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26 results for “multi-temporal”
Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles
Over the past two decades, severe mountain pine beetle (MPB) outbreaks have affected several million hectares of forest in western North America. The extensive ecological and economic damage caused by widespread insect infestations make understanding the development and spread of MPB outbreaks critical. This study uses a time series of Landsat5 TM and Landsat7 ETM + images to map the spread of mortality due to MPB infestation in Arapaho–Roosevelt National Forest, Colorado, between 2003 and 2010. The Normalized Difference Vegetation Index (NDVI) and change in the Normalized Difference Moisture Index (NDMI) were used to classify red attack and non-red attack stands based on a maximum likelihood algorithm with manually selected training classes. The classification was validated by comparison with independent interpretations of aerial photography and high-resolution satellite imagery. The classification had good agreement (84.5–90.5% total accuracy). Cluster analysis for time series showed infestations originating in several different locations on the landscape early in the time series and subsequent infestations likely represent a combination of dispersal from outbreak populations and independent population growth. Analysis using conditional inference trees suggested that a combination of forest composition, topography, and dispersal predicted the distribution of MPB infestation on the landscape and that the importance of these variables changed as the outbreak developed. In early years, red attack was associated with forest and topographic characteristics known to influence susceptibility to MPB. Over time, beetle pressure became an increasingly important predictor of red attack, but in later years host tree availability played an important role in outbreak spread. If this pattern occurs consistently in MPB outbreaks, knowledge of these patterns could aid managers in targeting their efforts to reduce damage resulting from MPB outbreaks.
Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model
<p>This dataset contains the hyperspectral images and train-test split from the article. </p> <p> </p> <p>The corresponding comet ML experiment is:</p> <p><a href="https://www.comet.com/bw4sz/deeptreeattention2/209ca047ed004d778c0f0e728e126bda?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&viewId=VWzVg9fkZDMidwu9VOZi2weM9&xAxis=epoch">CometML</a></p> <p>To protect ongoing scientific activities in the area, the geospatial position of the crops have been omitted. Please contact ForestGEO for data requests. </p> <p><a href="http://ForestGEO">https://forestgeo.si.edu/</a></p> <p>For the corresponding git repo please see: </p> <p>Contents:</p> <p>train.csv: The image crops used for model training. The image_path corresponds to the relative path to the image file in the directory.</p> <p>test.csv: The image crops used for model evaluation.</p> <p>*.tif images: A 369 band hyperspectral image using the NEON surface reflectance data cropped by the tree crown.</p> <p>The original HSI data: https://data.neonscience.org/data-products/DP3.30006.001. The location of the crown was predicted using the RGB data product and the deepforest model: https://deepforest.readthedocs.io/. </p> <p>The taxonID abbreviations follow NEON's taxonomy: https://data.neonscience.org/taxonomic-lists</p> <p>OSBS.shp: Shapefile with ensemble predictions for the full Ordway Swisher Biological Station. The ensemblaTa column is the predicted taxonID for each crown. The ens_score is the confidence prediction for that crown. The CHM_height is the extracted raster value from NEON's ecosystem structure canopy height model.</p>
Rock glacier inventory from multi-temporal InSAR measurement and optical image interpretation in southern Daxue Shan
<p>Based on the Sentinel-1A ascending SAR images acquired between June 2019 and June 2022, we derived a five-year-long annual average velocity map in southern Daxue Shan. Kinematic characteristics from InSAR measurement and geomorphic characteristics from optical image interpretation are then considered comprehensively for rock glaciers inventorying. A total of 860 rock glaciers are compiled and their geomorphic and kinematic parameters are calculated.</p>
Data from: Multi-temporal analysis reveals that predictors of mountain pine beetle infestation change during outbreak cycles
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M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH
MULTI-TEMPORAL REMOTE SENSING IMAGE CLASSIFICATION - A MULTI-VIEW APPROACH VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Multispectral remote sensing images have been widely used for automated land use and land cover classification tasks. Often thematic classification is done using single date image, however in many instances a single date image is not informative enough to distinguish between different land cover types. In this paper we show how one can use multiple images, collected at different times of year (for example, during crop growing season), to learn a better classifier. We propose two approaches, an ensemble of classifiers approach and a co-training based approach, and show how both of these methods outperform a straightforward stacked vector approach often used in multi-temporal image classification. Additionally, the co-training based method addresses the challenge of limited labeled training data in supervised classification, as this classification scheme utilizes a large number of unlabeled samples (which comes for free) in conjunction with a small set of labeled training data.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.