T2, which used in the manuscript "Surface Elevation Change of Glaciers Along the Coast of Prudhoe Land, Northwestern Greenland from 1985 to 2018".</p>
Data for: Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial time scales
<p>At high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between e.g. the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms are poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long-term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change causing coastal water darkening has gradually delayed the spring bloom up to 1990 after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land-ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.</p>
Land use change converts temperate dryland landscape into a net methane source
<p>Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12% respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally.</p>
A SSP1-Low emission land use scenario based on LCM2019 for Scotland - Land Use Change only - baseline 2019 and scenario 2050 (nov22)
<p>This set of datasets contains a land use change scenario (2050) for Scotland within the scope of a SSP1 - Low emissions scenario (Shared Socio-Economic Pathways). For achieving a low-emission scenario, simulated land use change targeted woodland expansion (including silvo-arable and silvo-pastoral) and decreased grazing intensity, both land use changes also aimed at benefitting four aspects of ecosystem services: carbon storage through tree planting, emission reduction through deintensification, biodiversity enhancement through tree planting, and pollination to support food production.</p> <p>The baseline dataset is based on the Land Cover Map 2019 (Morton et al, 2020) aggregated at 100m resolution. Grazing intensity was added to it by using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). From the baseline dataset, the land use scenario map was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). The attached land use scenario map for 2050 is not an optimised result, but it is only one possibility that meets all the constraints stipulated for the scenario.</p> <p><strong>For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a></strong></p> <p>This analysis was conducted as part of the Land use Transformations (<a href="https://landusetransformations.hutton.ac.uk/">https://landusetransformations.hutton.ac.uk/</a>) project (JHI-C3-1) in the Scottish Government funded Strategic Research Programme 2022-27.</p> <p> </p> <p><strong>This version of the datasets only includes 100m cells with land use change (14% of Scotland). The full dataset has a non-commercial version of the licence (<a href="https://doi.org/10.5281/zenodo.10927157">https://doi.org/10.5281/zenodo.10927157</a>).</strong></p> <p> </p> <p><strong>-------------------------</strong></p> <p><strong>Datasets accessible here : <a href="https://openscience.hutton.ac.uk/dataset/low-emission-land-use-scenarios-land-use-change">SSP1-Low Emission Land Use Scenarios - land use change - Dataset - Natural Asset Register Data Portal (hutton.ac.uk)</a></strong></p> <p><strong>License</strong>: CC-BY-4.0 namely “Creative Commons Attribution 4.0 International“ <a name="_Hlk161153952"></a>(https://creativecommons.org/licenses/by/4.0/)</p> <p><strong>Copyright to display of the datasets</strong>: <br>“Contains Data owned by UK Centre for Ecology & Hydrology © Database Right/Copyright UKCEH. Based on Data from LPIS and JAC (Scottish Government, 2019).”</p> <p><strong>2 Main files :</strong></p> <ul> <li><strong>SSP1LEonLCM19_LUC_2019.tif</strong> : original land uses (2019) on which the scenario is based on. This land use map, of a resolution of 100m, is based on the Land Cover Map 2019 (Morton et al, 2020), estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions </strong>to the baseline dataset (SSP1LEonLCM19_LUC_2019.tif) : <ul> <li>100% of 100m cells: Land Cover Map 2019 (Morton et al, 2020)</li> <li>93.88% of 100m cells: the LCM 2019 was subdivided by grazing intensity using estimations of stocking rates from IACS (Wardell-Johnson, 2022), and grazing conservation thresholds (Chapman, 2007; FAS, 2021). This impacts the grasslands, heathers, bogs and arable classes.</li> <li>Estimated overall contributions: 65% UKCEH, 35% JHI</li> </ul> </li> </ul> <ul> <li><strong>SSP1LEonLCM19_LUC_2050.tif :</strong> land use scenario (2050), which is within the scope of a SSP1 - Low emissions scenario (Shared Scocio-Economic Pathways). The scenario was created using the SLM-OptionsTool, a land use change tool for Ecosystem Services based on the LandSFACTS model (Castellazzi et al, 2010). For a detailed description of the scenario refer to the following web storymap : <a href="https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9">https://storymaps.arcgis.com/stories/c3d3feff85f14460b6c973127089d6f9</a><u>. </u>This version of the datasets only includes 100m cells with land use change (14% of Scotland).<br><br><strong>Contributions</strong> to the scenario dataset (SSP1LEonLCM19_LUC_2050.tif) : <ul> <li>cf. contribution to the baseline (above)</li> <li>100% of 100m cells: modelled land use change</li> <li>Estimated overall contributions: 50% UKCEH, 50% JHI</li> </ul> </li> </ul> <p> </p> <p><strong>Main references:</strong></p> <p>Morton, R. D., Marston, C. G., O’Neil, A. W., & Rowland, C. S. (2020). Land Cover Map 2019 (25m rasterised land parcels, GB) [Data set]. NERC Environmental Information Data Centre. <a href="https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC">https://doi.org/10.5285/F15289DA-6424-4A5E-BD92-48C4D9C830CC</a></p> <p>Wardell-Johnson, D. (2022) Stocking rates derived from IACS 2019 version 4. <br>Based on data from Land Parcel Information System (2019) courtesy of Rural Payments and Inspections Division, Scottish Government.<br>Based on data from the June Agricultural Census (2019) courtesy of Rural and Environment Science and Analytical Services, Agricultural Statistics team, Scottish Government.</p> <p>Chapman, P. (2007) Conservation Grazing of Semi-natural Habitats. Technical note TN586. SAC tn586-conservation.pdf (sruc.ac.uk)</p> <p>FAS (2021) Practical Guide: Managing Peatlands and Upland Habitats. <a href="https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/">https://www.fas.scot/environment/biodiversity/protecting-scotlands-peatlands/practical-guide-managing-peatlands-and-upland-habitats/ </a>(author: Paul Chapman)</p> <p>Castellazzi, M.S.; Gimona, A. (2021) SLM-OptionsTool, a land use change tool for Ecosystem Services (arcgis toolbox and user manual included, part of RESAS Deliverable-O1.4.2ciiD27).</p> <p>Castellazzi, M.S., Matthews, J., Angevin, F., Sausse, C., Wood, G.A., Burgess, P.J., Brown I., Conrad, K.F., Perry J.N. (2010). Simulation scenarios of spatio-temporal arrangement of crops at the landscape scale . Environmental Modelling and Software 25, 1881-1889. <a href="https://doi.org/10.1016/j.envsoft.2010.04.006">https://doi.org/10.1016/j.envsoft.2010.04.006</a> </p> <p><a href="https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts">https://www.hutton.ac.uk/research/departments/information-and-computational-sciences/tools/landsfacts</a></p> <p> </p> <p> </p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)
<p><strong>Agricultural land resources – a global suitability evaluation (v3.0)</strong></p> <p>Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature. </p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.</p> <p><strong>Agricultural Suitability</strong></p> <p>The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km<sup>2</sup> at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.</p> <table> <caption><strong>Food, feed, fiber and first-generation bioenergy crops</strong></caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> <p><strong>Second-generation bioenergy crops</strong></p> </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> <p><strong>Growing Season Adaptation</strong></p> <p>The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.</p> <p><strong>Most Suitable Crop</strong></p> <p>The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publications:</p> <p>Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a></p> <p>Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a></p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a></p> <p>Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a></p> <p>Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, <em>J. Adv. Model. Earth Syst.</em>, 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>.</p> <p><strong>Improvements in v3.0</strong></p> <p>Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) <em>uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.</em></p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"
<p>Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land''</p> <p>The README.md file includes explanations about the data in the repository.</p>
Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon (Open)
<p>This work was carried out in the scope and with the support of the project: Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events (CLIMAX).</p> <p>The project consortium includes the following institutions: Centre National de la Recherche Scientifique CNRS/Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI-IFAECI) (Argentina-France); General Coordination of Earth Sciences /National Institute for Space Research (INPE) (Brazil); Institut de Recherche pour le Développement (IRD)/ Unité Mixte de Recherche (UMR 245) (France); Le Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (France); Potsdam Institute for Climate Impact Research (PIK) (Germany); Technical University of Munich (TUM) (Germany) and Wageningen University and Research (WUR) Netherlands). The project is sponsored by the Collaborative Research Action (CRA) on “Climate Predictability and Inter-Regional Linkages” of the Belmont Forum, launched in 2015.</p> <p>Climate variability patterns linking the South American Monsoon region, including Amazonia, with southeastern South America influence climate extremes and impact several societal sectors. More than 200 million people live in the study region, which is also one of the largest agricultural production regions of the world and home to the world’s second largest hydroelectric power plant.</p> <p>The objectives of CLIMAX include better understanding the combined role of remote and local drivers on South American climate variability from sub-seasonal to decadal timescales, and its impact on the occurrence and intensity of extreme events. Special focus is given to an improved understanding of the effects of land use changes from the Amazon to the subtropics and their impact on climate.</p> <ol> <li> <p><strong>EXPERIMENT DESIGN</strong></p> </li> </ol> <p>We used four models that are classified as Dynamic Global Vegetation Models (DGVMs) (Prentice et al., 2007; Rezende et al., 2015): Integrated Model of Land Surface Processes (INLAND) (Tourigny, 2014); Lund-Potsdam-Jena managed Land model version 4 (LPJmL4) (Schaphoff et al., 2018), Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) (Smith et al. 2001, Hickler et al., 2012), and Organising Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE) (Krinner et al., 2005).</p> <p>We used three forcings with climate data (GLDAS, GSWP3, and WATCH+WFDEI), Land Use Change (LUC) data and validation data (FLUXCOM (Remote sensor+meteorological data+artificial neural network approach), FLUXCOM (eddy covariance), MODIS (Light Use Efficiency), GLEAM, and TerraClimate (Rezende et al., 2022).</p> <p>We conducted two sets of simulation experiments with different values of CO2: 1) increasing CO2 from the pre-industrial period to 2010 named <strong>historical CO</strong><strong>2</strong> (<strong>hist CO</strong><strong>2</strong>); 2) constant concentration of 278 ppm of (pre-industrial) atmospheric CO2 named <strong>constant CO</strong><strong>2</strong><strong> (const CO</strong><strong>2</strong><strong>)</strong>. We ran both CO2 experiments under <strong>Land Use Change</strong> (<strong>LUC</strong>) and <strong>Potential Natural Vegetation </strong>(<strong>PNV</strong>) conditions. All combinations of CO2 and land use change resulted in four sets of simulation experiments per climate input: 1. <strong>LUC historical CO</strong><strong>2</strong>; 2. <strong>LUC constant CO</strong><strong>2</strong>; 3. <strong>PNV historical CO</strong><strong>2</strong>; 4. <strong>PNV constant CO</strong><strong>2 </strong> (Rezende et al., 2022).</p> <p><strong>2. DATA DESCRIPTION</strong></p> <p>The complete description of the data, including the climate forcing, LUC, the validation datasets, methodology, simulations, discussion and conclusion is in Rezende et al. (2022). This archive contains only the data description from the simulations (outputs) by the DGVMs.</p> <p><strong>2.1 SOFTWARE </strong></p> <p><br> The data were manipulated, worked, standardized, converted using the software: <strong>Climate Data Operators (cdo) version 1.7.0</strong>, <strong>Grid Analysis and Display System (Grads) (</strong><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a><strong>) version 2.0.2, and RStudio Desktop version</strong> 1.3.1093 <strong>(R Core Team, 2020)</strong>, through command lines and several scripts developed for this purpose. The figures were generated with <strong>Grads,</strong> and <strong>RStudio</strong>, and some images were enhanced with <strong>Gimp version 2.8.22</strong>. All the software used is freeware.</p> <p><strong> 2.2 PRIMARY DATA FROM SIMULATIONS</strong></p> <p>Data originating from the simulations are in monthly resolution, covering South America, with all the forcings. Despite data spanning over 1948-2010 or 1950-2010 our study focuses on the period 1981-2010.</p> <p><strong>Variables</strong>: Gross Primary Productivity (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p>The naming of the files is according to the following rules:</p> <p><strong>DGVM_forcing_vegetation cover_CO2 concentration_attribute</strong></p> <p><strong>DGVMs</strong>;</p> <p> InLand (INLAND)</p> <p> LPJ-G (LPJ-GUESS)</p> <p> LPJmL (LPJmL4)</p> <p> ORCHI (ORCHIDEE)</p> <p>forcings: </p> <p> gld - GLDAS</p> <p> gsw – GSWP3</p> <p> wat – WATCH+WFDEI</p> <p> </p> <p>vegetation cover:</p> <p> LU – Land Use Change</p> <p> PNV – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration:</p> <p> CO2 – historical CO2</p> <p> noCO2 – constant CO2 = 278 ppm</p> <p> </p> <p>variables:</p> <p> E – evaporation</p> <p> Et – transpiration</p> <p> gpp – Gross Primary Productivity</p> <p> npp - Net Primary Productivity (not used in the experiment)</p> <p> </p> <p><strong>Example</strong>:</p> <p> inLand_gld_LU_noCO2_E.nc</p> <p> </p> <p><strong> 2.3 SUPPLEMENTARY DATA </strong></p> <p>These interception loss data (mm month-1) were requested by a reviewer to complement the analysis and are available only for the LUC CO2 scenario and for the study region: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).</p> <p>Files are named according to the following rules:</p> <p>variable_season_forcing_DGVM_vegetation cover CO2 concentration_region</p> <p>variable:</p> <p> inter – loss by interception</p> <p> </p> <p>season:</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>forcings: </p> <p> gl - GLDAS</p> <p> gs – GSWP3</p> <p> wa – WATCH+WFDEI</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or – ORCHIDEE</p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p> inter_D_gl_in_LC_SA.nc</p> <p><strong>2.4 PROCESSED DATA</strong></p> <p>Processed data cover all scenarios and input data sets and are restricted to the study area: southern Amazon (70S and 140S of latitude and 660W and 510W of longitude).They are in seasonal resolution with averages for January-February-March-April (JFMA) (rainy season) and averages for June-July-August-September (JJAS). Each of the files contains the Gross Primary Productivity variables (GPP) (kg m-2 month-1), evaporation (mm month-1) and transpiration (mm month-1), and Net Primary Productivity (NPP) (kg m-2 month-1) (not used in our experiment).</p> <p> </p> <p>Files are named according to the following rules:</p> <p> </p> <p><strong>season_DGVM_forcing_vegetation cover CO2 concentration_region</strong></p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>Example:</p> <p>D_in_Gs_LN.nc</p> <p><strong>2.5 DERIVED DATA</strong></p> <p> </p> <p>The variable Water Use Efficiency (WUE) (kg m-2 mm-1 month-1) results from rate: GPP / Tr (transpiration) (Eq. 1). </p> <p> </p> <table> <tbody> <tr> <td> <p> WUE = GPP / Tr</p> </td> <td> <p>(Eq. 1)</p> </td> </tr> </tbody> </table> <p> </p> <p>These data refer to the study region: southern Amazon and apply to only one scenario: Land Use Change and historic CO2. Files are named naming of according to the following rules:</p> <p> </p> <p>Variable:</p> <p> WUE – Water Use Efficiency</p> <p> </p> <p>season</p> <p> D – dry season</p> <p> R – rainy season</p> <p> </p> <p>DGVMs:</p> <p> in - INLAND </p> <p> lg - LPJ-GUESS</p> <p> lm - LPJmL4</p> <p> or - ORCHIDEE</p> <p> </p> <p>forcings: </p> <p> Gl - GLDAS</p> <p> Gs – GSWP3</p> <p> Wa – WATCH+WFDEI </p> <p> </p> <p>vegetation cover </p> <p> L – Land Use Change</p> <p> P – Potential Natural Vegetation</p> <p> </p> <p>CO2 concentration</p> <p> C – historical CO2</p> <p> N – constant CO2 = 278 ppm</p> <p> </p> <p>region</p> <p> SA – southern Amazon</p> <p> </p> <p>Example:</p> <p>wue_D_lm_Gl_LC_SA.nc</p> <p> </p> <p><strong>How to cite this work</strong>:</p> <p>Rezende, Luiz F. C., Aline Castro, Celso Von Randow, Romina Ruscica, Boris Sakschewski, Phillip Papastefanou, Nicolas Viovy, Kirsten Thonicke, Anna Sörensson, Anja Rammig, Iracema F. A. Cavalcanti. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p><strong>References</strong></p> <p><a href="https://web.archive.org/web/20150407042441/http://www.iges.org/grads/gadoc/">Documentation of GrADS</a>. Center for Ocean-Land-Atmosphere Studies, Institute of Global Environment and Society,<a href="https://en.wikipedia.org/wiki/George_Mason_University"> George Mason University</a>. Archived from<a href="http://www.iges.org/grads/gadoc/"> the original</a> on 7 April 2015. Retrieved 14 March 2015.</p> <p>Hickler T. et al., 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species based dynamic vegetation model. Glob Ecol Biogeograp 21:50–63, <a href="https://doi.org/10.1111/j.1466-8238.2010.00613.x">https://doi.org/10.1111/j.1466-8238.2010.00613.x</a></p> <p>Krinner, G.et al., 2005. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, GB1015, doi:10.1029/2003GB002199.</p> <p>R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. </p> <p>Prentice IC (2007) Dynamic global vegetation modeling: quantifying terrestrial ecosystem responses to large-scale environmental change. In: Canadell J, Pataki D, Pitelka L (eds) Terrestrial ecosystems in a changing world. Springer, Berlin Heidelberg.</p> <p>Rezende, Luiz F. C. et al., 2015. Evolution and challenges of dynamic global vegetation models for some aspects of plant physiology and elevated atmospheric CO2. Int J Biometeorol., 2015, doi: 10.1007/s00484-015-1087-6.</p> <p>Rezende, Luiz F. C. et al., 2022. Impacts of Land Use Change and atmospheric CO2 on Gross Primary Productivity (GPP), evaporation and climate in Southern Amazon. Journal of Geophysical Research - Atmospheres (JGRA) - doi: 10.1029/2021JD034608. 2022.</p> <p>Schaphoff, S. et al., 2018. LPJmL4 – a dynamic global vegetation model with managed land –Part 1: Model description. Geosci. Model Dev., 11, 1343–1375, 2018, <a href="https://doi.org/10.5194/gmd-11-1343-2018">https://doi.org/10.5194/gmd-11-1343-2018</a>.</p> <p>Smith B. et al (2001) Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob Ecol Biogeograp 10:621–637</p> <p>Tourigny, E. (2014). Multi-scale fire modeling in the neotropics: coupling a land surface model to a high resolution fire spread model, considering land cover heterogeneity. Phd dissertation, Meteorology. INPE. Retrieved from http://urlib.net/sid.inpe.br/mtc-m21b/2014/05.30.00.36</p> <p><br> </p> <p> </p>
Data repository - Land use change and carbon emissions of a transformation to timber cities
<p>Data and model source code for the publication:</p> <p>Land use change and carbon emissions of a transformation to timber cities<br> (Nature Communications, 2022)<br> DOI: 10.1038/s41467-022-32244-w</p> <p>Abhijeet Mishra1,2,*, Florian Humpenöder1, Galina Churkina1, Christopher P.O. Reyer1, Felicitas Beier1,2, Benjamin Leon Bodirsky1, Hans Joachim Schellnhuber1, Hermann Lotze-Campen1,2, and Alexander Popp1</p> <p>1 Potsdam Institute for Climate Impact Research (PIK), Member of Leibniz Association, P.O.Box 60 12 03, 14412,6<br> Potsdam, Germany<br> 2 Humboldt University of Berlin, Department of Agricultural Economics, Unter den Linden 6, 10099 Berlin,8<br> Germany</p> <p>Abhijeet Mishra<br> *mishra@pik-potsdam.de<br> May 2022</p> <p>See README.txt for further details.</p>
Land use and land cover changes in the contiguous United States from 1630 to 2020
<p>Through integrating multi-source data including high-resolution remote sensing image-based land use and land cover (LULC) data, model-based land use products, and historical land archives, we reconstructed historical LULC at an annual time scale and 1 km x 1 km resolution in the contiguous United States (CONUS) from 1630 to 2020. Compared to other historical LULC datasets, our data can capture the major characters of LULC as well as provide more accurate information with higher spatial and temporal resolution. The LULC data can be used for regional studies in a wide range of topics including LULC impacts on the ecosystem, biodiversity, water resource, carbon and nitrogen cycles, and greenhouse gas emissions.</p>
Supporting data: Land-use change alters the mechanisms assembling rainforest mammal communities in Borneo
<p>These supporting data files were used in the analyses for a forthcoming<em> </em>paper (DOI to be confirmed). The two files consist of: </p> <p>1. Combined camera trap and live trap species-abundance matrix. Each row corresponds to a separate location, with species in different columns. Old-growth forest, logged forest and oil palm plantation locations have the prefixes "Old", "Log" and "Palm", respectively. Values in each cell are the number of independent captures (as defined in the paper) per seven days summed over the camera- and live-trapping protocols.</p> <p>2. Covariate data for each location, covering habitat structure, topography and local landscape context (covariates as defined in the paper). </p>
Differential impacts of land use change on multiple components of common Milkweed (Asclepias syriaca) pollination success
<p>Land-use change is one the greatest threats to biodiversity and is projected to increase in magnitude in the coming years, stressing the importance of better understanding how land-use change may affect vital ecosystem services, such as pollination. Past studies on the impact of land-use change have largely focused on only one aspect of the pollination process (e.g. pollinator composition, pollinator visitation, pollen transfer), potentially misrepresenting the full complexity of land-use effects on pollination services. Evaluating the impacts across multiple components of the pollination process can also help pinpoint the underlying mechanisms driving land-use change effects. This study evaluates how land-use change affects multiple aspects of the pollination process in common milkweed populations, including pollinator community composition, pollinator visitation rate, pollen removal, and pollen deposition. Overall, land-use change altered floral visitor composition, with small bees having a larger presence in developed areas. Insect visitation rate and pollen removal were also higher in more developed areas, perhaps suggesting a positive impact of land-use change. However, pollen deposition did not differ between developed and undeveloped sites. Our findings highlight the complexity evaluating land-use change effects on pollination, as these likely depend on the specific aspect of pollination evaluated and on the of the intensity of disturbance. Our study stresses the importance of evaluating multiple components of the pollination process in order to fully understand overall effects and mechanisms underlying land-use change effects on this vital ecosystem service.</p>
Chang'e 5 Landing Camera Crater Detection Dataset
<p>132 hand-labelled images from the Chang'e 5 Landing Camera. Visible impact craters in each image have their crater rim inscribed by a bounding ellipse.</p> <p>On average, there are approximately 50 labelled craters per image.</p> <p>The first 100 images of the landers descent were labelled - this is the intended training set.</p> <p>Every 10 images of the remaining 313 were then labelled - this is the intended testing set.</p> <p> </p> <p>File Descriptions:</p> <p>CE5-ellipse-labels: joblib dump of ellipse parameters per image.</p> <p>change5-*.json: Raw labels as produced by the labelling software of choice, Label Studio.</p> <p> </p> <p>Images:</p> <p>The images used in this work were produced and processed by the Ground Research and Application System (GRAS) of China's Lunar and Planetary Exploration Program (https://moon.bao.ac.cn). Specifically, the first 413 images from the Chang'e 5 landing camera level 2A were used. The images can be downloaded from here: <a href="https://moon.bao.ac.cn/ce5web/searchOrder_hyperSearchData.search?pid=CE5/LCAM/level/2A" target="_blank" rel="noopener">https://dx.doi.org/10.12350/CLPDS.GRAS.CE5.LCAM-2A.vA</a>.</p> <p> </p> <p>Reference and Acknowledgement:</p> <p>Users of these annotations and associated data are requested to cite both the original dataset source (https://moon.bao.ac.cn) and the following paper:</p> <p>Matthew Rodda, Sofia McLeod, Ky Cuong Pham, and Tat-Jun Chin. (2024). Camera-Pose Robust Crater Detection from Chang'e 5. doi: https://doi.org/10.48550/arXiv.2406.04569</p> <p> </p> <p>BibTeX:</p> <pre><code>@misc{rodda2024camerapose, title={Camera-Pose Robust Crater Detection from Chang'e 5}, author={Matthew Rodda and Sofia McLeod and Ky Cuong Pham and Tat-Jun Chin}, year={2024}, eprint={2406.04569}, archivePrefix={arXiv}, primaryClass={cs.CV} }</code></pre>
Dataset from "Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes"
<p>These are the output land cover and land cover change raster maps from the paper, "<a href="https://doi.org/10.3390/rs10020306">Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes</a>," published in Remote Sensing journal.</p> <p>ABSTRACT. Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km<sup>2</sup>; 39% of total area), as well as total forest conversion into agricultural plantations (3,214 km<sup>2</sup>). Gross forest loss (5,133 km<sup>2</sup>) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5,471 km<sup>2</sup>) and rubber (4,025 km<sup>2</sup>) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.</p>
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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.