Monitoring the Landscape Pattern Dynamics and Driving Forces in Dongting Lake Wetland in China Based on Landsat Images DOI Open Access
Mengshen Guo,

Nianqing Zhou,

Yi Cai

и другие.

Water, Год журнала: 2024, Номер 16(9), С. 1273 - 1273

Опубликована: Апрель 29, 2024

Dongting Lake wetland is a typical lake in the Middle and Lower Yangtze River Plain China. Due to influence of natural human activities, landscape pattern has changed significantly. This study used 12 Landsat images from 1991 2022 applied three common classification methods (support vector machine, maximum likelihood, CART decision tree) extract classify information, with latter having superior annual accuracy over 90%. Based on tree results, dynamic characteristics spatial patterns were analyzed through index, degree model, transition matrix model. Redundancy grey correlation analysis employed investigate driving factors. The results showed increased fragmentation, reduced heterogeneity, complexity 2022. water mudflat areas exhibited distinct stages: gradual decline until 2001 (−3.06 km2/a); sharp decrease 2014 (−19.44 steady increase (22.93 km2/a). Vegetation conversion, particularly between sedge reed, dominated change pattern. Reed area initially (18.88 km2/a), then decreased (−35.89 while opposite trend. Woodland fluctuated, peaking 2016 declined by construction Three Gorges Dam significantly altered dynamics level changes, reflected 4.03% comprehensive during 2001–2004. Potential evaporation also emerged as significant factor, exhibiting negative index. During 1991–2001 2004–2022, explanatory rates temperature, precipitation, potential evaporation, 88.56% 52.44%, respectively. Other factors like policies socio-economic played crucial role change. These findings offer valuable insights into evolution mechanisms wetland.

Язык: Английский

Wetland habitats supporting waterbird diversity: Conservation perspective on biodiversity-ecosystem functioning relationship DOI
Jie Qiu, Yixin Zhang,

Jianwu Ma

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 357, С. 120663 - 120663

Опубликована: Март 28, 2024

Язык: Английский

Процитировано

21
Probabilistic coastal wetland mapping with integration of optical, SAR and hydro-geomorphic data through stacking ensemble machine learning model DOI
Pankaj Prasad, Victor J. Loveson, Mahender Kotha

и другие.

Ecological Informatics, Год журнала: 2023, Номер 77, С. 102273 - 102273

Опубликована: Авг. 20, 2023

Язык: Английский

Процитировано

17
Spatiotemporal evolution and driving factors of tourism ecological adaptation in the Dongting Lake Area, China DOI Creative Commons

Xinbin Wang,

Jianxin Xiong,

Jing Wang

и другие.

Ecological Informatics, Год журнала: 2024, Номер 80, С. 102459 - 102459

Опубликована: Янв. 8, 2024

Tourism ecological adaptation (TEA) offers a novel research framework and practical tool for analyzing sustainable regional development. This facilitates high-quality tourism development, safeguards the ecosystem, enhances risk resilience. However, existing TEA has shortcomings regarding methodology scales. study constructed index system to address these deficiencies. The comprises two subsystems, industry (TIA) environment (EEA), including three dimensions: sensitivity, stability, response. entropy-weighted TOPSIS method, assessment model, standard deviation ellipse, geographic detector were used analyze spatiotemporal evolution driving factors of in Dongting Lake area, China, from 2012 2021. TIA displays rise-fall-rise pattern, whereas EEA demonstrates fluctuating upward trend. Additionally, exhibits distinctive basin-type spatial distribution with lower values central region higher surrounding areas. Over past decade, ellipses adaptability have shown minimal changes shape, position, center. Both clustering center positions reached state basic equilibrium. predominant type was characterized by low adaptation, 41.18% counties. is driven resource endowment, government regulatory efforts, level economic environmental governance capacity

Язык: Английский

Процитировано

8
Spatiotemporal evolution for early warning of ecological carrying capacity during the urbanization process in the Dongting Lake area, China DOI

Jianxin Xiong,

Xinbin Wang,

Di Zhao

и другие.

Ecological Informatics, Год журнала: 2023, Номер 75, С. 102071 - 102071

Опубликована: Март 24, 2023

Язык: Английский

Процитировано

16
OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images DOI Creative Commons
Houcai Guo, Dingqi Ye, Hanzeyu Xu

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 304, С. 114046 - 114046

Опубликована: Фев. 13, 2024

Spatiotemporal fusion aims to improve both the spatial and temporal resolution of remote sensing images, thus facilitating time-series analysis at a fine scale. However, there are several important issues that limit application current spatiotemporal methods. First, most methods based on pixel-level computation, which neglects valuable shape information ground objects. Moreover, many existing cannot accurately retrieve strong changes between available high-resolution image base date predicted one. This study proposes an Object-Based Spatial Unmixing Model (OBSUM), incorporates object-based unmixing, overcome two abovementioned problems. OBSUM consists one preprocessing step three steps, i.e., object-level residual compensation, compensation. The performance was compared with seven representative agricultural sites. experimental results demonstrated outperformed other in terms accuracy indices visual effects over time-series. Furthermore, also achieved satisfactory crop progress monitoring mapping. Therefore, it has great potential generate accurate observations for supporting various applications.

Язык: Английский

Процитировано

5
Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards DOI Creative Commons
Zhewei Liu, Chenyue Liu, Ali Mostafavi

и другие.

Environmental Science & Technology, Год журнала: 2023, Номер 57(41), С. 15511 - 15522

Опубликована: Окт. 4, 2023

Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals' exposures due to activities outside home neighborhood and underestimating peoples' overall exposures. To address this limitation, study proposes a novel mobility-based index for the evaluation. Using large-scale human mobility data, we quantify extent of population dwell time in high places 239 US counties three hazards. We explore how extends reach hazards leads emergence latent populations living high-hazard areas. Notably, neglect can lead over 10% underestimation The interplay spatial clustering regions movement trends creates "environmental traps." Poor ethnic minority residents disproportionately face multiple types This data-driven evidence supports severity these injustices. also studied arising from visits residents' areas, revealing millions having 5 daily occur high-exposure zones. Despite perceived safe could expose different These findings provide crucial insights targeted policies mitigate severe

Язык: Английский

Процитировано

12
Monitoring of wetland cover changes in protected areas to trade-offs between ecological conservation and food security: A case study from the Dongting Lake, China DOI
Huanhua Peng,

Haonan Xia,

Qian Shi

и другие.

Ecological Informatics, Год журнала: 2023, Номер 78, С. 102338 - 102338

Опубликована: Окт. 15, 2023

Язык: Английский

Процитировано

12
Exploring the feasibility of GF1-WFV data in estimating SPAD using spatiotemporal fusion algorithms DOI Creative Commons

Zeng An-jun,

Jianli Ding,

Jinjie Wang

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103035 - 103035

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images DOI Creative Commons

Zilong Lian,

Yulin Zhan,

Wenhao Zhang

и другие.

Sensors, Год журнала: 2025, Номер 25(4), С. 1093 - 1093

Опубликована: Фев. 12, 2025

Remote sensing images captured by satellites play a critical role in Earth observation (EO). With the advancement of satellite technology, number and variety remote have increased, which provide abundant data for precise environmental monitoring effective resource management. However, existing imagery often faces trade-off between spatial temporal resolutions. It is challenging single to simultaneously capture with high Consequently, spatiotemporal fusion techniques, integrate from different sensors, garnered significant attention. Over past decade, research on has achieved remarkable progress. Nevertheless, traditional methods encounter difficulties when dealing complicated scenarios. development computer science, deep learning models, such as convolutional neural networks (CNNs), generative adversarial (GANs), Transformers, diffusion recently been introduced into field fusion, resulting efficient accurate algorithms. These algorithms exhibit various strengths limitations, require further analysis comparison. Therefore, this paper reviews literature learning-based methods, analyzes compares algorithms, summarizes current challenges field, proposes possible directions future studies.

Язык: Английский

Процитировано

0
Assessment of the Impact of Extreme Hydrological Conditions on Migratory Bird Habitats of the Largest Freshwater Lake Wetlands in China Based on Multi-Source Remote Sensing Fusion Approach DOI Open Access

Jingfeng Qiu,

Yu Li, Xinggen Liu

и другие.

Sustainability, Год журнала: 2025, Номер 17(5), С. 1900 - 1900

Опубликована: Фев. 24, 2025

Poyang Lake, the largest freshwater lake of China, serves as a crucial wintering site for migratory birds in East Asian–Australasian Flyway, where habitat quality is essential maintaining diverse bird populations. Recently, frequent alternation extreme wet years, e.g., 2020, and dry 2022, have inflicted considerable perturbation on local wetland ecology, severely impacting avian habitats. This study employed spatiotemporal fusion method (ESTARFM) to obtain continuous imagery Lake National Nature Reserve during seasons from 2020 2022. Habitat areas were identified based classification water depth constraints. The results indicate that both conditions exacerbated fragmentation shallow habitats showed minor short-term fluctuations response levels but more significantly affected by long-term hydrological trends. These exhibited interannual variability across different affecting their proportion within overall distribution area. demonstrates ability ESTARFM reveal dynamic changes responses conditions, highlighting critical role analysis. outcomes this improve understanding impact habitats, which may help expand knowledge about protection other floodplain wetlands around world.

Язык: Английский

Процитировано

0