GIScience & Remote Sensing最新文献

{"title":"Stage-integrated fire resilience assessment of forest vegetation and its driving factors","authors":"Sen Wang,Xingpeng Liu,Bing Ma,Rima Ga,Zhijun Tong,Jiquan Zhang","doi":"10.1080/15481603.2026.2742614","DOIUrl":"https://doi.org/10.1080/15481603.2026.2742614","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"19 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-task learning for simultaneous surface water and burned area mapping from earth observation data","authors":"Aiym Orynbaikyzy,Marc Wieland,Moritz Rösch,Michael Nolde,Sandro Martinis","doi":"10.1080/15481603.2026.2737497","DOIUrl":"https://doi.org/10.1080/15481603.2026.2737497","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"37 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global asymmetric mangrove-edge shifts reveal coastal squeeze under sea-level rise","authors":"Dongjie Fu,Xichen Xu,Fenzhen Su,Zhijun Dai,Vincent Lyne,Jiasheng Tang,Lu Kang,Hao Yu","doi":"10.1080/15481603.2026.2741774","DOIUrl":"https://doi.org/10.1080/15481603.2026.2741774","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"6 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for identifying urban flood inundation based on single-temporal remote sensing imagery","authors":"Chenglong Yao,Guoqiang Wang,Yinglan A.,Yuntao Wang,Jin Wu,Baolin Xue,Qiao Wang","doi":"10.1080/15481603.2026.2736379","DOIUrl":"https://doi.org/10.1080/15481603.2026.2736379","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"25 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149091318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing heat dome effects on forest dynamics in the Pacific Northwest using Planet imagery","authors":"Aji John, Kavya Pradhan, Michael J. Case, Ailene K. Ettinger, Janneke Hille RisLambers","doi":"10.1080/15481603.2026.2728824","DOIUrl":"https://doi.org/10.1080/15481603.2026.2728824","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"40 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thinking on maps: how foundation model agents explore, remember, and reason in map environments","authors":"Zhiwei Wei,Yuxing Liu,Hua Liao,Wenjia Xu","doi":"10.1080/15481603.2026.2730755","DOIUrl":"https://doi.org/10.1080/15481603.2026.2730755","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"59 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving automatic glacier mapping accuracy, robustness, and reliability across the Third Pole Region","authors":"Wei Jia, Ninglian Wang, Chenlie Shi, Yuwei Wu, Anan Chen, Gexia Qin, Bo Jiang","doi":"10.1080/15481603.2026.2737501","DOIUrl":"https://doi.org/10.1080/15481603.2026.2737501","url":null,"abstract":"","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"115 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A source–medium–receiver framework for modeling wind-driven vegetation cooling in urban landscapes","authors":"Hassan Qasim,Xiaoli Ding,Sawaid Abbas","doi":"10.1080/15481603.2026.2733792","DOIUrl":"https://doi.org/10.1080/15481603.2026.2733792","url":null,"abstract":"Vegetation cooling in cities is often represented as a local, isotropic effect, although atmospheric processes can redistribute cooling beyond individual vegetation patches. This simplification may overlook the nonlocal and directionally structured nature of cooling propagation. We developed a physics-guided source–medium–receiver (SMR) framework for the Greater Bay Area of China to compare multi-source isotropic connectivity with prevailing-wind-conditioned connectivity. Vegetation-source strength was represented by the VCSI, and parameters estimated from 2021 to 2023 were frozen for an independent 2024 holdout and contrasting monsoon assessments. Cooling connectivity was modeled using a spatially varying distance-decay function, controlled by the wind speed, moisture content, surface albedo, terrain, and directional constraints from prevailing winds. In 2024, isotropic connectivity showed a stronger relationship with LST (r = −0.8069) than wind-conditioned connectivity (r = −0.7780) and local VCSI (r = −0.7757), with the same general ordering across monsoon and built-up receiver assessments. The source-level decay lengths ranged from 0.504 to 0.518 km, whereas the wind-conditioned emergent multi-source persistence increased from 4.13 to 4.88 km, reflecting cumulative field behaviors. Wind-relative decomposition further showed that annual connectivity was distributed across downwind, crosswind, and upwind pathways. These findings indicate that regional vegetation cooling is primarily characterized by distance-mediated, multi-source connectivity, while prevailing-wind direction acts as a scale- and condition-dependent modifier. This shift in perspective provides a physically grounded framework for effective urban climate analysis and precision greening strategies.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"58 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global daily joint reconstruction of TROPOMI tropospheric No 2 columns and averaging kernels with temporally aware deep learning","authors":"Chang Wang,Kai Cheng,Xingcheng Lu","doi":"10.1080/15481603.2026.2732650","DOIUrl":"https://doi.org/10.1080/15481603.2026.2732650","url":null,"abstract":"Achieving spatiotemporal consistency in comparisons between satellite tropospheric nitrogen dioxide (NO2) retrievals and numerical models or profile-resolving observations depends on the availability of both seamless NO2 vertical columns and their corresponding averaging kernels (AKs). However, cloud cover, aerosols, and solar-zenith-angle constraints frequently invalidate TROPOMI retrievals. Because existing reconstruction studies typically provide NO2 columns in isolation, AK-weighted analyses remain restricted to the original, fragmented observation footprints. Here we develop a temporally aware deep learning framework that jointly reconstructs TROPOMI tropospheric NO2 columns and 34-layer AKs over global land at 0.05° daily resolution from May 2018 to December 2024, with the resulting gap-filled field representing a clear-view, retrieval-consistent column and providing a lower-bound representation under persistent cloud. Because static-feature ML baselines do not explicitly represent photochemical-lifetime, boundary-layer, and transport processes, we introduce a CNN-Temporal-Attention architecture that explicitly encodes 24-hour pre-overpass meteorological dependencies. For AK, a retrieval-specific operator that lacks Vertical Column Density (VCD)-equivalent spatial priors, we exploit inter-layer continuity in vertical sensitivity through a layer-preserving 1D-CNN. Both models achieve ΔR2 ≤ 0.02 across random, temporal, and spatial-block cross-validation, with the AK model reaching a pooled R2 of 0.96; attribution patterns are consistent with the seasonal variation of the NOx photochemical lifetime, and the learned temporal weighting is consistent with cross-day boundary-layer memory, a bimodal boundary-layer cycling, and a ~2-hour upwind transport-related temporal association. Independent validation against 141 Pandora and 29 MAX-DOAS stations supports daily-to-annual accuracy, while AK-smoothed MAX-DOAS and aircraft spiral-profile comparisons support reconstructed AK physical consistency. Reconstructed fields preserve emission-control trends, COVID-19 responses, and biomass-burning signals. Co-delivering VCDs, layer-resolved AKs, and per-pixel uncertainties enables rigorous AK-weighted analysis beyond the original TROPOMI footprint.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"1 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Sar oil-spill segmentation for marine monitoring Via self-supervised denoising and lightweight wavelet–state-space modeling","authors":"Yiheng Xie,Xiaoping Rui,Yarong Zou,Heng Tang,Ninglei Ouyang,Hongyue Zhang,Jiayu Ge","doi":"10.1080/15481603.2026.2727781","DOIUrl":"https://doi.org/10.1080/15481603.2026.2727781","url":null,"abstract":"Reliable SAR oil-spill segmentation must simultaneously address image degradation caused by speckle and semantic confusion caused by look-alike dark features, such as low-wind areas, biofilms, and oceanic internal waves. To this end, this paper proposes the OS-AdaAttReNet-WSSNet framework, which decomposes the task into two complementary stages: self-supervised image restoration and supervised oil-spill segmentation. In the first stage, a self-supervised denoising network, AdaAttReNet, is constructed. Without requiring clean reference images, it integrates rotation-equivariant features, channel-spatial attention, and adaptive residual reconstruction to alleviate the effects of speckle on image texture, weak boundaries, and elongated structures. In the second stage, a lightweight wavelet-state-space segmentation network family, WSSNet, is developed. Multi-Scale Split Attention (MSSAtt), the Dynamic Enhanced Residual Wavelet Encoder (DE-RWE), and the Mamba-Enhanced Multi-scale Context Aggregation Upsampler (MEMCAU) are employed for multi-scale feature enhancement, wavelet-domain structural encoding, and long-range contextual fusion, respectively, thereby improving the recognition and boundary localization of irregular oil slicks. On the PALSAR and Sentinel subsets of the SOS dataset, the proposed framework achieves comparable or higher average segmentation performance than recent oil-spill-specific segmentation models. In particular, WSSNet-Tiny, with only 0.27 M parameters, substantially reduces model size and computational cost while maintaining competitive segmentation performance, demonstrating a favorable accuracy-efficiency trade-off. In addition, experiments on six external data subsets show that the WSSNet series exhibits a certain degree of direct transferability without using target-domain data for training or adaptation. The proposed framework provides a solution for reliable and efficient SAR oil-spill monitoring that balances segmentation accuracy, computational efficiency, and transferability.","PeriodicalId":55091,"journal":{"name":"GIScience & Remote Sensing","volume":"157 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148993931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}