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19 results for “National-scale.”
Summertime methane and carbon dioxide emission rates and associated variables from a national-scale survey of 146 reservoirs in the United States, 2016-2023
Reservoirs are globally important sources of greenhouse gases, but the magnitude of their emissions is highly uncertain. Here we present data for 146 reservoirs from two surveys of reservoir methane and carbon dioxide emissions, one at the regional scale in the midwestern United States and one at the national scale in the conterminous United States, plus data from one reservoir in Washington and another in Puerto Rico. At all reservoirs, ebullitive and diffusive emissions and basic physiochemistry were measured at 15-70 locations during one 22 to 64-hour period during the summers of 2016-2023, with four reservoirs revisited a second time. Concomitant water chemistry measurements were also made at an index site. The dataset is comprised of two geospatial files and seven .csv files containing greenhouse gas emissions, water chemistry, morphology, and other relevant data. These data comprise the largest multi-reservoir emissions dataset ever assembled using consistent measurement methods.
The first 10-m China's national-scale sandy beach map in 2022 derived from Sentinel-2 imagery
<p>This is the first 10-meter national scale beach map dataset of China. Based on the cloudless Sentinel-2 images for the whole year of 2022, we use the image classification method to draw a 10-meter beach map of China. The projection coordinate system of this data is WGS_1984_UTM_Zone_51N and the geographic coordinate system is GCS_WGS_1984.<br>The "Shape_Leng" field in the data set represents the circumference of the beach, the "Shape_Area" field represents the area of the beach, and the "Province" field represents the province of each independent beach.</p>
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data (User guide V2.4)
<p>The<strong> User Guide V2.4 </strong>of the SinoLC-1 land-cover product. The SinoLC-1 was created by the Low-to-High Network (L2HNet), which can be found at: <strong><a href="https://doi.org/10.1016/j.isprsjprs.2022.08.008">L2HNet</a></strong>. A more detailed description of the data can be found in the<strong> <a href="https://doi.org/10.5194/essd-15-4749-2023">paper</a>.</strong> More related work can be found at my <strong><a href="https://lizhuohong.github.io/lzh/">homepage</a>.</strong></p> <p><a href="https://zenodo.org/search?q=parent.id%3A7707461&f=allversions%3Atrue&l=list&p=1&s=10&sort=version"><strong>Click to check all the data versions and download the data (点击查看/下载所有数据版本)</strong></a></p> <p><strong>NOTE: If you have any data needs, questions, or technical issues, contact us at </strong><a href="http://ashelee@whu.edu.cn"><strong>ashelee@whu.edu.cn</strong></a><strong> (Zhuohong Li, 李卓鸿).</strong></p> <p>The land-cover mapping method with Python code is open-access at <a href="https://github.com/LiZhuoHong/Paraformer/"><strong>Code link</strong></a>. You can now update the high-resolution land-cover map by yourself with the code! The updated method is accepted by CVPR 2024 (<strong><a href="https://arxiv.org/abs/2403.02746">Paper link</a></strong>).</p> <p><strong>我们的最新制图算法被计算机视觉顶会CVPR2024接收(<a href="https://arxiv.org/abs/2403.02746">Paper link</a>),代码开源在:<a href="https://github.com/LiZhuoHong/Paraformer/">Code link</a>,您可以利用该代码高效地更新自己数据集的高分土地覆盖图。</strong></p> <p><strong>Citation format of the paper:</strong><br>Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780, 2023. </p> <p>Li, Z., Zhang, H., Lu, F., Xue, R., Yang, G. and Zhang, L.: Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels, <em>ISPRS Journal of Photogrammetry and Remote Sensing</em>. <em>192</em>, pp.244-267, 2022.</p> <p><strong>BibTex format of the paper:</strong></p> <blockquote> <pre>@article{li2023sinolc, title={SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data}, author={Li, Zhuohong and He, Wei and Cheng, Mofan and Hu, Jingxin and Yang, Guangyi and Zhang, Hongyan}, journal={Earth System Science Data}, volume={15}, number={11}, pages={4749--4780}, year={2023}, publisher={Copernicus Publications G{\"o}ttingen, Germany} }</pre> <pre>@article{li2022breaking, title={Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels}, author={Li, Zhuohong and Zhang, Hongyan and Lu, Fangxiao and Xue, Ruoyao and Yang, Guangyi and Zhang, Liangpei}, journal={ISPRS Journal of Photogrammetry and Remote Sensing}, volume={192}, pages={244--267}, year={2022}, publisher={Elsevier} }</pre> </blockquote>
Data from: Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting
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National-scale, remotely sensed lake trophic state (LTS-US) 1984-2020
Lake trophic state is a key water quality property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance and lake morphometric data to create the first compendium of lake trophic state for more than 56,000 lakes of at least 10 ha in size throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
<p class="BodyText1">Population dynamics can be highly variable in the face of environmental heterogeneity, and understanding this variation is central in the study of ecology. Robust management decisions require that we understand how populations respond to management at a range of scales, and under a broad suite of conditions. Population models are potentially valuable tools in addressing this challenge. However, without adequate data, models can fail to produce useful results. Populations of arable weeds are particularly problematic in this respect, as they are widespread and their dynamics are extremely variable. Owing to the inherent cost of collecting data, most studies of weed population dynamics are derived from localized experiments under a small range of environmental conditions, limiting the extent to which variance in population dynamics can be measured. Density-structured models provide a route to rapid, large-scale analysis of population dynamics, and can expand the scale of ecological models that are directly tied to data. Here we extend previous density-structured models to include environmental heterogeneity, variation in management, and to account for inter-population variation. We develop, parameterize and test hierarchical density-structured models for a common agricultural weed, black-grass (<i>Alopecurus myosuroides</i>). We model the dynamics of this species in response to crop management, using survey data gathered over 4 years from 364 fields across a network of 45 UK farms. We show that hierarchical density-structured models provide a substantial improvement over their non-hierarchical counterparts. Using these models, we demonstrate that several alternative crop-rotations are effective in reducing weed densities. Rotations with high wheat prevalence exhibit the most severe infestations, and diverse rotations generally have lower weed densities. However, a key outcome is that in many cases the effect of crop rotation is small compared to the high variability arising from spatio-temporal heterogeneity. This result highlights the need to monitor and model population dynamics across large spatial and temporal scales in order to account for variation in the drivers of plant dynamics. Our framework for data collection and modelling provides a means to achieve this.</p>
National-scale tree species/genera map for Poland from Sentinel-2 time series
<p>Map of 16 dominant tree species/genera in Poland based on classification of time series of Sentinel-2 imagery. This dataset is associated with the article by Grabska-Szwagrzyk et al. (2024)<em>: <a href="https://essd.copernicus.org/articles/16/2877/2024/">Map of forest tree species for Poland based on Sentinel-2 data.</a></em></p> <p>The map is provided as GeoTiff file. In addition, training and test data is provided in shapefile format. The map can be explored online in a <a href="https://ee-aweaksbarg.projects.earthengine.app/view/speciesmappl">webviewer</a>.</p> <p> </p>
CRLC: Cross-resolution national-scale land-cover mapping based on noisy label learning: a case study of China
<p><strong>CRLC are the 10-meter resolution land cover maps for China in 2020 achieved by a deep classification network.</strong></p> <p><strong>The maps include eight land cover classes:</strong></p> <p>1: Cropland</p> <p>2: Forest</p> <p>3: Grass/shrubland</p> <p>5: Wetland</p> <p>6: Water bodies</p> <p>8: Impervious</p> <p>9: Bareland</p> <p>10: Snow/ice</p> <p> </p> <p><strong>Reference:</strong></p> <pre>@article{liu2023cross, title={Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China}, author={Liu, Yinhe and Zhong, Yanfei and Ma, Ailong and Zhao, Ji and Zhang, Liangpei}, journal={International Journal of Applied Earth Observation and Geoinformation}, volume={118}, pages={103265}, year={2023}, publisher={Elsevier} }</pre>
Developing hierarchical density-structured models to study the national-scale dynamics of an arable weed
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Prioritizing road-kill mitigation areas: a spatially explicit national-scale model for an elusive carnivore
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Pathways to national-scale adoption of enhanced geothermal power through experience-driven cost reductions: Supplementary Data
<p>Dataset containing inputs, results, and code referenced in "Pathways to national-scale adoption of enhanced geothermal power through experience-driven cost reductions."</p> <ul> <li>"Central_Cases.zip", "NoEGS.zip", "Policy_Sensitivities.zip", "EGS_Sensitivities_Learning.zip", "EGS_Sensitivities_Other.zip", "OtherTech_Sensitivities.zip", and "IRA_Repeal_Cases.zip" contain the full set of capacity expansion model results referenced in the paper</li> <li>"GenX_EGS_HalfTimeSeries.zip" contains the source code for the modified version of the GenX electricity system capacity expansion model used in this work</li> <li>"GenX_Input_Files.zip" contains the full set of GenX inputs used in this work, including customized run files that apply inter-planning-period linkages, conditional policies, and endogenous learning-by-doing, which may be used alongside the model source code to replicate the results</li> <li>"PowerGenome_Inputs_and_Processing.zip" contains the settings files used to create GenX inputs in the PowerGenome tool, as well as post-processing scripts used to modify certain technologies and implement 2-hourly resolution</li> <li>"Costing_and_Supply_Curves.zip" contains the source code for the EGS cost model used in this work, as well as input temperature-at-depth data from Aljubran and Horne (2024). Temperature-at-depth data from Blackwell et al. (2011) is available for purchase from the SMU Geothermal Laboratory.</li> <li>"EGS_GenX_Inputs.zip" contains other EGS performance data and scripts used to build final GenX inputs</li> </ul> <p> </p>
Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)
<p>The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>.</p> <p>All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated.</p> <p>The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). </p> <p>Version v201:<br>Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015).</p> <p>Version v202:<br>Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the „Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps.</p> <p>The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately.</p> <p>Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. </p> <p> </p> <p><strong>References:<br></strong><br><em>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831.</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022).</em></p> <p><em>BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. </em><br><em>https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</em></p> <p><em>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</em></p> <p><em>Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland <br>https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024).</em></p> <p>___________________________________________________________________________<br>National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan; licensed under CC BY 4.0. </p> <p>Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes).</p> <p>The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).</p>
Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model
<p>Composite data with NWM 2.0 streamflow, basin PET, drainage area and stoage.</p> <p>SAR data used in this study are downloaded from</p> <p><a href="https://scihub.copernicus.eu/">https://scihub.copernicus.eu/</a></p> <p> </p>
Data from: A national-scale model of linear features improves predictions of farmland biodiversity
1. Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically-relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies. 2. We assessed whether a novel spatial dataset mapping linear and woody linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites respectively. 3. Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. 4. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems, and also can potentially be used to assess habitat connectivity. 10-Mar-2017
National-scale rainfall-triggered landslide susceptibility and exposure in Nepal
<p>This dataset is a 30 m resolution landslide susceptibility (hazard) model for Nepal. The dataset is derived from a fuzzy-logic model following the approach developed by Kritikos et al (2015), and is trained on a manually mapped landslide inventory (polygons of landslide locations and extents), originally produced by Durham University and NSET Nepal as part of the UKRI-DFID SHEAR program. The dataset shows the relative probability of a landslide occurring across the geographical extent of Nepal for rainfall triggered landslides only, on a scale of 1 (very highly anticipated) to 0 (very weakly anticipated). Further details and an online map viewer containing the data are available through the Bipad portal (https://bipadportal.gov.np/risk-info/#/hazard).</p> <p>The research relating to this landslide susceptibility model has been supported by the UKRI-DFID SHEAR program (201844-112), the EPSRC project ‘<em>Risk at the Margins</em>’ (EP/T024747/1), the EU ECHO HIP project ‘<em>Risk informed landslide management in Nepal’s hill areas</em>’ (ECHO/-XA/BUD/2020/91026), and by a grant from the Global Challenges Research Fund Multi-Hazard and Systemic Risk programme for the ‘Sajag-Nepal’ project (NE/T01038X/1).</p>
Data from: A national-scale model of linear features improves predictions of farmland biodiversity
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National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2020)
<p>This data set contains information on the agricultural land use in Germany for the year 2020.<br> The map was derived from dense time series of Sentinel-2 and Landsat 8 data, Sentinel-1 monthly composites and addtional environmental data. It is based on the methods described in <a href="https://www.sciencedirect.com/science/article/pii/S0034425721005514">Blickensdörfer et al. 2022</a> and can be seen as a continuation of the dataset provided under: <a href="http://zenodo.org/record/5153047#.YWFyXn1CREZ">https://zenodo.org/record/5153047#.YWFyXn1CREZ</a>.<br> The maps can be explored online in a <a href="https://ows.geo.hu-berlin.de/webviewer/landwirtschaft/">webviewer</a>.</p> <p>Due to specific user needs the class catalogue was slightly modified but a translation key (Table 1) and a translated map version (*_V1.tif) is provided. However, it has to be noted that some rather small classes in the previous maps were not differentiated anymore (e.g., onions, carrots, asparagus).Thus, the classes 34, 43, 92, 130, 140, 181 and 182 were excluded from the raster and legend files.</p> <p> </p> <p>Table 1: Updated class catalogue and translation key to the class catalogue used in Blickensdörfer et al. 2022.</p> <table> <tbody> <tr> <td> <p><strong>New class code (V2) </strong></p> </td> <td> <p><strong>Class name (V2)</strong></p> </td> <td> <p><strong>Class code (V1)</strong></p> </td> <td> <p><strong>Class name (V1)</strong></p> </td> </tr> <tr> <td> <p>1101</p> </td> <td> <p>Winter wheat</p> </td> <td> <p>31</p> </td> <td> <p>Winter wheat</p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p>34</p> </td> <td> <p>Other winter cereals</p> </td> </tr> <tr> <td> <p>1102</p> </td> <td> <p>Winter barley</p> </td> <td> <p>33</p> </td> <td> <p>Winter barley</p> </td> </tr> <tr> <td> <p>1103</p> </td> <td> <p>Winter rye</p> </td> <td> <p>32</p> </td> <td> <p>Winter rye</p> </td> </tr> <tr> <td> <p>1201</p> </td> <td> <p>Spring barley</p> </td> <td> <p>41</p> </td> <td> <p>Spring barley</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>43</p> </td> <td> <p>Other spring cereals</p> </td> </tr> <tr> <td> <p>1202</p> </td> <td> <p>Oat</p> </td> <td> <p>42</p> </td> <td> <p>Spring oat</p> </td> </tr> <tr> <td> <p>1300</p> </td> <td> <p>Maize</p> </td> <td> <p>91</p> </td> <td> <p>Maize (silage)</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>92</p> </td> <td> <p>Maize (grain)</p> </td> </tr> <tr> <td> <p>1401</p> </td> <td> <p>Potatoe</p> </td> <td> <p>100</p> </td> <td> <p>Potatoe</p> </td> </tr> <tr> <td> <p>1402</p> </td> <td> <p>Sugar beet</p> </td> <td> <p>80</p> </td> <td> <p>Sugar beet</p> </td> </tr> <tr> <td> <p>1501</p> </td> <td> <p>Rapeseed</p> </td> <td> <p>50</p> </td> <td> <p>Winter rapeseed</p> </td> </tr> <tr> <td> <p>1502</p> </td> <td> <p>Sunflower</p> </td> <td> <p>70</p> </td> <td> <p>Sunflower</p> </td> </tr> <tr> <td> <p>1611</p> </td> <td> <p>Peas</p> </td> <td> <p>60</p> </td> <td> <p>Legume</p> </td> </tr> <tr> <td> <p>1612</p> </td> <td> <p>Broad beans</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1613</p> </td> <td> <p>Lupine</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1614</p> </td> <td> <p>Soy</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>1603</p> </td> <td> <p>Vegetables</p> </td> <td> <p>120</p> </td> <td> <p>Strawberry</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>130</p> </td> <td> <p>Asparagus</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>140</p> </td> <td> <p>Onion</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>181</p> </td> <td> <p>Carrot</p> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>182</p> </td> <td> <p>Other leafy vegetables</p> </td> </tr> <tr> <td> <p>1602</p> </td> <td> <p>Cultivated grassland</p> </td> <td> <p>10</p> </td> <td> <p>Grassland</p> </td> </tr> <tr> <td> <p>200</p> </td> <td> <p>Permanent grassland</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3003</p> </td> <td> <p>Fallow land</p> </td> <td> </td> <td> </td> </tr> <tr> <td> <p>3001</p> </td> <td> <p>Small woody features</p> </td> <td> <p>555</p> </td> <td> <p>Small woody features</p> </td> </tr> <tr> <td> <p>3002</p> </td> <td> <p>Other areas</p> </td> <td> <p>999</p> </td> <td> <p>Other agricultural areas</p> </td> </tr> <tr> <td> <p>4001</p> </td> <td> <p>Grapevine</p> </td> <td> <p>110</p> </td> <td> <p>Grapevine</p> </td> </tr> <tr> <td> <p>4002</p> </td> <td> <p>Hops</p> </td> <td> <p>150</p> </td> <td> <p>Hops</p> </td> </tr> <tr> <td> <p>4003</p> </td> <td> <p>Orchard</p> </td> <td> <p>160</p> </td> <td> <p>Orchards</p> </td> </tr> </tbody> </table> <p> </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 12. April 2022), before environmental and SAR data were included in the ARD cube. </p> <p>The models were trained in FORCE and applied to all areas in Germany that were defined as agricultural land, small woody features, heathland or peatland in ATKIS DLM 2020 (Geobasisdaten: © GeoBasis-DE / BKG (2020)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine and hops areas that were not labelled as the respective permanent crop in ATKIS DLM (BKG (2020); labelled as other agricultural areas in the final map). <br> </p> <p>The maps are provided as GeoTiff files together with QGIS legend files for visualization. </p> <p> </p> <p>References:</p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2022 by Schwieder, Marcel; Erasmi, Stefan; Nendel, Claas; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data (2017, 2018 and 2019)
<p>Detailed maps of agricultural landscapes are a valuable data source for manifold applications, such as environmental modelling, biodiversity monitoring or the support of agricultural statistics. Satellites from the European Copernicus program, especially, Sentinel-1 and Sentinel-2, as well as the Landsat missions operated by NASA/USGS, acquire data with a spatial resolution (10 m to 30 m) that is sufficient to identify field structures in complex agricultural landscapes. Time series of combined Sentinel-2 and Landsat data facilitate to differentiate crop types with a high thematic detail based on differences in land surface phenology. However, large data gaps due to frequent cloud cover may hamper such classification approaches. </p> <p>We thus combined dense interpolated times series of Sentinel-2A/B and Landsat data with monthly composites of Sentinel-1 backscatter data to overcome periods with high cloud contamination. To further account for regional variations along the agroecological gradient within Germany, we additionally included a broad set of spatially explicit environmental data in a random forest classification model. </p> <p>All optical satellite data were downloaded, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019; <a href="https://force-eo.readthedocs.io/en/latest/">https://force-eo.readthedocs.io/en/latest/</a> last accessed: 19. August 2021), before environmental and SAR data were included in the ARD cube. </p> <p>For each year (2017, 2018 and 2019) we trained an individual random forest model with 24 agricultural classes. Each model was independently validated with area adjusted overall accuracies of 80% (2017), 79% (2018), and 78% (2019). Further details regarding the data and methods used as well as class wise accuracies can be found in Blickensdörfer et al. (2022). </p> <p>The final models were applied to areas in Germany that were defined as agricultural land in ATKIS DLM 2018 (Geobasisdaten: © GeoBasis-DE / BKG (2018)). Post-processing of the final maps included applying a sieve filter, the exclusion of classes other than grasslands and small woody features above 900 m (based on the Digital Elevation Model for Germany BKG (2015)) and the exclusion of grapevine/hops areas that were not labelled as the respective permanent crop in ATKIS DLM (labelled as other agricultural areas in the final map). </p> <p>The maps are provided as GeoTiff files together with a QGIS legend file for visualization. </p> <p>Class catalogue:</p> <p>10 Grassland<br> 31 Winter wheat<br> 32 Winter rye<br> 33 Winter barley<br> 34 Other winter cereal<br> 41 Spring barley<br> 42 Spring oat<br> 43 Other spring cereal<br> 50 Winter rapeseed<br> 60 Legume<br> 70 Sunflower<br> 80 Sugar beet<br> 91 Maize<br> 92 Maize (grain)<br> 100 Potato<br> 110 Grapevine<br> 120 Strawberry<br> 130 Asparagus<br> 140 Onion<br> 150 Hops<br> 160 Orchard<br> 181 Carrot<br> 182 Other vegetables<br> 555 Small woody features<br> 999 Other agricultural areas</p> <p> </p> <p>Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831</p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 19. August 2021). </p> <p>BKG, Bundesamt für Kartographie und Geodäsie (2018). Digitales Basis-Landschaftsmodell. <br> https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 19. August 2021).</p> <p>Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124.</p> <p> </p> <p><a href="https://zenodo.org/record/5153047#.YhYwgpYxmUn">National-scale crop type maps for Germany </a>© 2021 by Blickensdörfer, Lukas; Schwieder, Marcel; Pflugmacher, Dirk; Nendel, Claas; Erasmi, Stefan; Hostert, Patrick is licensed under <a href="http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1">CC BY 4.0. </a></p>
National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)
<p>National-Scale Spatial Flood Modeling with an Optimized Deep Learning Approach (case study: Sweden)</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.