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239 results for “remote sensing data”
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
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Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data
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Data from: A neighborhood approach for using remotely sensed data to estimate current ranges for conservation assessments
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Monitoring long-term vegetation dynamics over the Yangtze River Basin, China, using multi-temporal remote sensing data
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Data from: Predicting photosynthesis-irradiance relationships from satellite remote-sensing observations
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Climactic data derived from remotely sensed, daily weather parameters (NASA DAYMET): Maricopa County, AZ (2000-2016)
overview There is considerable interest in using climatic variables and bioclimatic predictors not only in ecological species distribution models but in interdisciplinary studies of urban environments. We compiled a downloadable geodatabase of monthly environmental variables on a 1km x 1km spatial resolution including raw climate variables such as precipitation, minimum and maximum air temperature, and water vapor pressure obtained from NASA Earth Science Data and Information System Daily Surface Weather and Climatological Summaries (DAYMET) for Maricopa County, Arizona. This geodatabase of environmental variables provides accessible vital data for the entire Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) study area that can be used in an array of interdisciplinary studies. related data set Annual bioclimatic predictors for Maricopa County (as defined by Nix, 1986 and Hijmans, 2004) generated from data in this data set are accessible from: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-cap&identifier=661 literature cited Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A., 2004. The WorldClim interpolated global terrestrial climate surfaces. Version 1.3. Nix, Henry A., 1986, A biogeographic analysis of Australian elapid snakes, in Longmore, Richard, ed., Atlas of elapid snakes of Australia: Canberra, Australian Flora and Fauna Series 7, Australian Government Publishing Service, p. 4‒15.
Modelling Avian Habitat Suitability in Boreal Forest using Structural and Spectral Remote Sensing Data
<p>Data used in research regarding avian habitat suitability models in Harry's River Watershed in Newfoundland, Canada</p>
Characterization of Industrial Smoke Plumes from Remote Sensing Data
<p><strong>Characterization of Industrial Smoke Plumes from Remote Sensing Data</strong><br> </p> <p>This data set contains imaging data acquired by ESA's <a href="https://earth.esa.int/web/sentinel/missions/sentinel-2">Sentinel-2 Earth-observing satellite constellation</a> for a sample of industrial sites that were picked based on emission information provided by the <a href="https://www.eea.europa.eu/data-and-maps/data/industrial-reporting-under-the-industrial">European Pollutant Release and Transfer Register</a>. The images contain scenes of mainly industrial sites, some of which are actively emitting smoke plumes.</p> <p>This data set was created to investigate whether it would be possible to train a deep learning model to automatically identify and segment smoke plumes from remote sensing image data. Please refer to the acknowledgements section for more on information on this project.</p> <p><br> <strong>Description</strong></p> <p>Each image is provided in the GeoTIFF file format, contains a total of 13 bands and georeferencing information, and has a shape of 120 x 120 pixels (corresponding to a square area with an edge length of 1.2 km on the ground). The bands are extracted from Sentinel-2 Level-2A products, except for band 10, which has been extracted from the<br> corresponding Level-1C product (this band has not been utilized in the underlying work).</p> <p>This repository contains a total of 21,350 images. Based on manual annotation, the image sample was split into a sample of 3,750 <em>positive</em> images that contain industrial smoke plumes, and 17,600 <em>negative</em> images that do not contain smoke plumes. Furthermore, this repository contains a collection of JSON files that hold manual segmentation labels for smoke plumes present in 1,437 images. Segmentation labels were generated using <a href="http://https://labelstud.io/">label-studio</a>. Please note that polygon edge coordinates have to be scaled by a factor of 1.2 to fit the images.</p> <p><br> <strong>Content</strong></p> <p>The following tarballs are contained in this repository:</p> <ul> <li>README.md - this file</li> <li>images.tar.gz [6.0GB] - contains 21,350 GeoTIFF images</li> <li>segmentation_labels.tar.gz [350KB] - contains 1,437 JSON files</li> </ul> <p><strong>Acknowledgement</strong></p> <p>If you use this data set, please cite our publication:</p> <p><em>Mommert, M., Sigel, M., Neuhausler, M., Scheibenreif, L., Borth, D., "Characterization of Industrial Smoke Plumes from Remote Sensing Data", Tackling Climate Change with Machine Learning workshop at NeurIPS 2020.</em></p> <p>Please refer to this publication for additional information on the data set.</p> <p>The code used for this publication is available at <a href="https://github.com/HSG-AIML/IndustrialSmokePlumeDetection">github</a>.</p> <p>This data set contains modified Copernicus Sentinel data acquired in 2019, processed by ESA.</p> <p> </p> <p><strong>Responsible Author</strong></p> <p>Michael Mommert<br> University of St. Gallen, Institute of Computer Science<br> Chair Artificial Intelligence and Machine Learning<br> michael.mommert ( at ) unisg.ch</p>
Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)
<p>The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of IberianWhite Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.</p>
Data from: Remotely-sensed primary productivity shows that domestic and native herbivores combined are overgrazing Patagonia
1. Carrying capacity is the maximum density of animals an area can sustain without a deterioration of its resources. Overgrazing degraded Patagonia grasslands in the past, but sheep stocks decreased in the last three decades and gave way to a mixed system with cattle, goats and guanacos (native wild camelids). 2. The objective of this paper was to develop a method to estimate carrying capacity based on remotely sensed data, and to assess wild and domestic herbivore numbers to establish if combined grazing stocks have evolved to a balance with carrying capacity. 3. Net Primary Productivity (NPP) MOD17/A3 images and Aerial Net Primary Productivity (ANPP) data of 66 sites were linearly regressed (R2= 0.83, P<0.01), and the slope 0.236 was used to convert MOD17/A3 NPP to ANPP. The proportion of ANPP that can be sustainably consumed (Harvest index) was HI=-5.71+0.72 ANPP0.5. Consumable forage was CF =ANPP.HI and carrying capacity CC =CF.EAC-1, where EAC is an estimate of annual consumption: 500, 3200 and 750 kg DM.head-1.year-1 for sheep or goats, cattle and guanacos, respectively. 4. Regional ANPP±SD (2000-2015) was 758±52 kg DM ha-1.yr-1; HI= 13.7±0.6% and CF= 104±12 kg DM ha-1yr-1 resulting in regional carrying capacity of 14.8±1.6 M sheep or goats, 2.3±0.3 M cattle or 9.9±1.2 M guanacos. 5. Domestic stock was high from 1920 to 1980, but declined thereafter and remained mostly within ±1SD of carrying capacity in this century. Annual mean provincial stocks and carrying capacity (2000-2015) correlated well (R²= 0.94, p<0.01) with a slope close to 1. 6. Guanacos increased from 0.5 M to 2 M between 2000 and 2015, driving linearly combined grazing pressures 36 and 62% above carrying capacity in southern Patagonia provinces in 2015. 7. Synthesis and applications: Herbivore de-stocking is necessary to restore grazing balance in south Patagonia. Guanacos are capable of grazing low-quality food and maintain production during dry periods, and their meat and fibre may be incorporated and combined with sheep production. Management of population of guanacos in mixed grazing systems with adjusted stocks may prevent further rangeland degradation, farm abandonment, and loss of ecosystem services of these rangelands.05-Apr-2019
Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
Plant responses to biotic and abiotic legacies left in soil by preceding plants is known as plant–soil feedback (PSF). PSF is an important mechanism to explain plant community dynamics and plant performance in natural and agricultural systems. However, most PSF studies are short-term and small-scale due to practical constraints for field-scale quantification of PSF effects, yet field experiments are warranted to assess actual PSF effects under less controlled conditions. Here we used unmanned aerial vehicle (UAV)-based optical sensors to test whether PSF effects on plant traits can be quantified remotely. We established a randomized agro-ecological field experiment in which six different cover crop species and species combinations from three different plant families (Poaceae, Fabaceae, Brassicaceae) were grown. The feedback effects on plant traits were tested in oat (Avena sativa) by quantifying the cover crop legacy effects on key plant traits: height, fresh biomass, nitrogen content, and leaf chlorophyll content. Prior to destructive sampling, hyperspectral data were acquired and used for calibration and independent validation of regression models to retrieve plant traits from optical data. Subsequently, for each trait the model with highest precision and accuracy was selected. We used the hyperspectral analyses to predict the directly measured plant height (RMSE = 5.12 cm, R2 = 0.79), chlorophyll content (RMSE = 0.11 g m−2, R2 = 0.80), N-content (RMSE = 1.94 g m−2, R2 = 0.68), and fresh biomass (RMSE = 0.72 kg m−2, R2 = 0.56). Overall the PSF effects of the different cover crop treatments based on the remote sensing data matched the results based on in situ measurements. The average oat canopy was tallest and its leaf chlorophyll content highest in response to legacy of Vicia sativa monocultures (100 cm, 0.95 g m−2, respectively) and in mixture with Raphanus sativus (100 cm, 1.09 g m−2, respectively), while the lowest values (76 cm, 0.41 g m−2, respectively) were found in response to legacy of Lolium perenne monoculture, and intermediate responses to the legacy of the other treatments. We show that PSF effects in the field occur and alter several important plant traits that can be sensed remotely and quantified in a non-destructive way using UAV-based optical sensors; these can be repeated over the growing season to increase temporal resolution. Remote sensing thereby offers great potential for studying PSF effects at field scale and relevant spatial-temporal resolutions which will facilitate the elucidation of the underlying mechanisms.
Data from: Remotely sensed data informs red list evaluations and conservation priorities in southeast Asia
The IUCN Red List has assessed the global distributions of the majority of the world's amphibians, birds and mammals. Yet these assessments lack explicit reference to widely available, remotely-sensed data that can sensibly inform a species' risk of extinction. Our first goal is to add additional quantitative data to the existing standardised process that IUCN employs. Secondly, we ask: do our results suggest species of concern—those at considerably greater risk than hitherto appreciated? Thirdly, these assessments are not only important on a species-by-species basis. By combining distributions of species of concern, we map conservation priorities. We ask to what degree these areas are currently protected and how might knowledge from remote sensing modify the priorities? Finally, we develop a quick and simple method to identify and modify the priority setting in a landscape where natural habitats are disappearing rapidly and so where conventional species' assessments might be too slow to respond. Tropical, mainland Southeast Asia is under exceptional threat, yet relatively poorly known. Here, additional quantitative measures may be particularly helpful. This region contains over 122, 183, and 214 endemic mammals, birds, and amphibians, respectively, of which the IUCN considers 37, 21, and 37 threatened. When corrected for the amount of remaining natural habitats within the known elevation preferences of species, the average sizes of species ranges shrink to <40% of their published ranges. Some 79 mammal, 49 bird, and 184 amphibian ranges are <20,000km2—an area at which IUCN considers most other species to be threatened. Moreover, these species are not better protected by the existing network of protected areas than are species that IUCN accepts as threatened. Simply, there appear to be considerably more species at risk than hitherto appreciated. Furthermore, incorporating remote sensing data showing where habitat loss is prevalent changes the locations of conservation priorities.
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.
Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria
<p>Meteorological data_Integrating Remote Sensing and Machine Learning for Developing Spatio-Temporal Model to Predict Aquatic Larval Habitats of Malaria</p>
GPS data and plotting codes for Remote Sensing paper titled Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity
<p>Data files (meteorological data, sattelite derived velocity time series, and seismic station GPS data) and plotting codes to reproduce the dataset and plots presented in the paper Gajek et al., Utilizing Seismic Station Internal GPS for Tracking Surging Glacier Sliding Velocity</p>
Spatio-temporal analysis of remotely sensed forest loss data in the Cordillera Administrative Region, Philippines
<p>The Cordillera Administrative Region (CAR) in the Philippines is among the last forest frontiers in the country and is also home to 13 major watersheds in Northern Luzon that supply irrigation and hydroelectricity to other regions. However, it is faced with the deterioration of the quality of its watersheds due to forest loss driven mainly by agricultural expansion and illegal logging. Thus, this study was conducted to analyze the spatial and temporal patterns of forest loss that could serve as a basis for policy decisions. Also, this paper determined the strength of relationships using Pearson's correlation coefficient (<i>r</i>) between forest loss and seven independent variables, which includes forest cover, agricultural areas, built-up, road network, and socio-economic data. This study utilized the Hansen Global Forest Change (HGFC), a Landsat-derived dataset from 2001 to 2019. Results revealed that 70,925 hectares (ha) of forest loss were detected with an annual deforestation rate of 3,744 ha/year across the region. Based on the validation, the accuracy of the HGFC data is 72%, but great caution should be observed when using the data with less than 0.2 ha due to very low accuracy. On a region-wide analysis, only the forest cover had a strong association with the forest loss with a computed <i>r-value</i> of 0.78. Conversely, on a provincial level, the explanatory variables had a strong to moderately strong correlation with deforestation. Hence, immediate, science-based, and sustained regional and multi-stakeholder efforts are necessary to conserve and protect the remaining forest cover in the region.</p>
Data-Linking remote sensing data to the estimation of pollination services in agroecosystems
<p>Wild bees are key providers of pollination services in agroecosystems. The abundance of these pollinators, and the service they provide, relies on the availability of supporting resources in the landscape. Because of this, spatially explicit models have been developed to quantify wild bee abundance and pollination services in food crops, while accounting for the influence of locally available foraging and nesting resources. However, model implementation is limited by the availability of land cover maps and experts on pollinators capable of establishing the quality of local habitats for pollinators. In this study, we present how remote sensing data can be linked to the estimation of wild bee abundance, and act as an alternative to the conventional use of land cover maps and local expertise in spatially explicit models estimating pollination services. For this, we used landscape characteristics derived from remote sensors to qualify nesting resources in the landscape and thereafter estimate the delivery of pollination services by mining bees (<i>Andrena</i> spp.) in 30 fruit orchards located in the Flemish region of Belgium. Mining bees were selected for this study for their major role as local pollinators and underground nesting habits. Estimated pollination services were compared with those derived from conventional qualifications of nesting resources and showed no significant differences (P=0.68) in the amount of explained variation in activity of mining bees on the studied orchards. Estimates derived from remote sensing data and conventional inputs explaining 69% and 72% of the total variation, respectively. These results confirmed that remote sensing data can deliver nesting suitability characterizations suitable for the estimation of pollination services, This research also illustrates the relevance of nesting resources and highlights the importance of considering soil resources in the estimation of pollination services provided by pollinators like mining bees. Our results support the development of holistic agro-environmental policies that rely on the use of modern tools like remote sensors and promote pollinators by considering nesting resources.</p>
Bird observation data for: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds
<p>This data repository contains the bird observation data used in Koma, Z., Seijmonsbergen, A.C., Grootes, M.W., Nattino, F., Groot, J., Sierdsema, H., Foppen, R. & Kissling, W.D. (2022): Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds. <em>Diversity and Distributions</em> 28: 685–699.</p> <p>The content of this directory is shared under Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0, see https://creativecommons.org/licenses/by-nc-nd/4.0/). For accessing the bird occurrence data for further use then reproducing this article you can contact with Henk Sierdsema (Henk.Sierdsema@sovon.nl) and Ruud Foppen (Ruud.Foppen@sovon.nl) for further information.</p>
Data set to Remote sensing-supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem
<p>This data set is part of the article Wraase et al. (2022): Remote sensing -supported mapping of the activity of a subterranean landscape engineer across an afro-alpine ecosystem. Remote sensing in Ecology and Conservation. (https://doi.org/10.1002/RSE2.303)</p> <p>The repository contains a Readme file ("readme.txt") and two additional folders labeled: “input data” and “script”.<br> <br> The first folder contains 13 data files further divided into three subfolders “cca_analysis”, “main_modelling_prc_texture_idx” and “vectors”. Data formats are .csv format for all tables, .rds files for model objects from R and .shp format for all vector data.</p> <p>The second folder contains all 31 R-scripts necessary to do the analysis, as described in the article. Additionally, the folder is further categorized into five subfolders equivalent to the main analysis operations: “cca_analysis”, “landsat_temp_modelling”, “main_modelling_prc”, “maxent” and “texture_idx”.</p>
Remotely sensed temperature metrics data for Ningaloo Coast, Western Australia
<p>Here we made available four datasets of remotely sensed temperature metrics (daily SST, weekly SST, SSTA frequency, TSA DHW) from different NOAA and IMOS satellite products for the Ningaloo Coast (Western Australia) bounding box with coordinates [-23.5654,-21.66538], [113.4847,114.318], which can be used to assess thermal history and trends across coral reef areas within this World Heritage Area. </p> <ul> <li><a target="_blank" rel="noopener noreferrer">ningaloo_reef_crw_sst_1985-2022.nc:</a> Daily SST (1985-01-01 - 2022-12-29), NOAA Coral Reef Watch (CRW) Version 3.1 global 5 km daily nighttime SST (aka CoralTemp) </li> <li>ningaloo_reef_cortadv6_filled_sst_1985_2022.nc: Weekly SST (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_cortadv6_ssta_freq_1985_2022.nc: Frequency of SSTA (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_cortadv6_tsa_dhw_1985_2022.nc: TSA DHW (1985-12-31 - 2022-12-20), NOAA Coral Temperature Anomaly Database (CoRTAD) Version 6 global 4.6 km from weekly averaged daytime and nighttime SST;</li> <li>ningaloo_reef_imos_hinawari-8_L3C_sst_1_hour_2016-2022.nc: Hourly SST (2016-01-01 - 2022-12-13) , IMOS Hinawari-8 L3C 1 km 1 hour SST from 2016 to 2022</li> </ul> <p> </p> <p> </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.