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501 results for “Remote Sensing”
Dataset for paper "Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity"
<p>Interpreting the shifts in forest structure, plant community composition, diversity, and functional identity by using remote sensing-derived wildfire severity . New collected data</p>
Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.
<p>For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km<sup>2</sup> watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content<br> in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (∼ 0.16 km<sup>3</sup>y<sup>−1</sup>) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based approaches, particularly in areas of discontinuous and sporadic permafrost.</p>
Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery
Open the record for dataset details and reuse information.
Forest disturbance detection by using remote sensing and artificial intelligence in Africa
<p>The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration funded by the European Space Agency and conducted by Wrocław University of Environmental and Life Sciences (Poland) and Lagos State University (Nigeria). Designed for forest monitoring in the Ogun and Lagos States, the dataset includes detailed land cover classification maps for the years 2015, 2019, 2022, and 2023, all at a 20-meter spatial resolution to ensure accurate representation of land cover. A legend file accompanies the maps, clarifying six defined land cover classes: water, urbanized areas, soil, cropland, grasslands, and forest.</p> <p>The dataset includes over 112,000 training and 11,900 validation samples, which are essential for the accurate development and evaluation of the Random Forest classification models used. Special emphasis was placed on the forest class, ensuring a diverse representation of forest types, including tropical humid forests, mangroves, and dry woodlands.</p> <p>In addition to land cover maps, the dataset also contains forest gain and loss maps, with a particular focus on recent updates for selected subareas in 2022 and 2023, available in the NTR_ForestUpdate folder.</p> <p>Based on optical satellite imagery from Sentinel-2 and Landsat-8, the dataset leverages spectral indices and the Random Forest algorithm to classify land cover types. It provides valuable insights for environmental research related to deforestation, reforestation, and afforestation. Forest change maps are included to highlight areas of forest loss and gain, capturing the dynamic shifts in Nigeria's forest cover and offering detailed geographic context.</p> <p>The methodology, including data acquisition, preprocessing, feature extraction (spectral index calculation), classification, and accuracy assessment, is fully documented in Python scripts available in the associated GitHub repository. This ensures transparency and reproducibility, offering users both the processed outputs and the tools necessary for custom analyses and advanced forest monitoring.</p>
data_Systematic review and best practices for drone remote sensing of invasive plants
<p>We generated this dataset to compile a review article titled "Systematic Review and Best Practices for Drone Remote Sensing of Invasive Plants." </p>
ASSESSING THE CHLOROPHYLL-A VARIABILITY IN THE GULF OF GUINEA USING REMOTE SENSING DATA.
<h3>Introduction</h3> <p>The report begins by highlighting the importance of oceans in influencing the Earth’s climate and supporting marine life. It focuses on phytoplankton, which are crucial for the marine food web and global carbon cycle. The study aims to evaluate the variability of chlorophyll-a (Chl-a) and sea surface temperature (SST) in the Gulf of Guinea using satellite remote sensing data.</p> <h3>Materials and Methods</h3> <ul> <li><strong>Study Site</strong>: The Gulf of Guinea, located on the eastern edge of the Atlantic Ocean, bordered by several West African countries.</li> <li><strong>Data</strong>: Monthly Chl-a and SST data from the Aqua-MODIS satellite, covering the period from 2020 to 2022.</li> <li><strong>Methods</strong>: Analysis of satellite images using Python programming to evaluate spatiotemporal variability and conduct time series analysis.</li> </ul> <h3>Results and Discussion</h3> <ul> <li><strong>Chlorophyll-a Variability</strong>: The study found significant spatial and temporal variability in Chl-a concentrations, with higher values near the coastline due to nutrient inputs from rivers and coastal upwelling.</li> <li><strong>Sea Surface Temperature Variability</strong>: SST showed relatively uniform spatial distribution but notable seasonal and interannual variability, influenced by climatic phenomena like the West African Monsoon.</li> <li><strong>Interannual and Monthly Climatology Variability</strong>: The report discusses the seasonal patterns and the influence of environmental factors on Chl-a and SST.</li> </ul> <h3>Conclusion</h3> <p>The study concludes that Chl-a concentrations are higher near the coast due to nutrient inputs and coastal upwelling, while SST shows a consistent seasonal cycle. These findings provide insights into the dynamic nature of marine productivity in the Gulf of Guinea and the influence of environmental factors on phytoplankton biomass.</p>
Data for the publication: Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations
<p>This repository contains the data and results associated to the publication submitted entitled "Surging process and mechanism of small glaciers in the Qilian mountains revealed by long-term and dense remote sensing observations".</p> <p>The results and data contain:</p> <ul> <li>Raw and processed ASTER DEM time series data stored in netcdf format (<em>Hala_surges_aster**.nc</em>): </li> </ul> <ol> <li>Raw DEM stack composed of 56 ASTER DEM.</li> <li>Processed DEM stacks generated by LOWESS-ALPS-REML workflow in each step.</li> </ol> <ul> <li>Multi-temporal elevation change maps stored in geotiff format:</li> </ul> <ol> <li>multi-temporal elevation change results calculated from different DEMs during different period (<em>Hala_surges_[sensor]_[period]_dh_final.tif</em>).</li> <li>Elevation difference map of SRTM-X and SRTM-C DEMs for estimation penetration depth difference ( <br><em>strm-c_x_n37_39_e96_e98_pentration_dh_final.tif</em>)</li> </ol> <ul> <li>Flow velocity time-series result processed by TICOI package stored in netcdf format:</li> </ul> <ol> <li>Irregular-sampling time-series inverted flow velocity results, represted by pixel-wise cumulative displacements ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_invert_ticoi.nc</em>)</li> <li>Regular-sampling time-series flow velocity results, interpolated to 30 days interval from the inverted results ( <br><em>Hala_surges_LS7_LS8_ticoi_flow_angle_refine_velo_interp_ticoi.nc</em>)</li> </ol>
Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 2_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Aerial Images_Part 1_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
Remote Sensing with TerrSet Guide Tutorial Data
<p>The Remote Sensing Guide provides a comprehensive introduction to the TerrSet remote sensing software package. With clear instructions and more than 300 color illustrations, the text is ideal for students and professionals seeking a hands-on and guided exploration of the fundamental issues in remote sensing and image processing. This latest version is digital only (<a href="https://www.amazon.com/Remote-Sensing-TerrSet-2020-IDRISI-ebook/dp/B08V1LBT15/ref=sr_1_12?crid=34HSK8ZIPQ9PG&dchild=1&keywords=remote+sensing+guide&qid=1611854020&s=books&sprefix=remote+sens%2Cstripbooks%2C148&sr=1-12">buy on Amazon</a>) and works with TerrSet liberaGIS release as well as also previous versions of TerrSet. You can download data for each chapter separately, or use the Download All button to download all of them toether. Please note that all files are zipped.</p>
Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada
<p>Dataset representing functional lake-to-channel connectivity in the Mackenzie Delta, NWT, Canada between 1984 and 2022 (final.class_20230324.feather), developed using Landsat 5, 7, and 9 optical imagery. </p> <p>Data in folders corresponds to data processing steps in scripts: https://doi.org/10.5281/zenodo.14618991</p> <p>Associated with manuscript: Remote sensing of multitemporal functional lake-to-channel connectivity and implications for water movement through the Mackenzie River Delta, Canada in WRR: Dolan, W., Pavelsky, T. M., & Piliouras, A. (2024). Remote sensing of multitemporal functional lake‐to‐channel connectivity and implications for water movement through the Mackenzie River Delta, Canada. <em>Water Resources Research</em>, <em>60</em>(4), e2023WR036614. https://doi.org/10.1029/2023WR036614</p>
E-scape: consumer-specific landscapes of energetic resources derived from stable isotope analysis and remote sensing
<p>Energetic resources and habitat distribution are inherently linked. Energetic resource availability is a major driver of the distribution of consumers, but estimating how much specific habitats contribute to the energetic resource needs of a consumer can be problematic.</p> <p>We present a new approach that combines remote sensing information and stable isotope ecology to produce maps of energetic resources (<i>E</i>-scapes). <i>E</i>-scapes project species-specific resource use information onto the landscape to classify areas based on energetic importance.</p> <p>Using our <i>E</i>-scapes, we investigated the relationship between energetic resource distribution and white shrimp distribution and how the scale used to generate the <i>E</i>-scape mediated this relationship.</p> <p><i>E</i>-scapes successfully predicted the size, abundance, biomass, and total energy of a consumer in salt marsh habitats in coastal Louisiana, USA at scales relevant to the movement of the consumer.</p> <p>Our <i>E</i>-scape maps can be used alone or in combination with existing models to improve habitat management and restoration practices and have potential to be used to test fundamental movement theory. </p>
Preliminary on-ice remote sensing measurements during the MOSAiC expedition
<p>Several different remote sensing instruments were deployed on the sea ice floe next to RV Polarstern during the MOSAiC expedition (mosaic-expedition.org). Here, preliminary data from nine instruments for two observation periods (Nov 2019 and Sep 2020) is provided. Initial calibration was performed but data might change for the final datasets. Outliers were filtered and some time series smoothed. Data from Figure 10 in Nicolaus et al. (2021), "Overview of the MOSAiC expedition – Snow and Sea Ice", Elementa:</p> <p>Results from co-located active and passive remote sensing instruments (Table 2) looking at similar ice and snow conditions (Figure S4). (left) Measurements during a warming and storm event in November 2019 and (right) during a melting event in September 2020. (A) Air temperature and wind speed from the Polarstern weather station and snow surface temperature from the IR camera at the Remote Sensing Site (dashed blue line shows time periods with potential icing on the lens). (B) Radar backscatter at VV polarization from 2145 microwave scatterometers L-SCAT at 1.3 GHz and Ku/Ka-radar at 15 and 35 GHz (note the different y-scales). (C) Brightness temperature at V polarization from microwave radiometers: ELBARA at 1.4 GHz, ARIEL at 1.4 GHz looking at thin ice on a lead, HUTRAD at 7 and 11 GHz, SSMI at 19, 37, 89 GHz (not all available data shown). (D) Reflected GNSS data, i.e., reflectivity at the Remote Sensing Site (blue) and for sea ice next to Polarstern (red). In the plot titles the used incidence angle range is given. Vertical dashed lines mark the start of warming and/or storm events. (E) Exemple photographs of the remote sensing site during winter and summer.</p>
Data from: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the processed and extracted metrics from the Dutch land cover, country wide airborne laser scanning and Sentinel-1 and 2 datasets used as input predictor variables in the species distribution modelling step. The study area within the Netherlands comprised five Dutch provinces (Groningen, Drenthe, Overijssel, Gelderland, and Flevoland) for which both ALS and Sentinel data were available for the same year. The land cover metrics were derived using the Dutch land cover map from 2018 (LGN2018 or LGN8). The country-wide LiDAR point clouds were derived from the third Dutch national ALS flight campaign (AHN3, Actueel Hoogtebestand Nederland). The AHN3 dataset is openly accessible data available from (<a href="https://ahn.arcgisonline.nl/ahnviewer/">https://ahn.arcgisonline.nl/ahnviewer/</a>). The Sentinel datasets were processed using Google Earth Engine. </p>
Cloud-free Chinese Gaofen-1 WFV near-infrared surface reflectance over Huailai remote sensing test site throughout 2020
<p>Land surface reflectance product form the starting point for many application regions such as land cover mapping and the generation of biophysical essential climate variables (ECV). Therefore, ensuring the quality of surface reflectance products is necessary to maintain the integrity of the research outcoming of these application areas. However, ground validation of surface reflectance satellite products is challenging, because ground “truth” on a coarse grid scale based on sparse ground measurements is subject to uncertainty due to spatial heterogeneity. In order to quantify the influence of spatial heterogeneity on the uncertainty of surface reflectance ground “truth” in different sampling cases, we generated the high-resolution (16 m) near-infrared surface reflectance over Huailai remote sensing test site based on Chinese Gaofen-1 WFV Band4 data.</p> <p> </p> <p>All cloud-free GF-1 WFV images throughout the year 2020 were extracted. And there are 25 images in total, with at least one image for each month. The WFV Band4 data covering the whole Huailai test station have been processed into Analysis Ready Data (ARD) system, which aims to simplify and reduce the users’ burden by providing pre-processing such as geometric alignment, radiometric recalibration, and atmospheric correction (Zhong et al., 2021). The geometric normalization of the GF-1 WFV data was realized with the procedure developed by Shan et al. (2014). And the radiometric normalization was finished through cross-calibrating with the Landsat TM/OLI with the method proposed by Yang et al. (2015). The 25 images have been layer stacked into one file according to their acquisition time.</p> <p> </p> <p> </p> <p>Reference:</p> <p>Shan, X. J., P. Tang, and C. M. Hu (2014), An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images, Int J Remote Sens, 35(20), 7154-7178.</p> <p>Yang, A., B. Zhong et al. (2015), Cross-calibration of GF-1/WFV over a desert site using Landsat-8/OLI imagery and ZY-3/TLC data, Remote Sens., 7, 10763–10787.</p> <p>Zhong, B., A. Yang, Q. Liu, S. Wu, X. Shan, and X Mu (2021), Analysis ready data of the chinese gaofen satellite data, Remote Sens., 13, 9, 1709.</p>
Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data
Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in productivity, opening up possibilities to fully harness remote sensing data and reliably scale up from traits to map global productivity in a more direct, efficient, and cost-effective manner.
Training course materials: Introduction to Remote Sensing of Water Quality in Lakes
<p>Dataset including ESA Sentinel-2 MSI and Sentinel-3 OLCI L1B scenes (L1B), atmospherically corrected reflectance products (C2RCC), image subsets, and intermediate water quality products from the Copernicus Land Service and ESA Lakes_cci processing chain <em>Calimnos. </em>Data are in support of a recorded course introducing the theoretical and practical basis for water quality observation of lakes through remote sensing. See https://monocle-h2020.eu/Resources/Training</p> <p>The data package contains the following:</p> <p><strong>input_data.zip </strong>- unprocessed (L1B / L1C) satellite scenes of MSI and OLCI instruments over the Razelm/Sinoe lagoon and Black Sea</p> <p><strong>Output_S3_OLCI.zip - </strong>all outputs derived from the Sentinel-3 OLCI scene, including C2RCC and <em>Calimnos </em>(including POLYMER atmospheric correction and biogeochemical estimates as in the Lakes_cci)</p> <p><strong>Output_S2_MSI_Full_Scene_C2RCC.zip</strong> - atmospherically corrected (C2RCC algorithm) full Sentinel-2 MSI scene</p> <p><strong>Output_S2_MSI_Subset_C2RCC.zip </strong>- subset of Sentinel-2 MSI scene and atmospherically corrected results (C2RCC algorithm) </p> <p><strong>Output_S2_MSI_Calimnos_PML.zip </strong>- subset of Sentinel-2 MSI scene atmospherically corrected using POLYMER and biogeochemical estimates, using <em>Calimnos </em>as in the Copernicus Land Service. </p>
APRA500: a 500 m annual paddy rice dataset for monsoon Asia using multisource remote sensing data
<p>This dataset provides 500m-grid paddy rice maps of monsoon Asia (some countries) from 2000 to 2021.</p> <p>*** Updated paddy rice map for 2021</p> <p>*** The data file is in “.tif" format</p> <p>*** Temporal Resolution: Yearly</p> <p>*** Pixel size: 500 m</p> <p>*** Projection information: EPSG: 4326</p> <p>The map boundary employed in this database does not imply the expression of any opinion whatsoever on the part of us concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries.</p>
Remote Sensing Satellite Video Dataset for Super-resolution
<p>This is a satellite video super-resolution dataset generated from "Jilin-1" video satellite.</p> <p>Training set: 189 clips; Test set: 12 clips.</p> <p>More details can be found in our paper published in IEEE TGRS: https://ieeexplore.ieee.org/document/9530280</p> <p>If you find our work helpful, please cite our paper. Thank you very much!</p>
Remote sensing and field information aid in predicting the presence of the terrestrial orchid Cyclopogon lute-albus
<p>Tropical montane cloud forest is one of the most threatened ecosystems, in central Veracruz, Mexico. Within this ecosystem, terrestrial orchids are strongly dependent on forest conditions and may be sensitive to environmental change. We applied field surveys of abiotic and biotic factors associated with the presence of the terrestrial orchid <em>Cyclopogon luteo-albus</em> and combined this with correlative niche modeling approaches to evaluate its potential distribution under two different environmental sets and two different extents. Layers of environmental information were obtained from Landsat imagery and interpolated bioclimatic and soil property layers. Existing species records were used as training data by sampling five forest fragments in central Veracruz, utilizing herbarium and global biodiversity information facility databases. The resulting predictions were tested by sampling at 15 sites of potential distribution. The model predicted the presence of <em>C. luteo-albus</em> with a reliability of 80%. The most important variables of models derived from interpolated layers coincide with site-relevant parameters suggesting the utility of these tools to further explore species suitabilities at larger geographic extents. Potential distribution mapping is an important tool to identify key areas for conservation and priority areas for future studies of species and partially resolves the lack of records of many orchid species.</p>
ScienceDex guides
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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.