Geoscience Data Journal最新文献

{"title":"Terrestrial Ecosystem Detection Based on Deep Learning Framework and Satellite Image Geographical Information","authors":"Liang Dong,&nbsp;Fan Lei,&nbsp;Dongliang Huang,&nbsp;Yuye Gong,&nbsp;Jide Wei,&nbsp;Wenjing She","doi":"10.1002/gdj3.70058","DOIUrl":"https://doi.org/10.1002/gdj3.70058","url":null,"abstract":"<p>Detection of terrestrial ecosystems involves the identification and classification of land cover types and vegetation patterns using remote sensing and geospatial analysis. It provides assistance in environmental monitoring, the assessment of biodiversity, and sustainable land management, among others. The study given proposes a hybrid Deep Learning (DL) system that simultaneously incorporates ResNet-50 and LSTM for accurate classification of Land Use and Land Cover (LULC) in satellite images. The EuroSAT dataset, consisting of 27,000 labelled samples across 10 land-use classes, was used to train and evaluate the proposed model. This large, diverse dataset enables robust model development for accurate land-cover classification in satellite imagery. The sequence of activities involves advanced image pre-processing to enhance edge preservation and reduce noise, such as bilateral, guided, and median filtering. The next instance is feature extraction performed with HOG, which retains important edge and shape properties. The extracted Histogram of Oriented Gradients (HOG) features are given into the ResNet-50 backbone, where spatial representations at a higher level are derived. Then, those ResNet-50 based high-level features are fed into the LSTM layer to model temporal and sequential dependencies and then provide additional contextual information for spatial transitions. The last classification is done using a fully connected layer with a Softmax activation function that provides the class probabilities. The model gained higher performance with a precision of 0.9903, an accuracy of 0.9904, and recall of 0.9903, which showed its ability to differentiate efficiently between land cover types that are more similar. By integrating spatial and sequential modelling, the architecture proposed gives a very high degree of reliability and robustness in classification with regard to a variety of geographic and environmental conditions, rendering them well out for environmental monitoring, urban planning, and geospatial intelligence.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148281999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019","authors":"","doi":"10.1002/gdj3.70085","DOIUrl":"https://doi.org/10.1002/gdj3.70085","url":null,"abstract":"<p>Zhiwen, X., X. Chong, Y. Xie, et al. 2026. ‘Inventory of Landslides Triggered by Heavy Rainfall in Fuling District, Chongqing, China, June 2019’. <i>Geoscience Data Journal</i> 13, no. 2: e70062. https://doi.org/10.1002/gdj3.70062.</p><p>In the originally published version of this article, the author's names were presented incorrectly. The given names and surnames for authors <b>Zhiwen Xue</b> and <b>Chong Xu</b> have now been corrected in both the HTML and PDF versions of the article.</p><p>We apologize for this error.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70085","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Harmonized Landsat–Sentinel-2 Burned-Area Product for the Pantanal (2014–2024), Based on Multitemporal Composites","authors":"Liz B. C. Belém,&nbsp;Allan A. Pereira,&nbsp;Fabiano Morelli,&nbsp;Renata Libonati","doi":"10.1002/gdj3.70084","DOIUrl":"https://doi.org/10.1002/gdj3.70084","url":null,"abstract":"<p>Burned-area information is essential for documenting fire activity and supporting environmental management in dynamic ecosystems such as the Pantanal wetland. This study presents AQM-HLS, an 11-year burned-area dataset (2014–2024) derived from multisensor surface-reflectance imagery from Landsat-8/9 and Sentinel-2. The dataset is generated using multitemporal NBR-based compositing and an automated sampling procedure that integrates VIIRS active-fire detections with morphological filtering to produce consistent training samples for Random Forest classification. The classification achieved high performance, with overall accuracy above 99.6% and Dice and CSI values exceeding 0.99. Spectral diagnostics indicate a clear separation between burned and unburned surfaces, particularly in the NIR and SWIR regions. The resulting 30-m resolution annual and monthly maps capture the spatial and temporal patterns of fire occurrence across the Pantanal and provide a consistent time series suitable for climate, ecological and risk-management applications. The dataset is openly available and can support the intercomparison of burned-area products with lower spatial resolution, contribute to the development and improvement of algorithms based on medium- and low-resolution sensors, support cross-sensor calibration across different orbital platforms and inform regional studies on fire dynamics.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Wind Field Reconstruction in the Mesosphere and Lower Thermosphere (MLT) During Geomagnetic Storms by Fusing TIDI and SABER Data: A Generative Adversarial Network (GAN) Approach","authors":"Yunhao Si","doi":"10.1002/gdj3.70079","DOIUrl":"https://doi.org/10.1002/gdj3.70079","url":null,"abstract":"<p>The Mesosphere and Lower Thermosphere establish an essential connection between Earth's lower atmosphere and upper atmosphere because wind patterns essential for atmospheric movement also influence energy distribution and chemical movements during geomagnetic storms that disrupt satellite operations and space weather prediction systems. The existing reconstruction methods that use single-instrument data and physics-based models and statistical methods show two main flaws because they cannot capture all the spatial details and face varying levels of uncertainty at different altitudes, and they cannot model the complex weather patterns during storm events. The study presents an innovative intelligent generative reconstruction framework which combines TIMED Doppler Interferometer wind data with SABER thermal emission data through a Generative Adversarial Network structure that utilizes an attention-based spatial–channel feature fusion method which the Adam algorithm helps to improve. The attention module dynamically weights wind–temperature interactions to enhance spatial continuity and altitude-aware learning, while the Generative Adversarial Network generator reconstructs physically consistent three-dimensional wind vectors (U, V, W) and the discriminator enforces physical consistency. The research results based on 1390 fused samples, which included 973 training samples, 208 validation samples and 209 testing samples, achieved excellent prediction results. The study achieved R² values of 0.9889, 0.9891 and 0.9911 for the train, validation and test sets, respectively, while providing normalized RMSE values of 0.2321, 0.2343 and 0.2069 and MAE values of 0.1073, 0.1198 and 0.1111. The R² value between 0.98 and 0.99 stays intact during altitude-binned evaluation across the 60–110-km range, while the storm-phase analysis shows that geomagnetic disturbances lead to increased RMSE, yet the system maintains its fundamental reconstruction stability. The proposed framework delivers an expandable solution that utilizes data to create ongoing high-resolution worldwide Mesosphere and Lower Thermosphere wind reconstruction, which enables advanced space weather monitoring and upper atmospheric research.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148237529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood Hazard in Aotearoa New Zealand Under Current and Future Climates","authors":"A. Harang,&nbsp;E. M. Lane,&nbsp;C. Bosserelle,&nbsp;S. Dean,&nbsp;C. Cattoën,&nbsp;R. Pearson,&nbsp;T. Carey-Smith,&nbsp;R. Srinivasan,&nbsp;H. Shiona,&nbsp;M. Wilkins,&nbsp;G. Smart","doi":"10.1002/gdj3.70083","DOIUrl":"https://doi.org/10.1002/gdj3.70083","url":null,"abstract":"<p>Flooding is one of the costliest hazards facing Aotearoa New Zealand (ANZ). We present nationally consistent flood maps organised into four datasets corresponding to a range of current and future climate scenarios in Aotearoa New Zealand, along with the methodology used to create them. This was developed through the Endeavour project Mā te haumaru ō te wai: flood resilience for Aotearoa New Zealand (https://niwa.co.nz/hazards/ma-te-haumaru-o-nga-puna-wai-o-rakaihautu-ka-ora-mo-ake-tonu), funded by the Ministry of Business, Innovation &amp; Employment (MBIE). These flood maps were generated by a semi-automatic workflow linking a chain of models. For each catchment, rainfall corresponding to a 1% AEP (Annual Exceedance Probability) design storm event was routed downstream using hydrological modelling in the upper catchment and injected into a hydrodynamic model to produce fluvial-pluvial inundations maps. Tide was imposed on open sea boundaries and levees were enforced. An adaptable grid down to 4 m resolution was used and hazard layers of maximum water depth, maximum water elevation and maximum (depth times velocity) were generated. Flood hazard was first generated for actual climate conditions (or one degree warmer than pre-industrial global climate conditions). Using relations between global temperature increase and local extreme rainfall intensity, future climate flood maps were generated for scenarios corresponding to an increase of 1°C, 2°C and 3°C compared to actual current climate conditions (i.e., two, three and four degrees warmer than pre-industrial global temperature). These national maps can be used to reveal actual and future flood hazards for the country, especially in rural areas where flood hazard has never been assessed. They can also illustrate how flood-prone areas may evolve with climate change. They present a first consistent national flood assessment needed to inform policy. These maps were also used for a first consistent national flood risk assessment and are accessible on the Earth Sciences New Zealand (ESNZ) datahub under an attribution non-commercial licence.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observations of Electric Fields Sounding in the Mature Stage of a Thunderstorm","authors":"Xinglu Jiang,&nbsp;Tinglong Zhang,&nbsp;Zhe Li,&nbsp;Yunfeng Liu,&nbsp;Ying Wen,&nbsp;Lingjie Kong,&nbsp;Junming Gong","doi":"10.1002/gdj3.70081","DOIUrl":"https://doi.org/10.1002/gdj3.70081","url":null,"abstract":"<p>In the summer of 2021, observations of balloon-borne electric field within thunderstorms were conducted in Nanchang City, Jiangxi Province, China. On 24 August, two electric field profiles were obtained during the mature stage of a local thunderstorm in Nanchang City, Jiangxi Province, China. The initial sounding (S1) and subsequent sounding (S2) were initiated with a 6 min interval. However, S1 entered the weak echo region from the leading edge of the thunderstorm and entered the 30 dBZ region at an altitude of 6 km, while the second sounding (S2) entered from the cloud base and entered the strong echo region at approximately 0°C. At altitudes more than 6 km, the observations were synchronized (with a horizontal distance of 3–4 km). The results showed that two sounding balloons traversed a total of 8 and 11 charge regions for S1 and S2, respectively, and the predominant charge regions conformed to the tripole charge structure. The synchronized observations above 6 km indicated that the distribution of charge regions within the thunderstorm indeed exhibits horizontal inhomogeneity. In addition, an anomalous negative charge region was identified in the upper portion of the thunderstorm, with a depth ranging from 2.3 to 2.7 km, which is larger than the normal negative screening layer. It is notable that a pair of charges with negative upper and positive lower charges is observed at 8.5 and 10 km, which is difficult to explain reasonably using non-inductive charging mechanisms. This indicates the complexity of the formation of charge region within clouds.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digitized Stratigraphic and Geophysical Logs From Deep Boreholes in the 2022 Mw 5.5 Fano-Pesaro Earthquake Region, Adriatic Sea, Italy","authors":"Elham Safarzadeh,&nbsp;Maurizio Ercoli,&nbsp;Angela Bertinelli,&nbsp;Massimiliano Rinaldo Barchi","doi":"10.1002/gdj3.70077","DOIUrl":"https://doi.org/10.1002/gdj3.70077","url":null,"abstract":"<p>A clean, well-organized and comprehensive dataset developed in accordance with the FAIR principles (Findable, Accessible, Interoperable and Reusable), provides a solid foundation for a new research initiative and supports open science. In this context, also legacy, deep boreholes data represent a valuable and unrepeatable source of geological and geophysical information to recover. This article presents the digitalization of legacy well documentation originally available only as scanned images, resulting in a dataset of 30 files, derived by an accurate digitalization workflow carried out on exploration boreholes documentation. The latter was collected across the offshore area between Pesaro-Fano in the Adriatic Sea, a region that has experienced significant seismic activity, including the 9 November 2022 Mw 5.5 Fano-Pesaro earthquake sequence. Such data were originally gathered as scanned images extracted from vintage raster files (PDF format), publicly available on the Italian ViDEPI Project website (www.videpi.com). Despite their findability and accessibility are straightforward, their interoperability and reusability are severely limited, due to variable image quality, non-editable contents and obsolete stratigraphic nomenclature. Such wells are named from northwest to southeast as follows: Boheme 01, Tamara 01, Pesaro Mare 03, Pesaro Mare 04, Malachite 01, Cornelia 01 and Elga 01. The digitalization was carried out at high fidelity with respect to the original data, reassessing the relevant well stratigraphy by taking into account all the handwritten comments found inside the original images. Only minor reinterpretations based on lithology, depositional environment and age, were made. Some local formation names were updated, aligned and thus correlated with modern and officially recognized regional stratigraphic units. The digitalization also includes spontaneous potential, resistivity and sonic logs of the Tamara 01 and Boheme 01 wells. Such digitized data were successively stored in open and standard formats (CSV and LAS). Nowadays, these datasets are freely accessible and carry substantial significance as a foundation of earthquake studies, for enhancing geological and geophysical models, depicting the stratigraphic, structural and geophysical characteristics of the study area. Notable potential use includes, among others, seismotectonic studies, gas and CO₂ storage, basinal analysis, stratigraphic and paleogeographic investigations within the region. Digitizing such legacy data reduces the risk of data loss, improves modernization and accessibility, supports interoperability and collaboration. It also enables easier quality control and facilitates future reuse, especially when integrated with other data sources such as seismic surveys, production data and reservoir models.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70077","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148048733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148084482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148076659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ALERA-V Version 1.0: An Objective Analysis Dataset of the Venus Atmosphere","authors":"Yukiko Fujisawa,&nbsp;Shin-ya Murakami,&nbsp;Norihiko Sugimoto,&nbsp;Nobumasa Komori,&nbsp;Masahiro Takagi,&nbsp;Takeshi Imamura,&nbsp;Takeshi Horinouchi,&nbsp;George L. Hashimoto,&nbsp;Masaki Ishiwatari,&nbsp;Takeshi Enomoto,&nbsp;Takemasa Miyoshi,&nbsp;Hiroki Kashimura,&nbsp;Toshiki Matsushima,&nbsp;Yoshi-Yuki Hayashi","doi":"10.1002/gdj3.70080","DOIUrl":"https://doi.org/10.1002/gdj3.70080","url":null,"abstract":"<p>An objective analysis dataset of the Venus atmosphere, ALERA-V version 1.0, has been released. This objective analysis provides the best estimate of the state of the Venus atmosphere produced by combining observations and a forecast model weighted according to their respective levels of uncertainty. The accumulation of frequent observations by the Venus Climate Orbiter ‘Akatsuki’ has, for the first time, enabled an objective analysis of the Venus atmosphere. ALERA-V stands for AFES-LETKF experimental ensemble objective (re)analysis of the Venus atmosphere, and is generated by ALEDAS-V, the AFES-LETKF data assimilation system for the Venus atmosphere. ALEDAS-V uses AFES-Venus (Atmospheric General Circulation Model for the Earth Simulator for Venus) as a forecast model and the LETKF (Local Ensemble Transform Kalman Filter) for data assimilation. The observations of the zonal and meridional winds obtained by a cloud tracking technique from images taken by Akatsuki's UVI (Ultraviolet Imager) are assimilated to produce ALERA-V version 1.0. The dataset consists of atmospheric variables defined on a grid of 128 longitude and 64 latitude points, and 60 vertical levels, with output every 6 Earth hours from September to December 2018, including the intensive observation period of November. ALERA-V is expected to be useful for both scientific and engineering research, such as understanding the dynamical mechanisms of various atmospheric phenomena and planning for future satellite missions, with appropriate consideration of its quality and limitations.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"13 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148048162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}