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5,023 results for “landing”
LANDSAT Land Surface Temperature
<p>LANDSAT Land Surface Temperature for 2016 (Jan, Jul, Aug, Dec), calculated from USGS LANDSAT C02-O2 band B10.</p> <p>Lon Lat files are available</p>
Land use/land cover in Iraq and Syria, 2000-2016
<p>The land-use/land-cover (LULC) dataset covers the area between 28.35-37.84°N and 35.29-49.14°E (Iraq and Syria), and shows annual cropland extent (single- and double cropping) as well as bare soil and other (non-cropland) vegetaton in the area, for the period 2000-2016.</p> <p>The data are structured in the following folders:</p> <p><strong>Classification</strong> - yearly data on LULC. The values represent the class IDs:</p> <p>0 - Bare Soil (Includes areas with little or no vegetation, including bare soil, fallow croplands, and artificial areas (e.g. urban or built up)</p> <p>1 - Cropland (single) (Active croplands, harvested once per year (April/May), usually low intensity agriculture such as cereals.)</p> <p>2 - Cropland (double) (Active cropland with multiple harvests, usually in April/May and September/October.)</p> <p>3 - Other vegetation (All other natural vegetation cover, as well as orchards. The phenology does not show any sharp decline in greenness during harvest periods.)</p> <p><strong>Cropland_data</strong></p> <p>Showing extent of cropland (class 1 or 2) each year. 1 = cropland, 0 = not cropland.</p> <p><strong>Fallow_data_Syria</strong></p> <p>Fallow is defined based on the mode cropland extent for the period 2000-2016, and then compared with the current year. For example, if a pixel's mode value (aka most frequent class) is 1 or 2 and for a given year the value is 1 or 2 it will be classified as not fallow (0). If it's mode value is 1 or 2 and the value of a given year is 0 or 3, it is classified as fallow.</p> <p>Values:</p> <p>1 - Fallow</p> <p>0 - Not fallow, actively cropped</p> <p><strong>Summary_maps</strong></p> <p>This folder includes two maps, one with the cropland frequency (i.e. number of years actively cropped) and one with the Mode LULC value for the 2000-2016 period (class ID as in the Classification data).</p> <p><strong>Update 2024-05-21: </strong></p> <p>Added data and code used for Eklund <em>et al</em> 2024 (<em>Environ. Res. Lett.</em> <strong>19</strong> 014077) in Eklund_et_al_2024Data_Code.7zip.</p>
Data from: Local adaptation of Pinus leiophylla under climate and land use change models in the Avocado Belt of Michoacán
<p>Climate change and land use change are two main drivers of global biodiversity decline, decreasing the amount of genetic diversity that populations harbor and altering the patterns of local adaptation. Methods in landscape genomics allow measuring the effect of these anthropogenic disturbances on the adaptation of populations. However, both factors have rarely been considered simultaneously. We modeled the spatial turnover in allele frequencies of 19 localities of <em>Pinus leiophylla</em> across the Avocado Belt in Michoacán state, Mexico which could change under climate change and land use change scenarios, in addition to evaluating assisted gene flow strategies and connectivity metrics across the landscape to identify priority conservation areas. We found that localities at the center-east regions would be more vulnerable to climate change, while localities in the west area will be more threatened by actions of land use change. However, assisted gene flow actions could reduce their risk of extinction for both scenarios. Connectivity patterns will also be modified by future habitat loss, with the central and eastern parts having the highest connectivity values. These results show that the areas with the highest priority for conservation are in the eastern zones, which include the Monarch Butterfly Biosphere Reserve. This work is useful as a framework that incorporates distinct layers of information to provide a robust representation of the response of populations to future anthropogenic disturbances.</p>
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/ab9a564e-8c55-4ba2-828a-9577e9948803. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904
QA4SM validation: C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904. URL: https://qa4sm.eu/ui/validation-result/9241fbf2-995f-4ece-be64-2302efaae280. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/b61ec3bb-0eb9-4803-bd70-2dd052c5bcfb. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ceed8392-014f-48e6-a7f7-eb00d0b4ddd9. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/24418dc9-98ae-4b96-a272-87c4c6583df3. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/ed647646-8513-4bca-8d0a-1db534c145fd. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/62c2f4f1-572a-4932-b4c9-cf0d29da156b. Produced on QA4SM (https://qa4sm.eu)
Validation of C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global
QA4SM validation: C3S SM combined v201912 vs C3S SM combined v202012 vs C3S SM combined v202212 vs C3S SM combined v202312 vs ERA5-Land v20190904 vs ISMN 20240314 global. URL: https://qa4sm.eu/ui/validation-result/d6936b1a-e01c-4908-8f71-ea4be275c4fd. Produced on QA4SM (https://qa4sm.eu)
Fig. 3 in On the distribution and ecology of a rare land snail, Eostrobilops coreana (Pilsbry, 1927) (Gastropoda: Pulmonata: Strobilopsidae)
Fig. 3. Relief of Peschany Peninsula. Photo by L.A. Prozorova, November 1, 2020.
A catalogue of impact craters with diameters larger than 200 m in the Chang'e-6 landing area
<p>Chang'e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. This dataset is craters with diamters larger than 200m in the 134 km × 246 km landing area. The craters are extracted by an automated method and checked manually. </p>
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
OpenEarthMap Land Cover Mapping Few-Shot Learning Challenge
<pre><strong>***The challenge is over, please use this verion for post-challenge research. This version contains all the data and files, <br>except the labels of the query-set of the testset that are withheld and researchers can submit the predictions at the <br>challenge leaderboard for evaluation***</strong></pre> <p><strong>Overview</strong></p> <p>This challenge is co-organized with the <a href="https://sites.google.com/view/l3divu2024/overview" target="_blank" rel="noopener">L3D-IVU 2024 CVPR</a> workshop. The challenge is an extension of the <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap </a>benchmark dataset for a generalized few-shot semantic segmentation (GFSS) task. The challenge aims to evaluate and benchmark learning methods for few-shot semantic segmentation on the OpenEarthMap dataset to promote research on geoinformatics for social good. The motivation is to enable researchers to develop few-shot learning algorithms for high-resolution RS image semantic segmentation.</p> <p><strong>Page</strong></p> <p><a href="https://cliffbb.github.io/OEM-Fewshot-Challenge/" target="_blank" rel="noopener">https://cliffbb.github.io/OEM-Fewshot-Challenge/</a></p> <p><strong>Baseline</strong></p> <p>The baseline model for the challenge is available <a href="https://github.com/cliffbb/OEM-Fewshot-Challenge" target="_blank" rel="noopener">here</a>.</p> <p><strong>Leaderboard</strong></p> <p>The <a href="https://codalab.lisn.upsaclay.fr/competitions/19210" target="_blank" rel="noopener">challenge leaderboard </a>is opened for post-challenge research to enable researchers to evaluate the predictions on the query-set of the testset.</p> <p><strong>Description</strong></p> <p>The dataset has been designed for remote sensing few-shot learning, particularly, for GFSS tasks in land cover mapping. The dataset consists of only 408 samples from the original <a href="https://open-earth-map.org/" target="_blank" rel="noopener">OpenEarthMap</a> dataset for RS image semantic segmentation. It extends the original 8 semantic classes of the OpenEarthmap benchmark to 15 classes, which is split into 7:4:4 for <em>train_base_class</em>, <em>val_novel_class</em>, and <em>test_novel_class</em> disjointed sets, respectively (i.e., <em>train_base_class</em> ∩ <em>val_novel_class</em> ∩ <em>test_novel_class</em> = ∅). The 408 samples are also split into 258 as `trainset`, 50 as `valset`, and 100 as `testset`. The `trainset` is for pre-training a backbone network. It contains only the images and labels of the <em>train_base_class</em> split. Both the `valset` and the `testset` consist of a <em>support set</em> and a <em>query set</em> for a <strong>5-shot</strong> with <strong>4 novel classes</strong> and <strong>7 base classes</strong> GFSS task. The `valset` and the `testset` contain the images and labels of the <em>val_novel_class</em> and the <em>test_novel_class</em> splits, respectively.</p> <p>The challenge is in two phases: development phase and evaluation phase. The `valset` is for the development phase and the `testsets` is for the evaluation phase. Both `valset` and `testset` have 20 image-label pair examples, 5-set examples for each of the 4 novel classes in the <em>support set</em>. The `valset` and the `testset` contain an additional 30 images and 80 images, respectively, in the <em>query set</em>, which are to be predicted using the 20 labelled images in their <em>support set</em>. The labels for each image in the <em>support sets</em> do not contain any of the <em>train_base_class</em> split. Also, in each 5-set examples, the labels contain only one novel class (i.e., one novel class per 5-set examples). However, in the `valset`, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>val_novel_class</em>; and in the `testset, the labels for the images in the <em>query set</em> contain both <em>train_base_class</em> and <em>test_novel_class</em> `. Note that both the <em>support set</em> and <em>query set</em> in the `valset` are different from the ones in the `testset`.</p> <pre><br>File Structure and Content (All files are in `.tif` format): ----------------------------------------------------------- 1. **trainset.zip**: - Contains `images` and `labels` folders - `images` folder: 258 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 258 segmentation masks of the images in the `images` folder. 2. **valset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 50 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 30 ``query set`` images in the `images` folder are withheld.<br>3. **testset.zip**:<br> - Contains `images` and `labels` folders<br> - `images` folder: 100 images of size 1024x1024 with a GSD (Ground Sampling Distance) of 0.6-1m.<br> - `labels` folder: 20 labels of the ``support set`` images in the `images` folder. The labels for<br> the 80 ``query set`` images in the `images` folder are withheld.<br> 4. **train.txt**:<br> - Contains a list of file names in the `trainset.zip`.<br> <br>3. **val.json** and **test.json**:<br> - Contains a list of file names the in the `valset.zip` and `testset.zip`, respectively. Below is<br> the structure of the `val.json` and `test.json` files.<br> - fnames = {<br> {"support_set": {8: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 9: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 10: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"],<br> 11: ["filename_1.tif", "filename_2.tif", ...., "filename_5.tif"]},<br> {"query_set": ["filename_1.tif", "filename_2.tif", "filename_3.tif", ...<br> ...., <br> "filename_n.tif"]} <br> }<br> Land Cover Mapping Classes Strucure: ------------------------------------<br>1. **The `trainset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> }<br><br>2. **The `valset` and `testset`:<br> classId2className = {<br> # ***Base classes*** 1: 'tree', 2: 'rangeland', 3: 'bareland', 4: 'agric land type 1', 5: 'road type 1', 6: 'sea, lake, & pond', 7: 'building type 1'<br> # ***Novel classes***<br> 8: '',<br> 9: '',<br> 10: '',<br> 11: ''<br> }<br><br> - The class names for the ***Novel classes*** depends on the data set.<br><br> For the `valset`, the class names can be updated as:<br> {<br> 8: 'road type 2',<br> 9: 'river',<br> 10: 'boat & ship',<br> 11: 'agric land type 2'<br> }<br><br> For the `testset`, the class names can be updated as:<br> {<br> 8: 'vehicle & cargo-trailer',<br> 9: 'parking space',<br> 10: 'sports field',<br> 11: 'building type 2'<br> }<br><br><strong>License</strong></pre> <p>See <a href="../records/7223446" target="_blank" rel="noopener">OpenEarthMap</a></p>
Mitigating urban heat island through neighboring rural land cover: Dataset
<p>A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - <strong><em>Average_LST_GEE.sh</em></strong>; Codes for calculating urban development intensity - <em><strong>UrbanDevelopmentIntensity.py</strong></em>; Regression, Machine Learning, Interpretable Machine Learning Code - <em><strong>All</strong><strong><em>R</em>egression.py, SHAP.py, ALE.py</strong></em>; a zip file containing the data used in the calculations - <em><strong>OperationalData</strong></em>.<em><strong>rar.</strong></em></p>
Corine Land Cover 2018 V2020_20u1 vector
<p>Full coverage, downlodable copy of the European Union's Copernicus Land Monitoring Service information (CLMS) Corine Land Cover dataset in vector format (geopackage).</p> <ul> <li>Doi: https://doi.org/10.2909/71c95a07-e296-44fc-b22b-415f42acfdf0</li> <li>Release / Major version: V2020_20u1</li> <li>Projection: EPSG:3035</li> <li>Spatial coverage: Europe</li> <li>Spatial resolution: 25 ha/100 m</li> <li>Spatial representation: Vector</li> <li>Temporal extent: 2017-2018</li> <li>Position accuracy: 100 m or better</li> <li>Thematic accuracy: ≥ 85%</li> <li>Format: Vector / Geopackage (gpkg)</li> <li>Size: 8.9 GB</li> </ul> <p><br><strong>Licence:</strong></p> <p>Free, full and open access to the products and services of the Copernicus Land Monitoring Service is made on the conditions that:</p> <ul> <li>When distributing or communicating Copernicus Land Monitoring Service products and services (data, software scripts, web services, user and methodological documentation and similar) to the public, users shall inform the public of the source of these products and services and shall acknowledge that the Copernicus Land Monitoring Service products and services were produced “with funding by the European Union”.</li> <li>Where the Copernicus Land Monitoring Service products and services have been adapted or modified by the user, the user shall clearly state this.</li> <li>Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the European Union. </li> </ul> <p>Important: the user has all intellectual property rights to the products he/she has created based on the Copernicus Land Monitoring Service products and services.</p>
Coupled PPE model output - land parameter impacts on the mean climate state
<p>Terrestrial processes influence the atmosphere by controlling land-to-atmosphere fluxes of energy, water, and carbon. Prior research has demonstrated that parameter uncertainty drives uncertainty in land surface fluxes. However, the influence of land process uncertainty on the climate system remains underexplored. Here, we quantify how assumptions about land processes impact climate using a perturbed parameter ensemble for 18 land parameters in the Community Earth System Model (CESM2) under preindustrial conditions. We find that an observationally-informed range of land parameters generate biogeophysical feedbacks that significantly influence the mean climate state, largely by modifying evapotranspiration. Global mean land surface temperature ranges by 2.2°C across our ensemble (standard deviation = 0.5°C) and precipitation changes were significant and spatially variable. Our analysis demonstrates that the impacts of land parameter uncertainty on surface fluxes propagates to the entire Earth system, and provides insights into where and how land process uncertainty influences climate.</p>
Canada's extreme wildfires dominate the decline in global land carbon sinks in 2023
<p>Terrestrial land carbon sinks are strongly influenced by climate extremes, and 2023 is the warmest year on record, accompanied by an El Niño event, extreme wildfires, and extreme precipitation and drought, but their impact on global land sinks in 2023 remains unclear. Here, we used the Global Carbon Assimilation System, version 2, to estimate recent global land sinks by assimilating the OCO-2 ACOS v11.1 XCO2 retrievals. We estimate the global land sink to be -1.63 ± 0.52 PgC/yr in 2023. Compared to 2017-2022, it decreases by 0.59 PgC/yr, in which net ecosystem exchange decreases by only 0.14 PgC/yr, but wildfire emissions increase significantly by 0.45 PgC/yr, mainly in Canada. Our findings suggest that extreme wildfires are an important threat to land sinks under global warming.</p>
Climate regulates the effect of land-use change on the diversity of soil microbial functional groups and soil multifunctionality
<p>Although studies have explored how soil microbial diversity and soil multifunctionality respond to land-use change at local scales, they have rarely been explored at larger scales and across different climatic and soil environmental conditions.</p> <p>By sampling 40 paired sites of land-use change from natural forests to agricultural lands (including croplands and orchards) along the middle and lower Yangtze River, combined with a global meta-analysis, we investigated the effects of land-use change and climate on the alpha and beta diversity of soil bacterial and fungal functional groups (FGs) and their associated soil multifunctionality at a regional scale.</p> <p>Our results showed that land-use change strongly changed the diversity of soil bacterial and fungal FGs and decreased multifunctionality, which was supported by our meta-analysis at a global scale. Direct effects of land-use change and climate and their interaction, together with changes in soil environmental variables, were the main determinants of the land-use change-induced changes in the diversity of soil bacterial or fungal FGs. The land-use change-induced decrease in multifunctionality was mainly associated with the direct effect of forest conversion, soil fertility, and diversity of fungal FGs. Furthermore, climate also regulated the effects of land-use change on multifunctionality by affecting soil fertility and fungal FGs diversity along the Yangtze River.</p> <p><em>Synthesis and applications</em>. Taken together, our findings highlight the important effects of land-use change, climate, and their interactions on microbial diversity and multifunctionality, and suggest that effective land-use management and climate change mitigation strategies should be adopted to protect biodiversity and ecosystem function in the Yangtze River Basin.</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.