Disaster and Environment Monitoring Based on Multisource Remote Sensing Images

Topic Information

Dear Colleagues,

Due to the impacts of climate and environmental change, the frequency, intensity, and scope of extreme weather events have increased, leading to ecological vulnerability and frequent disasters such as earthquakes and floods in various regions. Ecosystems directly contribute to social and economic development by providing tangible material products and intangible services for human beings. However, the ecological environment has experienced a trend of deterioration under the combination of global warming and human activities. Implementing ecological environment assessments and clarifying the causes and mechanisms of disasters can provide a scientific basis and data support for eco-environmental protection measures and disaster reduction. With a focus on multisource remote sensing and social sensing data, this Topic aims to collect articles providing approaches, methods, and tools for assessing and revealing the changing trends and characteristics of ecological vulnerability and multiple types of disasters. Moreover, this Topic is devoted to promoting advances in understanding and modeling the relationships between global change and regional ecological vulnerability, accurately identifying vulnerability characteristics, exploring disturbance mechanisms, and examining the impact of key vulnerability elements and their interrelationships with disaster risk. We invite you to submit original research articles or review articles on topics related to ecological vulnerability assessment methods, disaster monitoring, or risk assessment utilizing multisource remote sensing images. Articles can describe innovative technological developments; introduce experiments, numerical modeling, case studies, or analytical research; or evaluate the future prospects of emerging technological solutions and propose suggestions for potential approaches.

Prof. Dr. Bing Guo
Dr. Yuefeng Lu
Dr. Yingqiang Song
Dr. Rui Zhang
Dr. Huihui Zhao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800	Submit
Land land	3.2	4.9	2012	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (5 papers)

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All Geosciences Land Remote Sensing Sustainability

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24 pages, 6145 KiB  

Open AccessArticle

Flood Mapping and Assessment of Crop Damage Based on Multi-Source Remote Sensing: A Case Study of the “7.27” Rainstorm in Hebei Province, China

by Chenhao Wen, Zhongchang Sun, Hongwei Li, Youmei Han, Dinoo Gunasekera, Yu Chen, Hongsheng Zhang and Xiayu Zhao

Remote Sens. 2025, 17(5), 904; https://doi.org/10.3390/rs17050904 - 4 Mar 2025

Abstract

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters [...] Read more.

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. Full article

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

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14 pages, 11822 KiB  

Open AccessArticle

Surface Micro-Relief Evolution in Southeast Tibet Based on InSAR Technology

by Gesangzhuoma, Zitong Han, Liang Cheng, Zhouyuqian Jiang and Qun’ou Jiang

Land 2025, 14(3), 503; https://doi.org/10.3390/land14030503 - 28 Feb 2025

Viewed by 145

Abstract

Based on 143 Sentinel-1A images from January 2020 to December 2022, this study used SBAS-InSAR technology to monitor the surface deformation of the tailings pond and analyzed and predicted the surface deformation laws in southeast Tibet. Overall, the surface deformation of the tailings [...] Read more.

Based on 143 Sentinel-1A images from January 2020 to December 2022, this study used SBAS-InSAR technology to monitor the surface deformation of the tailings pond and analyzed and predicted the surface deformation laws in southeast Tibet. Overall, the surface deformation of the tailings pond was significant and there were many areas where the deformation was uneven. The typical subsidence areas were mainly located in the northern part of the right tailings pond and the southern part of the left tailings pond. From a temporal perspective, the subsidence in the tailings pond showed a certain periodic downward fluctuation. Specifically, cumulative subsidence from January to September each year displayed a clear downward trend, reaching its maximum around September. This was followed by a slight uplift in October and November, after which a notable downward trend resumed from December until the following September. Based on spatial scale analysis, the changes in the tailings pond were relatively stable before May 2020. After that, the northern part of the right tailings pond showed a sinking trend, while the southern part exhibited uplift, and the central part remained relatively stable. Conversely, the southeastern part of the left tailings pond showed an uplifting trend, while the northern, central, and western parts experienced subsidence. Based on the Holt–Winters exponential smoothing model, we predicted the cumulative subsidence for 10 monitoring points in 2023. The northern part of the right tailings pond is expected to continue showing a significant subsidence trend in 2023. A prominent subsidence center is projected to emerge in the central part of the left tailings pond, and we should strengthen monitoring to avoid the disaster risk in the mining area. Full article

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

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26 pages, 5624 KiB  

Open AccessArticle

Combining Global Features and Local Interoperability Optimization Method for Extracting and Connecting Fine Rivers

by Jian Xu, Xianjun Gao, Zaiai Wang, Guozhong Li, Hualong Luan, Xuejun Cheng, Shiming Yao, Lihua Wang, Sunan Shi, Xiao Xiao and Xudong Xie

Remote Sens. 2025, 17(5), 742; https://doi.org/10.3390/rs17050742 - 20 Feb 2025

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Abstract

Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine [...] Read more.

Due to the inherent limitations in remote sensing image quality, seasonal variations, and radiometric inconsistencies, river extraction based on remote sensing image classification often results in omissions. These challenges are particularly pronounced in the detection of narrow and complex river networks, where fine river features are frequently underrepresented, leading to fragmented and discontinuous water body extraction. To address these issues and enhance both the completeness and accuracy of fine river identification, this study proposes an advanced fine river extraction and optimization method. Firstly, a linear river feature enhancement algorithm for preliminary optimization is introduced, which combines Frangi filtering with an improved GA-OTSU segmentation technique. By thoroughly analyzing the global features of high-resolution remote sensing images, Frangi filtering is employed to enhance the river linear characteristics. Subsequently, the improved GA-OTSU thresholding algorithm is applied for feature segmentation, yielding the initial results. In the next stage, to preserve the original river topology and ensure stripe continuity, a river skeleton refinement algorithm is utilized to retain critical skeletal information about the river networks. Following this, river endpoints are identified using a connectivity domain labeling algorithm, and the bounding rectangles of potential disconnected regions are delineated. To address discontinuities, river endpoints are shifted and reconnected based on structural similarity index (SSIM) metrics, effectively bridging gaps in the river network. Finally, nonlinear water optimization combined K-means clustering segmentation, topology and spectral inspection, and small-area removal are designed to supplement some missed water bodies and remove some non-water bodies. Experimental results demonstrate that the proposed method significantly improves the regularization and completeness of river extraction, particularly in cases of fine, narrow, and discontinuous river features. The approach ensures more reliable and consistent river delineation, making the extracted results more robust and applicable for practical hydrological and environmental analyses. Full article

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

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19 pages, 13900 KiB  

Open AccessArticle

Study on the Spatiotemporal Evolution Relationship Between Ecological Resilience and Land Use Intensity in Hebei Province and Scenario Simulation

by Haiying Huo, Pengfei Liu, Su Li, Wei Hou, Wenjing Xu, Xiayu Wen and Yuhang Bai

Sustainability 2025, 17(2), 664; https://doi.org/10.3390/su17020664 - 16 Jan 2025

Viewed by 568

Abstract

The ecological health of Hebei Province is critical to the sustainable development of the Beijing–Tianjin–Hebei region. However, the increasing intensity of land use in recent years has placed significant pressure on local ecosystems, making it essential to understand how land use changes affect [...] Read more.

The ecological health of Hebei Province is critical to the sustainable development of the Beijing–Tianjin–Hebei region. However, the increasing intensity of land use in recent years has placed significant pressure on local ecosystems, making it essential to understand how land use changes affect ecological resilience across different regions and time periods. This study takes Hebei Province as the research area and selects four time points—1990, 2000, 2010, and 2020—to systematically evaluate the spatiotemporal variations in ecological resilience and land use intensity using indicators such as the water resource supply, climate regulation, hydrological regulation, biodiversity, the landscape pattern index, and land use types. This study employs spatial analysis methodologies, including the spatial autocorrelation model and the Geographically Weighted Regression (GWR) model, to systematically analyze spatial clustering patterns, spatial heterogeneity, and influencing mechanisms. Scenario simulations are also conducted to predict ecological resilience trends in 2030 under a sustainable development scenario. The results indicate that (1) over the past 30 years, both ecological resilience and land use intensity in Hebei Province have generally increased, with notable spatial disparities among cities. (2) Moreover, a significant negative correlation exists between ecological resilience and land use intensity, with the GWR model revealing pronounced spatial heterogeneity. The impact of land use intensity on ecological resilience is relatively minor in highly urbanized central and southern regions, while northern and northwestern regions are more sensitive to changes, highlighting the need for better coordination between land use planning and ecological protection. (3) Finally, scenario simulations predict a slight overall decline in ecological resilience by 2030, with central and southern cities projected to experience the largest decreases, while some northern cities are expected to see modest improvements. These findings underscore the importance of regionally differentiated land use management and ecological protection strategies. This study provides scientific evidence and planning recommendations to improve ecological resilience and environmental protection in Hebei Province. At the same time, this study contributes to a deeper understanding of how land use dynamics influence ecological resilience. The methodologies and findings presented in this study can also be applied to guide sustainable development planning in other rapidly urbanizing areas, providing a valuable framework for addressing regions facing similar ecological challenges. Full article

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

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25 pages, 20678 KiB  

Open AccessArticle

Spatial–Temporal Evolution Pattern of Soil Erosion and Its Dominant Factors on the Loess Plateau from 2000 to 2020

by Panpan Liu, Bing Guo, Rui Zhang and Longhao Wang

Land 2024, 13(11), 1944; https://doi.org/10.3390/land13111944 - 18 Nov 2024

Viewed by 815

Abstract

Global changes have led to significant changes in soil erosion on the Loess Plateau. Soil erosion leads to the degradation of land resources and a decline in soil fertility, adversely affecting agricultural production and the socioeconomic situation. Therefore, revealing the spatiotemporal evolution patterns [...] Read more.

Global changes have led to significant changes in soil erosion on the Loess Plateau. Soil erosion leads to the degradation of land resources and a decline in soil fertility, adversely affecting agricultural production and the socioeconomic situation. Therefore, revealing the spatiotemporal evolution patterns of soil erosion in the Loess Plateau region and investigating the influencing factors that contribute to soil erosion are crucial for its management and restoration. In this study, the RUSLE monthly model and the Geodetector model were utilized to reveal the spatiotemporal trends of soil erosion in the Loess Plateau from 2000 to 2020 and to determine the dominant influencing factors in different periods. The main results are as follows: (1) From 2000 to 2020, the soil erosion in the Loess Plateau initially weakened and then intensified, indicating that precipitation and precipitation intensity have different effects on surface soil. (2) From 2000 to 2015, the area experiencing slight and mild erosion increased. This is attributed to the increase in vegetation coverage in the Loess Plateau region, which has alleviated soil erosion in the area. (3) From 2000 to 2020, zones of severe soil erosion were mainly located in the cities of Yan’an and Yulin and their surrounding areas. The gravity center of soil erosion shifted northwestward from Yan’an City overall, indicating an improvement in the soil erosion conditions in the Yan’an area. (4) The predominant level of soil erosion across different land-use types was slight erosion, accounting for over 40%. This may be a result of forestry ecological projects that effectively reduce soil loss. (5) In slope zones of 0–5°, slight erosion accounted for the largest area proportion. As the slope increased, the area proportion of severe and extremely severe erosion also increased. This is attributed to the protective role of vegetation on soil in gentle slope areas. (6) From 2000 to 2020, vegetation was the dominant single factor influencing the spatiotemporal changes in soil erosion, while the interactions between vegetation and land use had the largest explanatory power, indicating that changes in land-use types partially affect variations in vegetation coverage. Our research findings could provide important data support for soil erosion control and eco-environment restoration in the Loess Plateau region. Full article

(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)

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