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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>
Morphological and physiological characteristics of cassava genotypes on dry-land of ultisol soil in Indonesia
<p>This data showed the characterization of morphological and physiological characters of some cassava genotypes in Indonesia that were collected from some areas in Indonesia and now, planting in the IPB Germplasm Farm Collection at Bogor Regency, Indonesia. We have more than 50 genotypes collections. </p>
Copernicus Global Land Service: Global biome cluster layer for the 100m global land cover processing line
<p><strong>A map of 73 global biome clusters, geographic areas that were grouped to optimize the global 100m land cover processing.</strong></p> <p>In order to group Earth Observation data for faster processing or adaptation of algorithms to specific regions, the 100m global land cover (CGLS-LC100) algorithm uses a Global Biome Cluster layer. The term <em>biome cluster</em> hereby refers to a geographic area which has similar bio-geophysical parameters and, therefore, can be grouped for processing. In other words, the biome cluster layer can be seen as an ecological regionalisation which outlines areas of similar environmental conditions, ecological processes, and biotic communities (Coops et al., 2018). There are already several global regionalisation layers existing, e.g. Ecoregions 2017 global dataset (Dinerstein et al., 2017), Geiger-Koeppen global ecozones after Olofsson update (Olofsson et al., 2012), Global ecological zones for FAO forest reporting with update 2010 (FAO, 2012). But several tests in the CGLS-LC100 workflow have shown that the existing layers did not provide the required global and continental classification accuracy. These findings go along with Coops et al. (2018) who stated that "<em>Most regionalisations are made based on subjective criteria, and cannot be readily revised, leading to outstanding questions with respect to how to optimally develop and define them."</em></p> <p>Therefore, we decided to develop a customized ecological regionalisation layer which performs best with the given PROBA-V remote sensing data and the specifications of the CGLS-LC100 product. It groups spectral similar areas and helps to optimize the later classification/regression to regional patterns. Input into the layer creation were well-known existing datasets which were combined, re-grouped and advanced based on prior CGLS-LC100 classification results and local mapping knowledge of the workflow developer. To ensure that this layer is clearly separable from other existing regionalisations and not mistakenly interpreted as an eco-region layer, we decide to call it <em>biome clusters</em> <em>layer</em>.</p> <p>The following steps outline the global biome clusters layer generation:</p> <ul> <li>Spatial union of Ecoregions 2017 dataset (Dinerstein et al., 2017), Geiger-Koeppen dataset (Olofsson et al., 2012) and Global FAO eco-regions datasets (FAO, 2012);</li> <li>Regrouping and dissolving by using experience from first global CGLS-LC100 mapping results and subjective mapping experience of the developer;</li> <li>Refinement of the biome clusters in the High North latitudes via incorporation of a Global tree-line layer (Alaska Geobotany Center, 2003);</li> <li>Manual improvement of borders between biome clusters to reduce classification artefacts by using a DEM and mapping experience from previous projects and continental test runs;</li> <li>Usage of a global land/sea mask, the Sentinel-2 tiling grid and PROBA-V imaging extent to extend the borders of the biome clusters into the sea to make sure that also small islands on the coastline are correctly processed.</li> </ul> <p>When developing a regionalisation, the definition of the clusters and the boundaries that delineate them in time and space is the key challenge. Overall, the map distinguishes <strong>73 global biome clusters</strong>.</p>
2017–2018 Land Cover Map of Pyrénées-Atlantiques
<p>This archive contains:</p> <p><br> ├── classification_dpt64_16_classes.tif : the 16 classes land cover map</p> <p>├── classification_dpt64_16_classes_confusion_matrix.png : the confusion matrix. Have a look at it, it is performed on a different dataset than the one used for training the classifier.</p> <p>├── classification_dpt64_21_classes.tif : the 21 classes land cover map including post-treatments (<a href="https://outlook.office.com/owa/redir.aspx?REF=IplRG6_B9OG8Rjvmh5Epv5dpru9Rpr5pwX-IxbR_kY-VTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9TY2h3YWFiL3Byb2pldF9wcmVkYXRldXJzNjQvLS9ibG9iL21haW4vc2NyaXB0cy9DbGFzc2lmaWNhdGlvblBvc3RQcm9jZXNzLnB5">https://framagit.org/Schwaab/projet_predateurs64/-/blob/main/scripts/ClassificationPostProcess.py</a>)</p> <p>├── colorFile.txt : color file for symbology</p> <p>├── configfile_iota2.cfg : iota2 configuration file (in case you are already using iota2. If not, what are you waiting for ?)</p> <p>├── document_methodologique.pdf : technical report (french) for the classification </p> <p>├── nomenclature.txt : nomenclature file</p> <p>├── reference_data_2018.shp : the training and validation data set in its 2018 version (for crops)</p> <p>├── reference_data_2019.shp : the training and validation data set in its 2019 version (for crops)</p> <p>├── reference_photo_interpretation.shp : the part of the training and validation data set that has been photo interpreted with a field giving the potential species or combinations of associated vegetation</p> <p>├── reference_tree_nomenclature.png : a visual about the reference data</p> <p>├── stratification_3_zones.shp : the stratification layer that has helped improve classification results. It is based on landscape entities (<a href="https://outlook.office.com/owa/redir.aspx?REF=-inpMPbdxkotoNFtrpo0ptiZmF3YJRSM91bnORcpCHKVTC7cUdfZCAFodHRwczovL2RhdGEubGU2NC5mci9leHBsb3JlL2RhdGFzZXQvZW50aXRlLXBheXNhZ2VyZS8.">https://data.le64.fr/explore/dataset/entite-paysagere/</a>) </p> <p>├── style_16_classes.qml : the Qgis style layer 16 classes</p> <p>└── style_21_classes.qml : the Qgis style layer 21 classes<br> </p> <p>Description:</p> <p><br> The land cover map of the French department Pyrénées-Atlantiques (64) is based on Sentinel-2 (L2A level) satellite images performed with Iota² chain (<a href="https://outlook.office.com/owa/redir.aspx?REF=xF93blZK9Vxs-N7pnBh9q489HLjMtSEZRU6VkhywO-qVTC7cUdfZCAFodHRwczovL2ZyYW1hZ2l0Lm9yZy9pb3RhMi1wcm9qZWN0L2lvdGEyLw..">https://framagit.org/iota2-project/iota2/</a>). The algorithm used is Random Forest. The time series used ranges from 2017 to 2018.</p> <p>During the development phase of this classification, the collection of additional training data on the photo-interpreted classes 'landes basses' (low heath shrublands), 'landes hautes' (high heath shrublands) and 'landes hautes avec arbres' (high heath shrublands with young-growth forest) has led to a remarkable increase of the number of pixels of these classes and with it the visual quality of the map. However, this increase has been linked with only minor to almost no significant improvement of the F-scores on these classes. Some are still massively confused with other land covers like grasslands and broadleaf mature forests. Especially the mixed class 'landes hautes avec arbres' (high heath shrublands with young-growth forest).<br> <br> We take it as a limit of the reference data that is built from divers data sources and would always beneficiate from more training samples of shrubby classes and a better precision of the class 'forêt de feuillus' (broadleaf mature forests). But this could also show the limit of pixel-oriented classifications for mixed/textured classes (classes with high intra-class heterogeneity). Experimentations using a contextual method – the Auto-context method now being included in Iota2 thanks to Dawa Derksen and Iota2 developers (<a href="https://outlook.office.com/owa/redir.aspx?REF=-QXKxpHR73RRUGsNGVs-FtQHPORNqP2y2wLSVVK_L-uVTC7cUdfZCAFodHRwOi8vbGFubmlzdGVyLnVwcy10bHNlLmZyL29zby9kb25uZWVzd3d3X1RoZWlhT1NPL2lvdGEyX2RvY3VtZW50YXRpb24vZGV2ZWxvcC9hdXRvQ29udGV4dC5odG1s">http://lannister.ups-tlse.fr/oso/donneeswww_TheiaOSO/iota2_documentation/develop/autoContext.html</a>) – has unfortunately not been conclusive on that matter yet.</p>
Chesapeake Land Cover dataset - Learning on the prior extension
<p>This dataset extends the "Chesapeake Land Cover" dataset at <a href="https://lila.science/datasets/chesapeakelandcover">https://lila.science/datasets/chesapeakelandcover</a> with an additional <em>layer </em>of data containing a prior of observing a 1m Chesapeake Conservancy land cover class label given an NLCD label. Specifically, for each <em>tile </em>in the original "Chesapeake Land Cover" dataset, this dataset contains another tile (named with a "_prior_from_cooccurrences_101_31_no_osm_no_buildings.tif" suffix) containing the prior probabilities of observing a four class version of the land cover classes at a 1m resolution. The prior probability is given by the normalized co-occurrence matrix between NLCD classes and the Chesapeake 1m land cover labels in each state with additional spatial smoothing (using a gaussian filter with a standard deviation of 31 pixels and a cutoff of 101 pixels) to reduce the block artifacts caused by the relatively low-resolution of the NLCD labels (30m) compared to the LC labels (1m). <strong>Note:</strong> the prior is a 4-class mapping of the 6-class labels. In the 4-class version of the dataset the "barren land", "impervious (other)", and "impervious (road)" classes are combined.</p> <p>This dataset also includes the per-state co-occurrence matrices. Each of these is a matrix, <span class="math-tex">\(C\)</span>, with size 7 x 17, where an entry <span class="math-tex">\(C_{ij}\)</span> gives the normalized count of land cover class <span class="math-tex">\(i\)</span> for given NLCD label <span class="math-tex">\(j\)</span>. The class indices <span class="math-tex">\(i\)</span> and <span class="math-tex">\(j\)</span> correspond to the indices used in the Chesapeake Conservancy land cover label and NLCD layers.</p> <p>The dataset is packaged in a way such that it can simply be unzipped over an existing copy of the "Chesapeake Land Cover" (i.e. follows the same directory structure). E.g., given a directory that contains the original dataset "cvpr_chesapeake_landcover/", and the file "cvpr_chesapeake_landcover_prior_extension.zip", run `unzip cvpr_chesapeake_landcover_prior_extension.zip` to create the merged dataset.</p> <p> </p> <p>Version 1.1 fixes an issue with the geotiff metadata that caused GDAL to interpret the 4th band as a nodata mask in some settings.</p>
Labeled points for four land cover classes referring to the year 2019
<p>This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees).</p> <p>It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019.</p> <p>This dataset was used for the random forest classification of satellite imagery in the following manuscript: </p> <p>"<em>Satellite image processing for the coarse-scale investigation of sandy coastal areas</em>".</p> <p>If you use any part of this dataset, please cite as follows:</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>
Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies
<p><strong>Global inventory of potentially cultivable land and potentially available cropland under different scenarios and policies</strong></p> <p>To identify and investigate potential land-use conflicts and emerging trade-offs between different Sustainable Development Goals, such as food security, climate protection and biodiversity conservation, it is important to identify where land-use change and particularly the expansion of cropland could potentially take place in the future. Therefore, we provide a consistent global dataset of land potentially cultivable and potentially available for agricultural use for past and future time periods from 1980 until 2100. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and first- and second-generation bioenergy crops, and high resolution land cover data, the potentially cultivable land is defined by its agricultural suitability and the (technical) feasibility of agriculture. The potentially available cropland additionally considers potential nature protection policies restricting agriculture in forests, wetlands and strictly protected areas, thereby reflecting key aims of the Sustainable Development goals and recent efforts to stop deforestation, protect the climate and preserve biodiversity.</p> <p>The spatially explicit global datasets of potentially cultivable land (pcl) and potentially available cropland (pac) are available for four different time periods (1980-2009, 2010-20,39, 2040-2069, 2070-2099) under RCP2.6 and RCP8.5. The impact of irrigation on the agricultural suitability is considered by referring to current irrigations patters. However, to enable different assumptions on the irrigation of land potentially cultivable or available for cropland use, all datasets are also available for rainfed and irrigated conditions separately. Moreover, we provide a subset-version of all dataset which excludes land that is solely suitable for second-generation bioenergy crops. All datasets are available at 30 arc-seconds and 30 arc-minutes spatial resolution and aggregated at country level to enable the application in models that use aggregated data.</p> <p>By serving as an input for land-use models, the data could improve the comparability of the models and their output, and increase the consistency within interdisciplinary research and integrated model coupling approaches that investigate land-use change.</p> <p> </p> <p><strong>Further information:</strong></p> <p>A detailed description on the methods and underlying data is available in:</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.<em> </em><a href="https://doi.org/10.1038/s41597-022-01632-8">https://doi.org/10.1038/s41597-022-01632-8</a></p> <p><strong>Contact:</strong></p> <p>Please contact: Julia M. Schneider (Schneider.ju@lmu.de)<br>Department of Geography, Ludwig-Maximilians-Universität München (LMU), Munich, Germany.</p>
FIGURE 5 in The world's tiniest land snails from Laos and Vietnam (Gastropoda, Pulmonata, Hypselostomatidae)
FIGURE 5 Plot of shell height against shell width (diameter) for Acmella nana, Angustopila coprologos n. sp., Angustopila dominikae, Angustopila pallgergelyi, Angustopila psammion n. sp., Arinia micro. Empty squares of Acmella nana represent shells of the population with smallest shells. The dashed line represents equal shell height and width. Ellipses drawn manually.
FIGURE 2 in The world's tiniest land snails from Laos and Vietnam (Gastropoda, Pulmonata, Hypselostomatidae)
FIGURE 2 Granules on the shell surface of A. coprologos n. sp. shells. A–B shows a shell with brown 'mud', whereas C–D shows a shell with white (calcareous) granules.
FIGURE 4 in The world's tiniest land snails from Laos and Vietnam (Gastropoda, Pulmonata, Hypselostomatidae)
FIGURE 4 Size comparisons of the former record holders, A. pallgergelyi, Acmella nana, Notharinia micro, the smallest marine snail, Ammonicera minortalis, and the two new species described herein. The image of Ammonicera minortalis Rolán, 1992 is from Oliver et al. (2012), whereas the other images (Acmella nana and Arinia micro) are from their respective original descriptions. The figure of Acmella nana is adjusted to scale with the measurement of the smallest specimen, whereas for Angustopila coprologos n. sp. and Angustopila psammion n. sp., the holotypes (not the tiniest shells) are shown.
Land use fractions and human impact index in 101 Canadian lake watersheds
<p>Land use fractions (urban, mines, agriculture, pasture, forestry, managed grassland, water and natural landscape) and associated human impact index in 101 lake watersheds sampled as part the NSERC Canadian Lake Pulse Network project. Land use and human impact index were calculated as described in Huot et al. (2019).</p> <p>Lakes IDs with respective locations (longitude and latitude coordinates) and Continental Basin allocations can be found here: <a href="https://doi.org/10.5281/zenodo.4701262">https://doi.org/10.5281/zenodo.4701262</a></p> <p>Reference</p> <p>Huot, Y., C. A. Brown, G. Potvin, and others. 2019. The NSERC Canadian Lake Pulse Network: A national assessment of lake health providing science for water management in a changing climate. Sci. Total Environ. <strong>695</strong>: 133668. doi:10.1016/j.scitotenv.2019.133668</p>
Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019
<p>A CCDC algorithm was used to produce 13 sets of geographical maps. Each set represents one capital city of Saudi Arabia: Riyadh (which also serves as the country’s capital), Buridah, Ha’il, Dammam, Makkah, Madinah, Arar, Skakah Tabuk, Albaha, Abha, Jazan, and Najran. Each of the 13 datasets contains 35 annual maps from 1985 to 2019. The file format of these datasets is the .hdr file. The dataset is free to download. </p> <p>In the case of using the urban land cover maps, please cites the website and the paper:</p> <ol> <li>Aljaddani AH, Song X-P, Zhu Z. Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. <em>Remote Sensing</em>. 2022; 14(10):2382. https://doi.org/10.3390/rs14102382</li> <li>Aljaddani, Amal H, Song, Xiao-Peng, & Zhu, Zhe. (2022). Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019 (Version: 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6210073</li> </ol> <p> </p> <p> </p>
Towards identifying industrial crop types and associated agronomies to improve biomass production from marginal lands in Europe
<p>Background: Growing industrial crops on marginal lands has been proposed as a strategy to minimize competition for arable land and food production. In the present study, eight experimental sites in three different climatic zones in Europe (Mediterranean, Atlantic and Continental), seven advanced industrial crop species [giant reed (two clones), miscanthus (<em>M</em>. × <em>giganteus</em> and two new seed-based hybrids), saccharum (one clones), switchgrass (one variety), tall wheatgrass (one variety), industrial hemp (three varieties) and willow (eleven clones)], and six marginality factors alone or in combination (dryness, unfavorable texture, stoniness, shallow soil, topsoil acidity, heavy metal and metalloid contamination) were investigated. At each site, biophysical constraints and low-input management practices were combined with prevailing climatic conditions.</p> <p>Results: The relative yield of a site-specific low-input system compared with the site-specific control was from small to large (i.e., from -99% in industrial hemp in the Mediterranean to +210% in willow in the Continental zone), due to the genotype-by-management interaction along with climatic variation between growing seasons. Genotype selection and improved knowledge on crop response to changing environmental, site-specific biophysical constraint and input application has been detected as key to profitably grow industrial crops on marginal areas.</p> <p>Conclusions: This study may act to provide hints on how to scale-up investigated cropping systems, through low-input practices, under similar environmental and soil conditions tested at each site. However, further attention to detail on the agronomy of early plant development and management in larger multi-year and multi-location field studies with commercially scalable agronomies are needed in order to validate yield performances, and thereby to inform on the best industrial crop options.</p>
EnviroAtlas land cover dataset
<p>This dataset contains imagery, land cover, and object layers for four of the cities covered by the EPA’s EnviroAtlas dataset: Pittsburgh, PA, Phoenix, AZ, Durham, NC, and Austin, TX. The dataset consists of 10 test tiles and 5 validation tiles in each city, with an additional 10 training tiles and 8 (total) validation tiles in Pittsburgh, subsetting the total area that the EnviroAtlas dataset covers in each city. The EnviroAtlas label definitions are explained in <a href="https://pubmed.ncbi.nlm.nih.gov/32844040/">Pilant et al. 2020</a>. In the processed prior files included in this dataset, channels correspond to:</p> <ul> <li>0: water</li> <li>1: impervious surface</li> <li>2: soil and barren</li> <li>3: trees and forest (and shrub in AZ)</li> <li>4: grass and herbaceous</li> </ul> <p>Specifically, this dataset is in the same format as the "<a href="https://lila.science/datasets/chesapeakelandcover">Chesapeake Land Cover</a>" dataset and contains the following layers:</p> <ul> <li>NAIP 1m aerial imagery (year = 2010 for Pittsburgh, P and Phoenix, AZ, and year = 2012 for Durham NC, and Austin TX <ul> <li><a href="https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index">https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index</a></li> <li><a href="https://planetarycomputer.microsoft.com/dataset/naip">https://planetarycomputer.microsoft.com/dataset/naip</a></li> </ul> </li> <li>NLCD 2016 <ul> <li><a href="https://www.mrlc.gov/data">https://www.mrlc.gov/data</a></li> </ul> </li> <li>OpenStreetMap roads <ul> <li><a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></li> </ul> </li> <li>OpenStreetMap waterways, waterbodies, water= max(waterways, waterbodies) <ul> <li><a href="https://www.openstreetmap.org/">https://www.openstreetmap.org/</a></li> </ul> </li> <li>Microsoft building footprints <ul> <li><a href="https://github.com/Microsoft/USBuildingFootprints">https://github.com/Microsoft/USBuildingFootprints</a></li> </ul> </li> <li>EnviroAtlas 1m land cover labels: <ul> <li><a href="https://www.epa.gov/enviroatlas/about-data">https://www.epa.gov/enviroatlas/about-data </a></li> </ul> </li> <li>Prior probabilities of the 1m land cover labels (with OSM and building data fused into prior)</li> <li>Prior probabilities of the 1m land cover labels (without OSM or building data)</li> </ul> <p>For more information on how the OpenStreetMap layers are downloaded and processed, as well as how the prior probabilities are calculated, see the accompanying GitHub repository: </p> <p> </p> <p>Version 1.1 fixes an issue with the spatial index file.</p>
Photographs of parasites encapsulated in Cepaea nemoralis shells (appendix to the paper "Morph-dependent effect of nematode infection on host movement in the land snail *Cepaea nemoralis* (Mollusca, Gastropoda)")
<p>A set of four photographs taken of shell fragments of Cepaea nemoralis (grove snail), taken in the course of the project leading to the manuscript titled: "Morph-dependent effect of nematode infection on host movement in the land snail *Cepaea nemoralis* (Mollusca, Gastropoda)". Each photograph shows a parasite encapsulated/trapped in the shell by the snail:</p> <p>AcaRX3_3.tif: a mite (presumably Riccardoella sp.)<br> NemaBX7_1.tif, NemaBX7_3.tif, NemaRK5_2.tif: unidentified nematodes (note in the latter image, the clearly visible brown-band on yellow background pattern of the shell).</p> <p>A scale bar (0.25 mm) is overlaid on each image</p> <p> </p>
MPAS-Albany Land Ice model simulations of Humboldt Glacier, North Greenland, from 2007–2100
<p>This dataset contains model input and output in netCDF format, model code, and analysis scripts for simulations of Humboldt Glacier, North Greenland, through the 21st century (Hillebrand et al., 2022) using the MPAS-Albany Land Ice model (Hoffman et al., 2018). We calibrate parameters controlling basal traction, iceberg calving, and submarine melt against observations from 2007–2017. We then explore the glacier’s sensitivity to climate forcing, iceberg calving, and basal conditions in an ensemble of 24 simulations from 2007–2100. We further explore its sensitivity to uncertainties in ice-shelf melt, bed topography, and calving rate limits in targeted sensitivity experiments. Input files include surface mass balance, ocean thermal forcing, and subglacial runoff forcings provided by ISMIP6 (Nowicki et al., 2020; Slater et al., 2020). Output includes basal traction optimization solutions for the year 2007; annual 2D ice speed, basal shear and driving stresses, and geometry; annual 3D temperature; and grounded, floating, and global mass budgets at every timestep.</p> <p>References:</p> <p>Hillebrand, T. R., Hoffman, M. J., Perego, M., Price, S. F., and Howat, I. M. (2022): The contribution of Humboldt Glacier, northern Greenland, to sea-level rise through 2100 constrained by recent observations of speedup and retreat, The Cryosphere, 16, 4679–4700, <a href="https://doi.org/10.5194/tc-16-4679-2022">https://doi.org/10.5194/tc-16-4679-2022</a>.</p> <p>Hoffman, M. J., Perego, M., Price, S. F., Lipscomb, W. H., Zhang, T., Jacobsen, D., et al. (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. <em>Geoscientific Model Development</em>, <em>11</em>(9), 3747–3780.<a href="https://doi.org/10.5194/gmd-11-3747-2018"> https://doi.org/10.5194/gmd-11-3747-2018</a></p> <p>Nowicki, S., Goelzer, H., Seroussi, H., Payne, A. J., Lipscomb, W. H., Abe-Ouchi, A., et al. (2020). Experimental protocol for sea level projections from ISMIP6 stand-alone ice sheet models. <em>The Cryosphere</em>, <em>14</em>(7), 2331–2368.<a href="https://doi.org/10.5194/tc-14-2331-2020"> https://doi.org/10.5194/tc-14-2331-2020</a></p> <p>Slater, D. A., Felikson, D., Straneo, F., Goelzer, H., Little, C. M., Morlighem, M., et al. (2020). Twenty-first century ocean forcing of the Greenland ice sheet for modelling of sea level contribution. <em>The Cryosphere</em>, <em>14</em>(3), 985–1008.<a href="https://doi.org/10.5194/tc-14-985-2020"> https://doi.org/10.5194/tc-14-985-2020</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.