Environmental Modelling & Software最新文献_第10页

A novel salient object detection network for burned area segmentation in high-resolution remote sensing images 一种新的高分辨率遥感图像烧伤区域分割显著目标检测网络

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-19 DOI: 10.1016/j.envsoft.2025.106629

Yuxiang Fu, Wei Fang

{"title":"A novel salient object detection network for burned area segmentation in high-resolution remote sensing images","authors":"Yuxiang Fu, Wei Fang","doi":"10.1016/j.envsoft.2025.106629","DOIUrl":"10.1016/j.envsoft.2025.106629","url":null,"abstract":"<div><div>Burned area segmentation (BAS) in remote sensing images (RSIs) is critical for forest fire monitoring, as it helps locate and extract damaged areas, providing a scientific basis for post-disaster recovery. However, existing BAS methods underperform on high-resolution RSIs due to diluted location information and blurred edges during sampling. To this, we propose PANet, a novel salient object detection (SOD) network designed for BAS in high-resolution RSIs. PANet introduces two key modules: Path Aggregation Decoder (PAD) and Progressive Multi-level Aggregation Predictor (PMAP). PAD integrates multi-level features for richer semantics, using detail feature flow to enhance edge quality and refined location feature flow to improve spatial accuracy. PMAP progressively fuses features from PAD to predict saliency maps, leveraging higher-level features to complement lower-level ones. We also constructed a new dataset for high-resolution BAS. Experiments on two BAS datasets show that PANet outperforms state-of-the-art methods. Code is available at: <span><span>https://github.com/Voruarn/PANet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106629"},"PeriodicalIF":4.8,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Agent-Based Modelling of food systems: A scoping review on incorporation of behavioural insights 基于主体的粮食系统建模：对纳入行为见解的范围审查

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-18 DOI: 10.1016/j.envsoft.2025.106617

Alexander Öttl, Mette Termansen

引用次数: 0

Monitoring carbon dynamics and driving forces in arid and semi-arid river basin: A case study of China's West Liao river Basin 干旱区半干旱区河流流域碳动态及驱动力监测——以中国西辽河流域为例

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-15 DOI: 10.1016/j.envsoft.2025.106625

Yuxia Hu , Jirui Gong , Siqi Zhang , Weiyuan Zhang , Xuede Dong , Guisen Yang , Chenyi Yan , Yingying Liu

{"title":"Monitoring carbon dynamics and driving forces in arid and semi-arid river basin: A case study of China's West Liao river Basin","authors":"Yuxia Hu , Jirui Gong , Siqi Zhang , Weiyuan Zhang , Xuede Dong , Guisen Yang , Chenyi Yan , Yingying Liu","doi":"10.1016/j.envsoft.2025.106625","DOIUrl":"10.1016/j.envsoft.2025.106625","url":null,"abstract":"<div><div>Accurate estimation of ecosystem carbon stocks and their dynamics and exploration of their drivers will contribute to the sustainable management of carbon neutrality. We combined field measurements in China's West Liao River Basin with the improved Terrestrial Ecosystem Regional (TECO-R) model to quantify the carbon pools and carbon turnover time from 2000 to 2021. Ecosystem carbon density averaged 3.65 kg C m<sup>−2</sup>; soil carbon pools accounted for about 80 %. Forests are major contributors to carbon sinks. The turnover times in the leaf, root, and soil carbon pools and the whole ecosystem were 0.54, 5.26, 42.76, and 23.31 years, respectively, depended on elevation and vegetation type. Although warming and increased precipitation promoted vegetation carbon accumulation, this increase was offset by a more substantial loss of soil organic carbon, the entire ecosystem (e<sub>c</sub>) decreased at an average rate of 84.21 g C m<sup>−2</sup> yr<sup>−1</sup>, mainly due to enhanced soil carbon loss. Precipitation significantly affects the e<sub>c</sub> in arid and semi-arid river basin; human activities and the natural environment also directly or indirectly influenced the evolution of e<sub>c</sub>. Our hybrid modeling framework enables spatially explicit assessment of carbon dynamics and provides a transferable approach for management, and guide nature-based climate solutions in fragile ecosystems.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106625"},"PeriodicalIF":4.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unravelling spatiotemporal patterns of event-based surface rainfall-runoff response using a cellular automata approach 利用元胞自动机方法揭示基于事件的地表降雨径流响应的时空模式

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-15 DOI: 10.1016/j.envsoft.2025.106623

Lidan Zhang , Yuming Wang , Xiaohong Chen

{"title":"Unravelling spatiotemporal patterns of event-based surface rainfall-runoff response using a cellular automata approach","authors":"Lidan Zhang , Yuming Wang , Xiaohong Chen","doi":"10.1016/j.envsoft.2025.106623","DOIUrl":"10.1016/j.envsoft.2025.106623","url":null,"abstract":"<div><div>Understanding surface water flow is critical for hydrological modeling and water resource management. Distributed hydrological models capture spatial heterogeneity in surface runoff, yet their full potential, especially in simulating surface flow complexities, requires further exploration. To bridge this gap, we developed the Surface Rainfall-Runoff Cellular Automata (SRRCA) model, a distributed hydrological framework featuring a Local-Finer Iteration (LFI) strategy to mitigate evolution errors from varying iteration steps. Implemented in a watershed in Wharfedale, England, the SRRCA model leverages multi-scale capabilities to resolve catchment-scale runoff dynamics and grid-scale flow interactions. Results indicated significant infiltration fluctuations in early rainfall due to spatial heterogeneity, alongside a strong correlation between peak flow and catchment size. Cells near confluence points exhibit delayed peaks, highlighting the influence of spatial position on runoff. This study systematically evaluates the strengths and limitations of cellular automata in hydrological modeling, and introduces a novel paradigm for investigating spatial heterogeneity in hydrology.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106623"},"PeriodicalIF":4.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VegRecoverAI: A deep learning-based system for automated vegetation recovery assessment and prediction with demonstration case study on gas pipeline construction VegRecoverAI：基于深度学习的自动植被恢复评估和预测系统，并以天然气管道建设为例进行了示范研究

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-15 DOI: 10.1016/j.envsoft.2025.106601

Alessandro Galdelli , Simone Pesaresi , Giacomo Quattrini , Roberto Pierdicca , Amal Benson Thaliath , Adriano Mancini

{"title":"VegRecoverAI: A deep learning-based system for automated vegetation recovery assessment and prediction with demonstration case study on gas pipeline construction","authors":"Alessandro Galdelli , Simone Pesaresi , Giacomo Quattrini , Roberto Pierdicca , Amal Benson Thaliath , Adriano Mancini","doi":"10.1016/j.envsoft.2025.106601","DOIUrl":"10.1016/j.envsoft.2025.106601","url":null,"abstract":"<div><div>Vegetation restoration is crucial for environmental conservation and maintaining ecosystem services. Traditional methods, such as manual inspections and expert photo interpretation, have been widely used to assess vegetation recovery but are labor-intensive, time-consuming, and prone to human bias. In contrast, modern Artificial Intelligence (AI) based methods use satellite imagery for efficient vegetation analysis, enabling large-scale monitoring with minimal human effort. This paper introduces VegRecoverAI, a comprehensive system that leverages multisource satellite data from Landsat, Sentinel-2, and PlanetScope. VegRecoverAI autonomously detects both subtle and significant vegetation changes, providing a reliable alternative to manual assessment. The system extracts NDVI time series data, detects vegetation change and uses an ensemble of forecasting models to predict future vegetation restoration. The system is demonstrated as a case study following gas pipeline construction in Italy. The results indicate that VegRecoverAI is automated and a scalable solution complementary to traditional techniques to support proactive environmental management.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106601"},"PeriodicalIF":4.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FloodSformer: A transformer-based data-driven model for predicting the 2-D dynamics of fluvial floods flooddsformer：一个基于变压器的数据驱动模型，用于预测河流洪水的二维动态

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-15 DOI: 10.1016/j.envsoft.2025.106599

Matteo Pianforini , Susanna Dazzi , Andrea Pilzer , Renato Vacondio

引用次数: 0

Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem) 通过gui支持的R脚本，引入“miniRECgap”R包，用于通过经典非线性环境响应函数对缺失的涡流相关CO2通量测量进行简单的间隙填充（案例研究：在“具有挑战性的”泥炭地生态系统中使用“miniRECgap”与MDS和优化的浅神经网络进行样本间隙填充）

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-14 DOI: 10.1016/j.envsoft.2025.106611

Alina Premrov , Jagadeesh Yeluripati , Richard Slevin , Adam Bates , Magdalena Matysek , Stephen Barry , Kenneth A. Byrne , Rowan Fealy , Bernard Hyde , Gary Lanigan , Mark McCorry , Rachael Murphy , Florence Renou-Wilson , Amey Tilak , David Wilson , Matthew Saunders

{"title":"Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem)","authors":"Alina Premrov , Jagadeesh Yeluripati , Richard Slevin , Adam Bates , Magdalena Matysek , Stephen Barry , Kenneth A. Byrne , Rowan Fealy , Bernard Hyde , Gary Lanigan , Mark McCorry , Rachael Murphy , Florence Renou-Wilson , Amey Tilak , David Wilson , Matthew Saunders","doi":"10.1016/j.envsoft.2025.106611","DOIUrl":"10.1016/j.envsoft.2025.106611","url":null,"abstract":"<div><div>Numerous tools/software exist to gap-fill missing eddy covariance (EC) data, with varying performance depending on study-site dynamics. Disturbed ecosystems like former cutaway-peatlands may be challenging for gap-filling. Researchers using gap-filling spreadsheets may benefit from transitioning to R, but may face challenges if they lack programming skills. To address these, we introduce ‘miniRECgap’, a user-friendly tool in R for effortless gap-filling of <span>EC</span> carbon dioxide flux data using well-known temperature- and light-response functions. ‘miniRECgap’ can model net ecosystem exchange (NEE) via GUI-supported scripts with only five code-lines and minimal inputs. A case-study on one ‘classic’ (forest) and one ‘challenging’ (rehabilitating cutaway-peatland) ecosystem indicated that standard gap-filling (MDS) performed better for the ‘classic’, but not for the ‘challenging’ ecosystem (MDS R<sup>2</sup> = 0.24; ‘miniRECgap’ R<sup>2</sup> = 0.57). For the rehabilitating-peatland, an optimised shallow Artificial Neural Network outperformed other two approaches (R<sup>2</sup> = 0.68). These findings demonstrate the importance of NEE gap-filling for assessing ecosystem-level carbon-dynamics, important for rehabilitating-peatlands.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106611"},"PeriodicalIF":4.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A mangrove lifecycle ecosystem analysis and forecasting (LEAF) model 红树林生命周期生态系统分析与预测（LEAF）模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-12 DOI: 10.1016/j.envsoft.2025.106619

Thomas Dunlop , Stefan Felder , William Glamore

引用次数: 0

VIC-Res Mekong: An open-source hydrological-water management model for the Mekong River basin VIC-Res湄公河：一个开放源代码的湄公河流域水文水资源管理模型

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-12 DOI: 10.1016/j.envsoft.2025.106603

Hisham Eldardiry , Shanti Shwarup Mahto , Simone Fatichi , Stefano Galelli

{"title":"VIC-Res Mekong: An open-source hydrological-water management model for the Mekong River basin","authors":"Hisham Eldardiry , Shanti Shwarup Mahto , Simone Fatichi , Stefano Galelli","doi":"10.1016/j.envsoft.2025.106603","DOIUrl":"10.1016/j.envsoft.2025.106603","url":null,"abstract":"<div><div>Decades of research on the Mekong River basin have vastly expanded our knowledge of key hydrological and ecological processes, in part thanks to the development of modelling tools. However, few of these models are openly available to the research community or adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles, particularly in the domain of hydrology, in turn limiting transparency, reproducibility, and broader scientific engagement. Here, we address this gap and introduce VIC-Res Mekong, an open-source hydrological-water management model for the Mekong River basin. The model is implemented over a spatial domain of <span><math><mo>∼</mo></math></span>630,000 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> and has a resolution of 0.0625 degrees. Water and energy budgets are simulated with the Variable Infiltration Capacity (VIC) model, while streamflow routing is simulated with VIC-Res, which explicitly accounts for the storage and release dynamics of reservoirs. This is of key importance for a basin like the Mekong, where the active storage capacity of dams has increased from 20 km<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span> to nearly 80 km<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span> in the past fifteen years. To this purpose, VIC-Res Mekong integrates information from a database containing reservoir storage time series (inferred from satellite images) for 129 dams that accounts for more than 90% of total storage capacity. These data support both hindcast simulations and the derivation of reservoir operating rules, enabling a flexible and realistic representation of dam operations that allows to characterize in detail dam-induced hydrological alterations.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106603"},"PeriodicalIF":4.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ECOBRIDGE an expert system for spatial downscaling of land use/land cover change scenario outputs 土地利用/土地覆盖变化情景输出的空间降尺度专家系统

IF 4.8 2区环境科学与生态学

Environmental Modelling & Software Pub Date : 2025-07-11 DOI: 10.1016/j.envsoft.2025.106616

Josep Serra Gallego , Hollie Blaydes , Emma Gardner , Richard F. Pywell , J. Duncan Whyatt , John W. Redhead

{"title":"ECOBRIDGE an expert system for spatial downscaling of land use/land cover change scenario outputs","authors":"Josep Serra Gallego , Hollie Blaydes , Emma Gardner , Richard F. Pywell , J. Duncan Whyatt , John W. Redhead","doi":"10.1016/j.envsoft.2025.106616","DOIUrl":"10.1016/j.envsoft.2025.106616","url":null,"abstract":"<div><div>Present day mapping captures fine land cover/land use (LC/LU) details, but future/alterative LC/LU scenarios are typically constructed at coarser spatial resolution, hindering comparisons.</div><div>ECOBRIDGE (<strong>Eco</strong>logy and <strong>B</strong>iodive<strong>r</strong>sity <strong>I</strong>ntegrated <strong>D</strong>ownscale <strong>Ge</strong>neration) is an open, knowledge-based ArcGIS Pro workflow, to produce high-resolution LC/LU maps from coarser sources. ECOBRIDGE draws on specialist knowledge to parse a low-resolution baseline and scenario, a higher-resolution baseline and information defining LC/LU change, to generate high-resolution spatial data for the scenario.</div><div>These outputs are produced in the form of two datasets: as a raw pixel map and as an intelligent mapping layout which considers the structure of the landscape. The datasets created by ECOBRIDGE can contribute to more detailed analysis, bridging the gap between low-resolution datasets and more precise high-resolution information.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"193 ","pages":"Article 106616"},"PeriodicalIF":4.8,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144613134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0