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Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 44977

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Special Issue Editors

Prof. Dr. Jiquan Zhang

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Guest Editor

School of Environment, Northeast Normal University, Changchun 130117, China
Interests: integrated natural disaster risk assessment and management; natural disaster risk warning; emergency decision-making for natural disasters; remote sensing and GIS techniques

Dr. Zhijun Tong

E-Mail Website
Guest Editor

School of Environment, Northeast Normal University, Changchun,130117, China
Interests: disaster risk assessment; application and modelling of GIS/RS; ecology security assessment

Dr. Xingpeng Liu

E-Mail Website
Guest Editor

School of Environment, Northeast Normal University, Changchun 130117, China
Interests: natural disaster risk assessment and reduction

Special Issue Information

Dear Colleagues,

The world has experienced an increasing impact of disasters in recent decades. Many regions are exposed to natural hazards, each with unique characteristics. The main causes for this increase are most probably related to climate change and an increase in vulnerable populations. To reduce disaster losses, the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) clearly indicates that more efforts should be directed toward disaster risk management, with a focus on hazard assessment, elements-at-risk mapping, and vulnerability and risk assessment, all of which have an important spatial component. The use of remote sensing techniques has become an integrated approach in natural disaster risk assessment and management. Natural disaster risk assessment and management are carried out at multiple scales, ranging from global to community levels. These levels have their own objectives and spatial data requirements for hazard inventories, environmental data, triggering or causal factors, and elements at risk. Remote sensing techniques can provide an effective data source for natural disaster risk assessment and management, and an effective solution for studying different scale disaster problems. In recent years, significant progress has been achieved in the innovative exploitation of remote sensing techniques in natural disaster risk assessment and management. Natural disaster risk assessment and management represent one of the disciplines that have seen the greatest advances in the field of remote sensing in recent years.

This Special Issue seeks contributions involving innovative approaches or relevant case studies regarding natural disaster risk assessment and management using remote sensing techniques. Topics may cover anything from classical natural disaster risk assessment and management, to more comprehensive aims and scales. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal) and multi-scale approaches or studies focused on multi-hazard disaster risk assessment and integrated disaster risk management, among other issues, are welcome.

Articles may address, but are not limited to, the following topics:

Hazard assessment of natural disasters;
Exposure evaluation of hazard-affected body;
Vulnerability assessment of hazard-affected body;
Disaster prevention and mitigation capability assessment;
Natural disaster risk identification;
Natural disaster risk survey;
Natural disaster risk analysis;
Natural disaster risk assessment;
Integrated natural disaster risk assessment and management;
Natural disaster chain risk assessment;
Natural disaster risk early warning;
Decision making for natural disaster risk;
Disaster risk estimation in the context of climate change;
Natural disaster insurance;
Urban, community, and infrastructure disaster resilience assessment.

Prof. Dr. Jiquan Zhang
Dr. Zhijun Tong
Dr. Xingpeng Liu
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

natural disaster
multi-hazards
remote sensing
hazard assessment
exposure evaluation
vulnerability assessment
risk assessment
risk management
integrated natural disaster risk
natural disaster risk early warning
spatial data
big data
multisource remote sensing data
decision making for natural disaster risk
natural disaster insurance
disaster resilience
meteorological disaster
agro-meteorological disaster
geologic disaster
earthquakes
hydrological disasters
forest and grassland fire
natural disaster chain
geographic information systems

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Published Papers (14 papers)

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22 pages, 4950 KiB

Open AccessArticle

Assessment of Land Desertification and Its Drivers on the Mongolian Plateau Using Intensity Analysis and the Geographical Detector Technique

by Yongfang Wang, Enliang Guo, Yao Kang and Haowen Ma

Remote Sens. 2022, 14(24), 6365; https://doi.org/10.3390/rs14246365 - 16 Dec 2022

Cited by 13 | Viewed by 2523

Abstract

Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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24 pages, 9595 KiB

Open AccessArticle

Application of 3D Error Diagram in Thermal Infrared Earthquake Prediction: Qinghai–Tibet Plateau

by Chengxiang Zhan, Qingyan Meng, Ying Zhang, Mona Allam, Pengcheng Wu, Linlin Zhang and Xian Lu

Remote Sens. 2022, 14(23), 5925; https://doi.org/10.3390/rs14235925 - 23 Nov 2022

Cited by 2 | Viewed by 1907

Abstract

Earthquakes are the most dangerous natural disasters, and scholars try to predict them to protect lives and property. Recently, a long-term statistical analysis based on a “heating core” filter was applied to explore thermal anomalies related to earthquakes; however, some gaps are still present. Specifically, (1) whether there are differences in thermal anomalies generated by earthquakes of different magnitudes has not yet been discussed; and (2) thermal anomalies in high-spatial-resolution data are often distributed in spots, which is not convenient for statistics of thermal anomalies. To address these issues, in this study, we applied high-spatial-resolution thermal infrared data to explore the performance of the “heating core” for earthquake prediction at different magnitudes (i.e., 3, 3.5, 4, 4.5, and 5). The specific steps were as follows: first, the resampling and moving-window methods were applied to reduce the spatial resolution of the dataset and extract the suspected thermal anomalies; second, the “heating core” filter was used to eliminate thermal noise unrelated to the seismic activity in order to identify potential thermal anomalies; third, the time–distance–magnitude (TDM) windows were used to establish the correspondence between earthquakes and thermal anomalies; finally, the new 3D error diagram (false discovery rate, false negative rate, and space–time correlation window) and the significance test method were applied to investigate the performance under each minimum magnitude with training data, and the robustness was validated using a test dataset. The results show that the following: (1) there is no obvious difference in the thermal anomalies produced by earthquakes of different magnitudes under the conditions of a “heating core”, and (2) the best model with a “heating core” can predict earthquakes effectively within 200 km and within 20 days of thermal anomalies’ appearance. The binary prediction model with a “heating core” based on thermal infrared anomalies can provide some reference for earthquake prediction. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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19 pages, 5412 KiB

Open AccessArticle

Investigation of Forest Fire Characteristics in North Korea Using Remote Sensing Data and GIS

by Ri Jin and Kyoo-Seock Lee

Remote Sens. 2022, 14(22), 5836; https://doi.org/10.3390/rs14225836 - 17 Nov 2022

Cited by 8 | Viewed by 3160

Abstract

Forest fires cause damage to property and the environment around the world every year. North Korea has suffered from fires every year. Fires may lead to temporary or permanent damage to forest ecosystems, long-term site degradation, and alteration of hydrological regimes, producing detrimental impacts on economies, human health, and safety. In North Korea, fires cause serious damage to the affected mountainous environment. However, it is very difficult to obtain ground information or perform field checks because of the political isolation of North Korea. Thus, there are few studies that have investigated North Korean fires. In this situation, remote sensing techniques and digital topographic data can be used to investigate fire characteristics in North Korea. In this study, fire trends were analyzed using Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Land Processes Distributed Active Archive Center (LPDAAC) from 2004 to 2015, and Landsat data were processed to estimate burned areas in South Hamgyong Province (SHP) and Gangwon Province (GWP) in North Korea. The burn severity of large fires in elevation, slope, and landform features was also analyzed to investigate large fire-burned areas using 30-m-resolution Global Digital Elevation Model (DEM) data from the United States National Aeronautics and Space Administration (NASA). After the results were compared and discussed, the following conclusions were derived. (1) In terms of location, fires in SHP were relatively concentrated along BaekDu-DaeGan (BDDG), while fires in GWP were scattered throughout the province. (2) In terms of size, the large fire-burned areas with an area greater than 1000 ha are significantly more frequent in SHP than in GWP. In brief, large fires occurred more frequently and were more serious in SHP than in GWP. (3) In terms of forest type, coniferous areas were more susceptible to damage from fires and large fires than deciduous areas in both GWP and SHP. This is attributed to the combustible resin within the coniferous trees. Particularly, when a crown fire occurs, it tends to spread rapidly throughout the coniferous forest. (4) Regarding landforms, most large fires occurred along windward-side open slopes, while there were very few fires in shallow valleys, high ridges, or U-shaped valleys. It is believed that cultivation in high-elevation terrain and a lack of fire-extinguishing equipment and systems allow large fires to spread quickly. North Korea is very susceptible to large fire damage and must develop preparation measures against such situations. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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19 pages, 19625 KiB

Open AccessArticle

Grassland Biomass Inversion Based on a Random Forest Algorithm and Drought Risk Assessment

by Lingxin Bu, Quan Lai, Song Qing, Yuhai Bao, Xinyi Liu, Qin Na and Yuan Li

Remote Sens. 2022, 14(22), 5745; https://doi.org/10.3390/rs14225745 - 13 Nov 2022

Cited by 6 | Viewed by 2280

Abstract

Xilin Gol is a typical kind of grassland in arid and semi-arid regions. Under climate warming, the droughts faced by various grassland types tend to expand in scope and intensity, and increase in frequency. Therefore, the quantitative analysis of drought risk in different grassland types becomes particularly important. Based on multi-source data, a random forest regression algorithm was used to construct a grassland biomass estimation model, which was then used to analyze the spatiotemporal variation characteristics of grassland biomass. A quantitative assessment of drought risk (DR) in different grassland types was applied based on the theory of risk formation, and a structural equation model (SEM) was used to analyze the drivers of drought risk in different grassland types. The results show that among the eight selected variables that affect grassland biomass, the model had the highest accuracy (R = 0.90) when the normalized difference vegetation index (NDVI), precipitation (Prcp), soil moisture (SM) and longitude (Lon) were combined as input variables. The grassland biomass showed a spatial distribution that was high in the east and low in the west, gradually decreasing from northeast to southwest. Among the grasslands, desert grassland (DRS) had the highest drought risk (DR = 0.30), while meadow grassland (MEG) had the lowest risk (DR = 0.02). The analysis of the drivers of drought risk in grassland biomass shows that meteorological elements mainly drive typical grasslands (TYG) and other grasslands (OTH). SM greatly impacted MEG, and ET had a relatively high contribution to DRS. This study provides a basis for managing different grassland types in large areas and developing corresponding drought adaptation programs. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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27 pages, 15169 KiB

Open AccessArticle

Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods

by Qing Zhou, Heng Zhang and Zhiwei Wu

Remote Sens. 2022, 14(22), 5724; https://doi.org/10.3390/rs14225724 - 12 Nov 2022

Cited by 12 | Viewed by 3177

Abstract

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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17 pages, 5661 KiB

Open AccessArticle

Three-Dimensional Vulnerability Assessment of Peanut (Arachis hypogaea) Based on Comprehensive Drought Index and Vulnerability Surface: A Case Study of Shandong Province, China

by Sicheng Wei, Yueting Yang, Kaiwei Li, Ying Guo and Jiquan Zhang

Remote Sens. 2022, 14(21), 5359; https://doi.org/10.3390/rs14215359 - 26 Oct 2022

Cited by 3 | Viewed by 1965

Abstract

Agricultural drought is a major problem facing China’s agricultural production. In this study, the cash crop ‘peanut’ was used as an example to explore vulnerability. Through the atmosphere–plant–soil continuum system, a single index that could represent different types of droughts affecting peanuts was selected and weighted using the CRITIC weighting method to construct a multi-source data fusion drought index (MFDI). Then, Pearson correlation analysis between the comprehensive drought index and relative meteorological yield and the Mann–Kendall trend test for different growth periods of peanuts were used to verify MFDI and analyze the variation over time. A three-dimensional vulnerability assessment method of drought intensity–drought duration–yield reduction rate was established based on the run theory and trend surface analysis. The results show that the constructed multi-source data fusion drought index (MFDI) can more accurately characterize the actual drought conditions of peanuts in Shandong Province. The MFDI results showed that the drought severity in the coastal areas of the study area decreased with the growth and development of peanuts, while the drought became more severe in the western and northern parts during the late growth period of peanuts. The vulnerability surface of the drought intensity–drought duration–yield reduction rate showed that when the drought intensity was < 0.8 and the duration was < 3.5 months, the vulnerability of peanut crops was low, and then with the increase in drought intensity or duration, the vulnerability increased. The impact of drought duration cannot be ignored. In contrast to traditional vulnerability assessment methods, this study established a three-dimensional vulnerability surface, which provides a new approach for agricultural drought vulnerability assessment. The research results are helpful for a deeper understanding of the relationship between drought and crop vulnerability and provide scientific support for local governments in formulating disaster prevention and mitigation policies. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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24 pages, 13971 KiB

Open AccessArticle

Urban Flood Risk Assessment in Zhengzhou, China, Based on a D-Number-Improved Analytic Hierarchy Process and a Self-Organizing Map Algorithm

by Zening Wu, Wanjie Xue, Hongshi Xu, Denghua Yan, Huiliang Wang and Wenchao Qi

Remote Sens. 2022, 14(19), 4777; https://doi.org/10.3390/rs14194777 - 24 Sep 2022

Cited by 23 | Viewed by 3149

Abstract

Flood risk assessment is an important tool for disaster warning and prevention. In this study, an integrated approach based on a D-number-improved analytic hierarchy process (D-AHP) and a self-organizing map (SOM) clustering algorithm are proposed for urban flooding risk assessment. The urban flood inundation model and geographic information system (GIS) technology were used to quantify the assessment indices of urban flood risk. The D-AHP approach was adopted to determine the weights of the indices, which effectively makes up for the shortcomings of the AHP in dealing with uncertain evaluation information (such as fuzzy and incomplete information). In addition, the SOM clustering algorithm was applied to determine the flood risk level. It is a data-driven approach that avoids the subjective determination of a flood risk classification threshold. The proposed approach for flood risk assessment was implemented in Zhengzhou, China. The flood risk was classified into five levels: highest risk, higher risk, medium risk, lower risk, and the lowest risk. The proportion of the highest risk areas was 9.86%; such areas were mainly distributed in the central and eastern parts of the Jinshui District, the eastern part of the Huiji District, and the northeastern part of the Guancheng District, where there were low terrain and serious waterlogging. The higher risk areas accounted for 24.26% of the study area, and were mainly distributed in the western and southern parts of the Jinshui District, the southern part of the Huiji District, the middle and eastern parts of the Zhongyuan District, the northeastern part of the Erqi District, and the northwestern part of the Guancheng District, which consisted of economically developed areas of dense population and buildings, matching well with historical flooding events. To verify the effectiveness of the proposed approach, traditional approaches for risk assessment were compared. The comparison indicated that the proposed approach is more reasonable and accurate than the traditional approaches. This study showed the potential of a novel approach to flood risk assessment. The results can provide a reference for urban flood management and disaster reduction in the study area. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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24 pages, 10101 KiB

Open AccessArticle

Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies

by Faming Huang, Siyu Tao, Deying Li, Zhipeng Lian, Filippo Catani, Jinsong Huang, Kailong Li and Chuhong Zhang

Remote Sens. 2022, 14(18), 4436; https://doi.org/10.3390/rs14184436 - 6 Sep 2022

Cited by 39 | Viewed by 3473

Abstract

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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28 pages, 6221 KiB

Open AccessArticle

Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen

by Ali R. Al-Aizari, Yousef A. Al-Masnay, Ali Aydda, Jiquan Zhang, Kashif Ullah, Abu Reza Md. Towfiqul Islam, Tayyiba Habib, Dawuda Usman Kaku, Jean Claude Nizeyimana, Bazel Al-Shaibah, Yasser M. Khalil, Wafaa M. M. AL-Hameedi and Xingpeng Liu

Remote Sens. 2022, 14(16), 4050; https://doi.org/10.3390/rs14164050 - 19 Aug 2022

Cited by 37 | Viewed by 5247

Abstract

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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19 pages, 7230 KiB

Open AccessArticle

Rapid and Automated Mapping of Crop Type in Jilin Province Using Historical Crop Labels and the Google Earth Engine

by Feng Zhi, Zhenhua Dong, Suri Guga, Yongbin Bao, Aru Han, Jiquan Zhang and Yulong Bao

Remote Sens. 2022, 14(16), 4028; https://doi.org/10.3390/rs14164028 - 18 Aug 2022

Cited by 15 | Viewed by 2966

Abstract

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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17 pages, 3012 KiB

Open AccessArticle

Evaluation of Drought Vulnerability of Maize and Influencing Factors in Songliao Plain Based on the SE-DEA-Tobit Model

by Yining Ma, Suri Guga, Jie Xu, Xingpeng Liu, Zhijun Tong and Jiquan Zhang

Remote Sens. 2022, 14(15), 3711; https://doi.org/10.3390/rs14153711 - 3 Aug 2022

Cited by 8 | Viewed by 1952

Abstract

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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17 pages, 4413 KiB

Open AccessArticle

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring

by Chunling Bao, Mei Yong, Cholaw Bueh, Yulong Bao, Eerdemutu Jin, Yuhai Bao and Gomboluudev Purevjav

Remote Sens. 2022, 14(15), 3661; https://doi.org/10.3390/rs14153661 - 30 Jul 2022

Cited by 10 | Viewed by 3044

Abstract

Dust storms are common in Mongolia and northern China, this is a serious threat to the ecological security and socioeconomic development of both countries and the surrounding areas. However, a complete quantitative study of the source area, affected area, and moving path of dust storm events (DSEs) in Mongolia and China is still lacking. In this study, we monitored and analyzed the spatiotemporal characteristics of the source area and affected areas of DSEs in Mongolia and China using the high-spatiotemporal-resolution images taken by the Himawari-8 satellite from March to June 2016–2020. In addition, we calculated the moving path of dusty weather using the HYSPLIT model. The results show that (1) temporality, a total of 605 DSEs occurred in the study area, with most of them occurring in April (232 DSEs), followed by May (173 DSEs). Spatially, the dust storm sources were concentrated in the arid inland areas such as the Taklimakan Desert (TK, 138 DSEs) and Badain Jaran Desert (BJ, 87 DSEs) in the western, and the Mongolian Gobi Desert (GD, 69 DSEs) in the central parts of the study area. (2) From the affected areas of the DSEs, about 60% of the DSEs in Mongolia started locally and then affected downwind China, as approximately 55% of the DSEs in the Inner Mongolia Desert Steppe and Hunshandake Sandy Land came from Mongolia. However, the DSEs in the TK located in the Tarim Basin of northwest China affected the entire study area, with only 31.3% belonging to the local dust. (3) From the moving path of the dusty weather, the dusty weather at the three meteorological stations (Dalanzadgad, Erlian, and Beijing), all located on the main transmission path of DSEs, was mainly transported from the windward area in the northwest, accounting for about 65.5% of the total path. This study provides a reliable scientific basis for disaster prevention and control, and has practical significance for protecting and improving human settlements. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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17 pages, 7182 KiB

Open AccessArticle

Comprehensive Risk Assessment of Urban Waterlogging Disaster Based on MCDA-GIS Integration: The Case Study of Changchun, China

by Chenyu Duan, Jiquan Zhang, Yanan Chen, Qiuling Lang, Yichen Zhang, Chenyang Wu and Zhen Zhang

Remote Sens. 2022, 14(13), 3101; https://doi.org/10.3390/rs14133101 - 28 Jun 2022

Cited by 34 | Viewed by 3605

Abstract

Urban waterlogging will harm economic development and people’s life safety; however, the waterlogging risk zoning map provides the necessary decision support for the management of urban waterlogging, urban development and urban planning. This paper proposes an urban waterlogging risk assessment method that combines multi-criteria decision analysis (MCDA) with a geographic information system (GIS). The framework of urban waterlogging risk assessment includes four main elements: hazard, exposure, vulnerability, and emergency response and recovery capability. Therefore, we selected the urban area of Changchun City, Jilin Province as the study area. The Analytic Hierarchy Process (AHP) is a generally accepted MCDA method, it is used to calculate the weight and generate a result map of hazards, exposure, vulnerability, and emergency responses and recovery capability. Based to the principle of natural disaster risk formation, a total of 18 parameters, including spatial data and attribute data, were collected in this study. The model results are compared with the recorded waterlogging points, and the results show that the model is more reliable. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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19 pages, 6056 KiB

Open AccessArticle

Assessment of Maize Drought Risk in Midwestern Jilin Province: A Comparative Analysis of TOPSIS and VIKOR Models

by Yining Ma, Suri Guga, Jie Xu, Xingpeng Liu, Zhijun Tong and Jiquan Zhang

Remote Sens. 2022, 14(10), 2399; https://doi.org/10.3390/rs14102399 - 17 May 2022

Cited by 13 | Viewed by 2749

Abstract

Drought is a major natural disaster that causes a reduction in rain-fed maize yield. Agricultural drought risk assessment is conducive to improving regional disaster management ability, thereby reducing food security risks and economic losses. Considering the complexity of risk assessment research, an increasing number of researchers are focusing on the multiple-criteria decision-making (MCDM) method. However, the applicability of the MCDM method to agro-meteorological disaster risk assessments is not clear. Therefore, this study comprehensively evaluated hazard, exposure, vulnerability, and emergency response and recovery capability using the TOPSIS and VIKOR models to generate a maize drought risk map in mid-western Jilin Province and ranked the drought risk of each county. The results showed that: (1) maize drought risk in the middle and west of Jilin province showed an increasing trend. Spatially, the risk diminished from west to east. The drought risks faced by Tongyu, Changchun, and Dehui were more severe; (2) the evaluation results of the two models were verified using the yield reduction rate. The VIKOR model was found to be more suitable for agrometeorological drought risk assessments; (3) according to the damage degree of drought disaster to maize, the cluster analysis method was used to divide the study area into three sub-regions: safe, moderate drought, and severe drought. Combined with the characteristics of different regions, suggestions on disaster prevention and mitigation are proposed. The results of this study can provide a basis for formulating strategies to alleviate drought, reduce losses, and ensure the sustainable development of agriculture. Full article

(This article belongs to the Special Issue Natural Disaster Risk Assessment and Management Using Remote Sensing Techniques)

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