cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.2tk3kq
title: Occurrence Download

description: A dataset containing 119040 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galeorhinus galeus"
 ]
}

 The dataset includes 119040 records from 208 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060603-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2pyhek
title: Occurrence Download

description: A dataset containing 254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Jacksonia condensata",
 "HasCoordinate is true"
 ]
}

 The dataset includes 254 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060617-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30693490
title: Research on GB-SAR slope deformation geocoding method based on Bayes theorem

description: In recent years, the ground based deformation monitoring radar (GB-SAR), as an emerging remote sensing deformation monitoring technology equipment, has become a research hotspot in the field of open pit mine slope safety and landslide geohazard prevention and control. In this paper, for the fuzzy problem of spatial localization of pixel units in the superimposed mask area of radar image, the Bayesian statistical framework is introduced into the spatial geographic coding of foundation deformation monitoring radar for the first time, and the maximum a posteriori probability function model is established on the basis of the incidence angle of the target of the slope feature and the elevation angle of the antenna's vertical direction map, so as to realize the three-dimensional visualization of landslide deformation hazardous areas, and to give full play to the technological support of the early warning of landslide disaster by foundation deformation monitoring radar. First introduction of Bayesian statistical framework into spatial geocoding of GB-SAR to model the maximum a posteriori probability function. Realization of three-dimensional visualization and identification of landslide deformation hazard areas.
DOI: 10.6084/m9.figshare.30693490.v1
title: Research on GB-SAR slope deformation geocoding method based on Bayes theorem

description: In recent years, the ground based deformation monitoring radar (GB-SAR), as an emerging remote sensing deformation monitoring technology equipment, has become a research hotspot in the field of open pit mine slope safety and landslide geohazard prevention and control. In this paper, for the fuzzy problem of spatial localization of pixel units in the superimposed mask area of radar image, the Bayesian statistical framework is introduced into the spatial geographic coding of foundation deformation monitoring radar for the first time, and the maximum a posteriori probability function model is established on the basis of the incidence angle of the target of the slope feature and the elevation angle of the antenna's vertical direction map, so as to realize the three-dimensional visualization of landslide deformation hazardous areas, and to give full play to the technological support of the early warning of landslide disaster by foundation deformation monitoring radar. First introduction of Bayesian statistical framework into spatial geocoding of GB-SAR to model the maximum a posteriori probability function. Realization of three-dimensional visualization and identification of landslide deformation hazard areas.
DOI: 10.6084/m9.figshare.30752907
title: Research progress on anomaly target detection algorithms for hyperspectral imagery

description: Hyperspectral image (HSI) data plays an important role in remote sensing images, and researchers have done a great deal of work to understand and recognize HSI. Among them, hyperspectral image-anomaly target detection (HSI-ATD) has numerous applications in both national defense, military, and civilian fields. In particular, because anomaly target detection does not require any prior information, it has become one of the key technologies and research hotspots in HSI processing and information extraction. Through systematic sorting and analysis, this paper summarizes the existing anomaly target detection algorithms in detail, and evaluates the key issues involved in HSI-ATD, future technological development directions (such as deep learning, multimodal, and real-time processing, etc), as well as the problems existing in the algorithms. Additionally, some innovative viewpoints and predictions on future research trends are proposed.
DOI: 10.6084/m9.figshare.30752907.v1
title: Research progress on anomaly target detection algorithms for hyperspectral imagery

description: Hyperspectral image (HSI) data plays an important role in remote sensing images, and researchers have done a great deal of work to understand and recognize HSI. Among them, hyperspectral image-anomaly target detection (HSI-ATD) has numerous applications in both national defense, military, and civilian fields. In particular, because anomaly target detection does not require any prior information, it has become one of the key technologies and research hotspots in HSI processing and information extraction. Through systematic sorting and analysis, this paper summarizes the existing anomaly target detection algorithms in detail, and evaluates the key issues involved in HSI-ATD, future technological development directions (such as deep learning, multimodal, and real-time processing, etc), as well as the problems existing in the algorithms. Additionally, some innovative viewpoints and predictions on future research trends are proposed.
DOI: 10.6084/m9.figshare.28253663
title: Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images

description: Accurate remote sensing of wetland vegetation is challenging due to heterogeneous land cover, dynamic water reflectance and extent, and spectrally similar plant types. The high spatiotemporal resolution of nanosatellite constellations enables ‘phenological leveraging’ (PL)—identification and tracking of spectrally distinct phenological events to help differentiate wetland vegetation. We use PlanetScope Dove-R images, field training data, and PL to map wetland landcover in a complex riverine wetland in Rhode Island, USA. Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. The greatest improvements in classification accuracy derive from targeting optimal months of maximum phenological contrast for each landcover class, and then compositing these optimized classifications into a single map. We conclude that nanosatellite constellations offer a powerful new approach for mapping wetland vegetation classes at fine spatial scales.
DOI: 10.6084/m9.figshare.28253663.v1
title: Wetland vegetation mapping improved by phenological leveraging of multitemporal nanosatellite images

description: Accurate remote sensing of wetland vegetation is challenging due to heterogeneous land cover, dynamic water reflectance and extent, and spectrally similar plant types. The high spatiotemporal resolution of nanosatellite constellations enables ‘phenological leveraging’ (PL)—identification and tracking of spectrally distinct phenological events to help differentiate wetland vegetation. We use PlanetScope Dove-R images, field training data, and PL to map wetland landcover in a complex riverine wetland in Rhode Island, USA. Maximum Likelihood (MLC), Support Vector Machine (SVM), and Artificial Neural Network (ANN) classification algorithms are tested on individual, monthly- and multi-seasonal composite images. The greatest improvements in classification accuracy derive from targeting optimal months of maximum phenological contrast for each landcover class, and then compositing these optimized classifications into a single map. We conclude that nanosatellite constellations offer a powerful new approach for mapping wetland vegetation classes at fine spatial scales.
DOI: 10.6084/m9.figshare.28882348
title: The model for grain wheat yield prediction at high spatial resolution based on physical-geographical properties and satellite vegetation indices

description: Precision agriculture is promising approach for improving agricultural production, especially nowadays when the population is rapidly increasing. For that, crop yield estimation provides valuable information. The main research focus was to predict within-field grain yield and detect its drivers. The Random Forest regression model on data from diverse sources at the 10-meter spatial resolution was developed. The study was conducted in the Vojvodina region (Serbia) for eight wheat-planted fields, having precise grain yield data. Open-source data including 15 vegetation indices (VIs) was calculated from Sentinel-2 satellite bands, physical-geographical features obtained from the digital elevation model and soil properties. The model succeeded in predicting the wheat grain yield with the RMSE of 0.66 t/ha (average yield of 0.09 t/ha) and the best predictors were VIs considering chlorophyll and moisture content in plants, while physical-geographical properties managed to explain within-field variability. This methodology can be applied to other crops (maize, soybean).
DOI: 10.6084/m9.figshare.28882348.v1
title: The model for grain wheat yield prediction at high spatial resolution based on physical-geographical properties and satellite vegetation indices

description: Precision agriculture is promising approach for improving agricultural production, especially nowadays when the population is rapidly increasing. For that, crop yield estimation provides valuable information. The main research focus was to predict within-field grain yield and detect its drivers. The Random Forest regression model on data from diverse sources at the 10-meter spatial resolution was developed. The study was conducted in the Vojvodina region (Serbia) for eight wheat-planted fields, having precise grain yield data. Open-source data including 15 vegetation indices (VIs) was calculated from Sentinel-2 satellite bands, physical-geographical features obtained from the digital elevation model and soil properties. The model succeeded in predicting the wheat grain yield with the RMSE of 0.66 t/ha (average yield of 0.09 t/ha) and the best predictors were VIs considering chlorophyll and moisture content in plants, while physical-geographical properties managed to explain within-field variability. This methodology can be applied to other crops (maize, soybean).
DOI: 10.6084/m9.figshare.28882347
title: Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine

description: The objectives of this study are to map the past and present LULC accurately, ensure the precision of classification and prediction models, and estimate future LULC changes using Google Earth Engine. LULC classification from 2014-2024 was performed using robust SmileCART and future LULC prediction for 2024, 2030, 2040, 2050, 2060 and 2070 was conducted using the Smile Random Forest (RF) algorithm incorporating various socio-economic variables. The classification model SmileCART was trained by using the European Space Agency World Cover data and validated through Landsat 8 satellite imagery, achieving training and test accuracies of 83% and 84% respectively. SmileRF prediction model showed an accuracy of 87% with a kappa coefficient of 0.86. The results indicate a decline in vegetation cover, snow & ice and water bodies, and an increase in built-up areas and cropland, with other classes showing fluctuations in the Jhelum and Chenab River basins from 2014 to 2070. These insights contribute to a deeper understanding of these critical watersheds, informing sustainable land management, water resource planning, and decision-making for the future sustainable development of the Jhelum and Chenab River basins using GEE and remote sensing data.
DOI: 10.6084/m9.figshare.28882347.v1
title: Estimating the plausible projections of land use/land cover dynamics in Jhelum and Chenab River basins using satellite imageries and machine learning models in Google Earth Engine

description: The objectives of this study are to map the past and present LULC accurately, ensure the precision of classification and prediction models, and estimate future LULC changes using Google Earth Engine. LULC classification from 2014-2024 was performed using robust SmileCART and future LULC prediction for 2024, 2030, 2040, 2050, 2060 and 2070 was conducted using the Smile Random Forest (RF) algorithm incorporating various socio-economic variables. The classification model SmileCART was trained by using the European Space Agency World Cover data and validated through Landsat 8 satellite imagery, achieving training and test accuracies of 83% and 84% respectively. SmileRF prediction model showed an accuracy of 87% with a kappa coefficient of 0.86. The results indicate a decline in vegetation cover, snow & ice and water bodies, and an increase in built-up areas and cropland, with other classes showing fluctuations in the Jhelum and Chenab River basins from 2014 to 2070. These insights contribute to a deeper understanding of these critical watersheds, informing sustainable land management, water resource planning, and decision-making for the future sustainable development of the Jhelum and Chenab River basins using GEE and remote sensing data.
DOI: 10.6084/m9.figshare.29357159
title: Evaluating and mapping spatial drought in northeast Thailand: utilizing analytic hierarchy process and random forest algorithms

description: Drought is a recurring and costly natural disaster that affects all climate zones, posing significant challenges to agriculture and livestock. This study evaluates drought vulnerabilities in Northeast Thailand using geospatial datasets and analysis combined with a random forest (RF) approach. The Analytical Hierarchy Process (AHP) was employed to map different drought types based on 14 spatial criteria. The RF model, trained using geospatial datasets and the Standardized Precipitation Index (SPI) from 96 meteorological stations, demonstrated high accuracy and reliability. The results revealed that approximately 70% of the region is vulnerable to moderate to extreme drought conditions. The AHP model achieved an overall accuracy (OA) of 85.19%, aligning closely with the RF model, which had an OA of 88.89%. The RF algorithm effectively mapped spatial drought events with high accuracy, as confirmed by comparisons with rainfall data. These findings provide essential insights for drought mitigation strategies and regional planning.
DOI: 10.6084/m9.figshare.29357159.v1
title: Evaluating and mapping spatial drought in northeast Thailand: utilizing analytic hierarchy process and random forest algorithms

description: Drought is a recurring and costly natural disaster that affects all climate zones, posing significant challenges to agriculture and livestock. This study evaluates drought vulnerabilities in Northeast Thailand using geospatial datasets and analysis combined with a random forest (RF) approach. The Analytical Hierarchy Process (AHP) was employed to map different drought types based on 14 spatial criteria. The RF model, trained using geospatial datasets and the Standardized Precipitation Index (SPI) from 96 meteorological stations, demonstrated high accuracy and reliability. The results revealed that approximately 70% of the region is vulnerable to moderate to extreme drought conditions. The AHP model achieved an overall accuracy (OA) of 85.19%, aligning closely with the RF model, which had an OA of 88.89%. The RF algorithm effectively mapped spatial drought events with high accuracy, as confirmed by comparisons with rainfall data. These findings provide essential insights for drought mitigation strategies and regional planning.
DOI: 10.6084/m9.figshare.30621182
title: Improving lithological mapping by support vector machine classification using integrated visible-near infrared, shortwave infrared, and thermal infrared of ASTER data sets

description: This study aims to enhance lithological mapping by employing Support Vector Machine (SVM) classification on integrated visible-near infrared (VNIR)-shortwave infrared (SWIR), and thermal infrared (TIR) datasets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study focuses on a part of Kerman Province, the Dehsard area, which was selected due to its diverse lithological units, including igneous, sedimentary, and metamorphic rocks, as represented in the 1: 100,000 geological map of Dehsard. SVM classification was applied on the three datasets using training areas derived from the geological map, along with principal component analysis (PCA) and minimum noise fraction (MNF). The results indicated that the SVM classification on the 14-band ASTER data yielded more accurate results compared to the 9 and 5 band datasets. This study underscores the effectiveness of integrating multiple spectral bands in improving the precision of lithological mapping, which is essential for mineral exploration and geological studies.
DOI: 10.6084/m9.figshare.30621182.v1
title: Improving lithological mapping by support vector machine classification using integrated visible-near infrared, shortwave infrared, and thermal infrared of ASTER data sets

description: This study aims to enhance lithological mapping by employing Support Vector Machine (SVM) classification on integrated visible-near infrared (VNIR)-shortwave infrared (SWIR), and thermal infrared (TIR) datasets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study focuses on a part of Kerman Province, the Dehsard area, which was selected due to its diverse lithological units, including igneous, sedimentary, and metamorphic rocks, as represented in the 1: 100,000 geological map of Dehsard. SVM classification was applied on the three datasets using training areas derived from the geological map, along with principal component analysis (PCA) and minimum noise fraction (MNF). The results indicated that the SVM classification on the 14-band ASTER data yielded more accurate results compared to the 9 and 5 band datasets. This study underscores the effectiveness of integrating multiple spectral bands in improving the precision of lithological mapping, which is essential for mineral exploration and geological studies.
DOI: 10.6084/m9.figshare.29912265
title: Comparable analysis of analytical hierarchy process and fuzzy logic methods based on GIS and remote sensing for anticipating landslide susceptibility in the Iraqi Kurdistan Region, Koya district

description: Landslides pose a serious threat in urban areas with steep and varied terrain, such as the Koya district in northern Iraq. This study aims to produce landslide susceptibility maps (LSMs) using remote sensing data and three models: Analytical Hierarchy Process (AHP), Fuzzy Logic SUM, and Fuzzy Gamma 0.9. A total of 280 landslides were identified through field surveys, GPS, and Google Earth, and used for model training and validation. Fifteen conditioning factors were used, including slope, geology, land cover, rainfall, and drainage density. The resulting LSMs were classified into five susceptibility levels. The Fuzzy Gamma 0.9 model identified 9.15% of the area as highly susceptible, while the Fuzzy SUM model showed 20.79%. Validation using the Area Under the Curve (AUC) revealed that the Fuzzy Gamma 0.9 model (AUC = 0.80) performed better than AHP (0.711) and Fuzzy SUM (0.716). These results provide valuable insights for land use planning and hazard mitigation.
DOI: 10.6084/m9.figshare.29912265.v1
title: Comparable analysis of analytical hierarchy process and fuzzy logic methods based on GIS and remote sensing for anticipating landslide susceptibility in the Iraqi Kurdistan Region, Koya district

description: Landslides pose a serious threat in urban areas with steep and varied terrain, such as the Koya district in northern Iraq. This study aims to produce landslide susceptibility maps (LSMs) using remote sensing data and three models: Analytical Hierarchy Process (AHP), Fuzzy Logic SUM, and Fuzzy Gamma 0.9. A total of 280 landslides were identified through field surveys, GPS, and Google Earth, and used for model training and validation. Fifteen conditioning factors were used, including slope, geology, land cover, rainfall, and drainage density. The resulting LSMs were classified into five susceptibility levels. The Fuzzy Gamma 0.9 model identified 9.15% of the area as highly susceptible, while the Fuzzy SUM model showed 20.79%. Validation using the Area Under the Curve (AUC) revealed that the Fuzzy Gamma 0.9 model (AUC = 0.80) performed better than AHP (0.711) and Fuzzy SUM (0.716). These results provide valuable insights for land use planning and hazard mitigation.
DOI: 10.6084/m9.figshare.28089735
title: ‘Environmental vulnerability of a tropical river watershed, Eastern amazon’

description: The assessment of environmental vulnerability to soil erosion (EVSE) has been used to predict the probability of physical soil degradation. The aim of this study is to analyze the EVSE in the Itacaiúnas River Watershed (IRW) in eastern Amazonia. The methodological approach is based on Multicriteria Decision Analysis based on Geographic Information Systems (MCDA-GIS). It integrates geological, geomorphological, pedological, climatological and land cover characteristics to determine the EVSE. From the integrated map analysis, a final EVSE map was generated from arithmetic average of the five thematic maps. The results showed that the IRW is moderately stable/vulnerable (68% of the watershed), moderately stable (22%) and moderately vulnerable (10%). The land use in nonprotected areas promoted intense deforestation, generating areas of greater EVSE. The vulnerability maps represent effective products to assist in territorial planning, as they highlighted conflicts of use, enhancing areas with soil losses and their environmental impacts.
DOI: 10.6084/m9.figshare.28089735.v1
title: ‘Environmental vulnerability of a tropical river watershed, Eastern amazon’

description: The assessment of environmental vulnerability to soil erosion (EVSE) has been used to predict the probability of physical soil degradation. The aim of this study is to analyze the EVSE in the Itacaiúnas River Watershed (IRW) in eastern Amazonia. The methodological approach is based on Multicriteria Decision Analysis based on Geographic Information Systems (MCDA-GIS). It integrates geological, geomorphological, pedological, climatological and land cover characteristics to determine the EVSE. From the integrated map analysis, a final EVSE map was generated from arithmetic average of the five thematic maps. The results showed that the IRW is moderately stable/vulnerable (68% of the watershed), moderately stable (22%) and moderately vulnerable (10%). The land use in nonprotected areas promoted intense deforestation, generating areas of greater EVSE. The vulnerability maps represent effective products to assist in territorial planning, as they highlighted conflicts of use, enhancing areas with soil losses and their environmental impacts.