Spatial Patterns of Disaster Risk Assessment via Remote Sensing

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15 pages, 28330 KiB

Open AccessArticle

Assessment of the Spatiotemporal Dynamics of Suitable Habitats for Typical Halophytic Vegetation in China Based on Maxent Model and Landscape Ecology Theory

by Fuyin Guo, Xiaohuang Liu, Xuehua Chen, Hongyu Li, Zulpiya Mamat, Jiufen Liu, Run Liu, Ran Wang, Liyuan Xing and Junnan Li

Forests 2024, 15(10), 1757; https://doi.org/10.3390/f15101757 - 6 Oct 2024

Viewed by 1038

Abstract

The widespread and complex formation of saline soils in China significantly affects the sustainable development of regional ecosystems. Intense climate changes and regional land use further exacerbate the uncertainties faced by ecosystems in saline areas. Therefore, studying the distribution characteristics of typical halophytic vegetation under the influence of climate change and human activities, and exploring their potential distribution areas, is crucial for maintaining ecological security in saline regions. This study focuses on Tamarix chinensis, Tamarix austromongolica, and Tamarix leptostachya, integrating geographic information systems, remote sensing, species distribution models, and landscape ecological risk (LER) theories and technologies. An optimized MaxEnt model was established using the ENMeval package, incorporating 143, 173, and 213 distribution records and 13 selected environmental variables to simulate the potential suitable habitats of these three Tamarix species. A quantitative assessment of the spatial characteristics and the area of their potential geographical distribution was conducted. Additionally, a landscape ecological risk assessment (LERA) of the highly suitable habitats of these three Tamarix species was performed using land use data from 1980 to 2020, and the results of the LERA were quantified using the Landscape Risk Index (LERI). The results showed that the suitable areas of Tamarix chinensis, Tamarix austromongolica, and Tamarix leptostachya were 9.09 × 10⁵ km², 6.03 × 10⁵ km², and 5.20 × 10⁵ km², respectively, and that the highly suitable habitats for the three species were concentrated in flat areas such as plains and basins. Tamarix austromongolica faced increasing ecological risk in 27.22% of its highly suitable habitat, concentrated in the northern region, followed by Tamarix chinensis in 16.70% of its area with increasing ecological risk, concentrated in the western and northern highly suitable habitats; Tamarix chinensis was the least affected, with an increase in ecological risk in only 1.38% of its area. This study provides valuable insights for the protection of halophytic vegetation, represented by Tamarix, in the context of China’s national land development. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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20 pages, 22765 KiB

Open AccessArticle

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

by Zhuoyu Lv, Shanshan Wang, Shuhao Yan, Jianyun Han and Gaoqiang Zhang

Sustainability 2024, 16(19), 8466; https://doi.org/10.3390/su16198466 - 29 Sep 2024

Viewed by 696

Abstract

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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17 pages, 9721 KiB

Open AccessArticle

A Spatio-Temporal Analysis of the Frequency of Droughts in Mexico’s Forest Ecosystems

by Leticia Citlaly López-Teloxa and Alejandro Ismael Monterroso-Rivas

Forests 2024, 15(7), 1241; https://doi.org/10.3390/f15071241 - 17 Jul 2024

Viewed by 785

Abstract

Droughts can affect forest ecosystems and lead to soil degradation, biodiversity loss, and desertification. Not all regions of Mexico are affected in the same way, as some areas are naturally more prone to drought due to their geographical location. Therefore, the objective of this work was to carry out a spatio-temporal analysis of the occurrence of droughts (severe and extreme) in Mexican forest systems, covering the period 2000–2021, and to study the area covered by these events in Mexican forest systems. This analysis was divided into three stages: the classification of land use and vegetation, spatial mapping and the classification of drought intensity, and an analysis of drought frequency and probability in forest systems. The results show that more than 46% of Mexico’s forest area experienced severe and extreme droughts during the 21-year period studied. Broadleaved forests were most affected by severe and extreme droughts, with a frequency of 6 years. The increasing frequency of droughts poses a major challenge to the resilience of forest ecosystems in Mexico, highlighting the need to implement climate change adaptation and forest management measures to protect the country’s biodiversity and natural resources. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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20 pages, 5043 KiB

Open AccessArticle

Spatio-Temporal Analysis of Erosion Risk Assessment Using GIS-Based AHP Method: A Case Study of Doğancı Dam Watershed in Bursa (Türkiye)

by Esin Erdoğan Yüksel, Ömer Faruk Karan and Abdullah Emin Akay

Forests 2024, 15(7), 1135; https://doi.org/10.3390/f15071135 - 29 Jun 2024

Viewed by 1007

Abstract

Soil erosion, one of the most serious phenomena in watershed management, can be estimated based on various criteria. Land use change is one of the most important factors affecting the susceptibility of soil erosion. In this study, the effect of land use change on soil erosion risk in two plan periods (2005 and 2017) was investigated using Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) for the forest planning units in the Doğancı Dam Watershed, located in Bursa, Türkiye. Eight criteria were evaluated including erosion-related slope, bedrock type, land use/land cover, precipitation, relative relief, aspect, drainage frequency, and density. According to the results, the most effective factor in soil erosion was slope (0.29), while bedrock type and land use/land cover ranked second with 0.19. It was found that full closure forests were characterized by high erosion resistance (0.3), while bare land was characterized as the most sensitive area to erosion (0.39). In terms of spatio-temporal changes in a 12-year period, the areas in the medium and high erosion risk decreased, while low and very low-risk areas increased. The ROC method showed a satisfactory accuracy of 72.8% and 80.2% for the 2005 and 2017 erosion risk maps, respectively. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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27 pages, 50593 KiB

Open AccessArticle

A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

by Jiayu Ding, Yuewei Wang and Chaoyue Li

Land 2024, 13(6), 753; https://doi.org/10.3390/land13060753 - 28 May 2024

Viewed by 1274

Abstract

Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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17 pages, 1785 KiB

Open AccessArticle

The Construction of a Crop Flood Damage Assessment Index to Rapidly Assess the Extent of Postdisaster Impact

by Yaoshuai Dang, Leiku Yang and Jinling Song

Remote Sens. 2024, 16(9), 1527; https://doi.org/10.3390/rs16091527 - 26 Apr 2024

Viewed by 1596

Abstract

Floods are among the most serious natural disasters worldwide; they cause enormous crop losses every year and threaten world food security. Many studies have focused on flood impact assessments for administrative districts, but fewer have focused on postdisaster impact assessments for specific crops. Therefore, this study used remote sensing data, including the normalized difference vegetation index (NDVI), elevation data, slope data, and precipitation data, combined with crop growth period data to construct a crop flood damage assessment index (CFAI). First, the analytic hierarchy process (AHP) was used to assign weights to the impact parameters; then, the Weighted Composite Score Method was used to calculate the CFAI; and finally, the impact was classified as sub-slight, slight, moderate, sub-severe, or severe based on the magnitude of the CFAI. This method was used for the Missouri River floods of 2019 in the United States and the Henan flood of 2021 in China. Due to the lack of measured data, the disaster vegetation damage index (DVDI) was used to compare the results. Compared with the DVDI, the CFAI underestimated the evaluation results. The CFAI can respond well to the degree of crop impact after flooding, providing new ideas and reference standards for agriculture-related departments. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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14 pages, 7816 KiB

Open AccessArticle

Incorporating the Results of Geological Disaster Ecological Risk Assessment into Spatial Policies for Ecological Functional Areas: Practice in the Qilian Mountains of China

by Xu Long, Qing Xiang, Rongguang Zhang and Hong Huang

Sustainability 2024, 16(7), 2976; https://doi.org/10.3390/su16072976 - 3 Apr 2024

Viewed by 1209

Abstract

Geological hazards cause changes in the quality of the ecological environment, affect the function and stability of ecosystems, and negatively impact the maintenance and restoration of ecological functions in ecological functional areas (EFAs). This study integrates machine learning, geographic information technology, and multivariate statistical analysis modeling to develop a technical framework for quantitative analysis of ecological risk assessment (ERA) based on the causal logic between geological hazards and ecosystems. The results of the geological disaster ERA are mapped to EFAs, effectively identifying and quantifying the risk characteristics of different EFAs. The results show that: (1) The hazard–vulnerability–exposure ERA framework effectively identifies the distribution characteristics of high ecological risk around the Qilian Mountains, with high risk in the east and low risk in the west. (2) In high ecological risk areas, high hazard–high vulnerability–low exposure is the main combination pattern, accounting for 83.3%. (3) Overall, hazard and vulnerability have a greater impact on geological disaster ecological risk than exposure, with path coefficients of 0.802 (significant at p = 0.01 level) and 0.438 (significant at p = 0.05 level), respectively, in SEM. The random forest model (R² = 0.748) shows that social factors such as human density and road density contribute significantly more to extreme high risk than other factors, with a contribution rate of up to 44%. (4) Thirty-five ecological functional units were systematically grouped into four clusters and used to formulate a “layered” spatial policy for EFAs. The results of the research are expected to provide support for maximizing the policy impact of EFAs and formulating management decisions that serve ecological protection. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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19 pages, 13001 KiB

Open AccessArticle

Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data

by Wei Wei, Jiping Wang, Libang Ma, Xufeng Wang, Binbin Xie, Junju Zhou and Haoyan Zhang

Land 2024, 13(1), 95; https://doi.org/10.3390/land13010095 - 15 Jan 2024

Cited by 4 | Viewed by 1793

Abstract

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America. Full article

(This article belongs to the Topic Spatial Patterns of Disaster Risk Assessment via Remote Sensing)

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Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Forests forests	2.4	4.4	2010	16.9 Days	CHF 2600
Land land	3.2	4.9	2012	17.8 Days	CHF 2600
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400
Water water	3.0	5.8	2009	16.5 Days	CHF 2600

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