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13 results for “multitemporal”
Canopy damage and recovery following Hurricane Maria using multitemporal lidar data, Mar-2017 - Mar-2020, Puerto Rico
The data archive is here: http://dx.doi.org/10.15486/ngt/1797399 please use this DOI when citing this dataset. Hurricane Maria (Category 4) snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100 to 800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥1 m in canopy height (mean = -7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13% of the study area had canopy heights ≤2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss (<-1 m, 2017-2018) followed by rapid height gain (>1 m, 2018-2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no structural changes >|1| m in either period. Canopy layers <10 m accounted for most increases in canopy height and fractional cover between 2018-2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage. Included in the attached zip file are four TIF and four KML files. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Multitemporal multispectral imagery for rice yield and phenology prediction
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Sentinel-2 Multitemporal Cities Pairs
<p>This dataset contains N=1520 Sentinel-2 level 1C image pairs focused on urban areas around the world.<br> Bands with a spatial resolution smaller than 10 m are resampled to 10 m and images are cropped to approximately 600x600 pixels.<br> The size of some images is smaller than 600x600 pixels as result of the fact that some coordinates were located close to the edge of a Sentinel tile, the images were then cropped to the tile border.<br> Geometric or radiometric corrections are not performed.</p> <p>The dataset is released with the conference article: Marrit Leenstra, Diego Marcos, Francesca Bovolo and Devis Tuia, Self-supervised pre-training enhances change detection in Sentinel-2 imagery, PRRS workshop, ICPR 2020</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>
Multitemporal hyperspepectral datasets for unmixing
<p>Dataset containing synthetic and real multitemporal hyperspectral images for the evaluation of dynamical hypersepctral unmixing algorithms, related to the repository <a href="https://github.com/ricardoborsoi/ReDSUNN">https://github.com/ricardoborsoi/ReDSUNN</a> and to the paper:</p> <pre><code>Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks R.A. Borsoi, T. Imbiriba, P Closas. IEEE Transactions on Image Processing, 2023.</code></pre>
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
Multitemporal DSMs and orthomosaics of a beach nourishment at the Baltic Sea
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Multitemporal inventory of landslides in the epicentral region of the 2017 Jiuzhaigou earthquake
<p>Multitemporal inventory of landslides in the epicentral region of the 2017 Jiuzhaigou earthquake</p>
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
Open the record for dataset details and reuse information.
A Study on the Accurate Evaluation of Pseudoprogression of Rectal Cancer Immunotherapy Based on Multitemporal and Multiparameter MRI
ClinicalTrials.gov study NCT07381322. IPD Sharing: Not stated. Countries: 1. Publications: 0.
High Mountain Asia Multitemporal Landslide Inventories V001
The mountains of Nepal are one of the most hazardous environments in the world, with frequent landslides caused by tectonic activity, extreme rainfall and infrastructure development. As a landlocked country, Nepal relies on proper functioning of major transportation networks such as the highways to sustain and improve the livelihoods of the population. Every year there are reports of landslides blocking the highways, especially during the rainy season; however, the frequency and location of landslides along the highway corridors are not well reported. RapidEye satellite imagery was used to create annual landslide initiation point inventories along three important highways in Nepal: the Arniko, Karnali, and Pasang Lhamu highway.
High Mountain Asia Multitemporal Landslide Inventory for the Pumqu/Arun River Basin V001
The transboundary Pumpqu/Arun River basin spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumpqu River flows. The river is named the Arun River once it enters Nepal. Five large hydropower projects (in total about 3,163 MW) are currently under construction or are planned for the Arun River valley. Rainfall and earthquake-induced landslides, landslide dammed lakes, and landslide-induced glacial lake outburst floods pose major risks to the smooth operation of these projects. This data set is a multitemporal landslide inventory covering the whole Pumpqu/Arun River basin. It was generated in support of the World Bank’s Risk Assessment of Landslides in the Upper Arun Hydropower Project.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.