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Silk-Road Disaster Monitoring and Risk Assessment Using Remote Sensing and GIS

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 March 2020) | Viewed by 16831

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Prof. Dr. Yong Ge

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Guest Editor

State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China
Interests: information extraction; uncertainty assessment; image processing and analysis; spatial statistics; classification
Special Issues, Collections and Topics in MDPI journals

Dr. Jennifer Mckinley

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Guest Editor

School of Natural and Built Environment, Queen’s University Belfast, Belfast BT7 1NN, UK
Interests: the application of spatial analysis techniques, including geostatistics, compositional data analysis and Geographical Information Science (GIS), to soil geochemistry, environmental and criminal forensics, human health, slope instability, airborne geophysics and weathering studies

Prof. Dr. Feng Ling

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Guest Editor

Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Science, No. 340, Xudong Road, Wuhan 430077, China
Interests: remote sensing; land cover; super-resolution mapping; sub-pixel information; water resource; forest disturbance; multi-temporal analysis; data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Belt and Road Initiative (BRI), which calls for cooperative economic, political, and cultural exchange at the global level along the ancient Silk Road, covers more than 70 countries and 4.4 billion people (63% of the world). Due to the active underlying geological structure and climate changes, natural hazards (e.g., landslides, flooding, drought, and storm surges) occur frequently along the belt and road, bringing great challenges to the areas along the BRI.

On May 11–12, 2019, the International Conference on Silk-Road Disaster Risk Reduction and Sustainable Development (SiDRR Conference 2019), which was co-hosted by the Chinese Academy of Sciences, the Chinese Association for Science and Technology, the United Nations Environment Program, the United Nations Office for Disaster Risk Reduction, and the Alliance of International Science Organizations (ANSO), was held in BeiJing, China. More than 780 experts and scholars from over 40 countries and regions exchanged ideas on how to make the areas along the Silk Road more sustainable and disaster-proof. Participants of the SiDRR Conference 2019 have reached a consensus on disaster risk reduction (DRR) and sustainable development through the announcement of the Beijing Statement, which is a scientific, technical, and political dialogue for the better implementation of Sendai Framework for Disaster Risk Reduction 2015–2030 and 2030 Agenda for Sustainable Development.

It is well recognized that remote sensing can provide accurate and timely observations and measurements of disasters across a range of spatial and temporal scales, and has become one of the most important technologies for disaster monitoring and risk assessment. The purpose of this Special Issue is to publish papers by scientists currently working on remote sensing and GIS for disaster monitoring and risk assessment in BRI. The topics include remote sensing and GIS techniques for natural and anthropogenic disaster monitoring, modelling, analyzing, assessing, predicting, and preventing. Through this Special Issue, we are hoping to encourage academic exchanges on methods, theories, models, strategies, etc., related to remote sensing and GIS for disaster reduction and sustainable development in the BRI.

Prof. Yong Ge
Dr. Jennifer Mckinley
Prof. Feng Ling
Guest Editor

Manuscript Submission Information

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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.

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Keywords

One belt one road;
Natural and anthropogenic disaster;
Remote sensing monitoring;
Data acquisition and interpretation;
Risk assessment.

Published Papers (4 papers)

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Research

17 pages, 4768 KiB

Open AccessArticle

Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology

by Liang Tang, Mingzhu He and Xinrong Li

Remote Sens. 2020, 12(11), 1742; https://doi.org/10.3390/rs12111742 - 28 May 2020

Cited by 31 | Viewed by 3823

Abstract

Desertification control and scientific evaluation of desert ecosystem sustainability are important issues for countries along the Silk Road Economic Belt. Fractional vegetation coverage (FVC) is used as a quantitative indicator to describe the vegetation coverage of desert ecosystems. Although satellite remote sensing technology has been widely used to retrieve FVC at the regional and global scale, the authenticity evaluation of the inversion results has been flawed. To gain insight into the composition, structure and changes of desert vegetation, it is important to assess the accuracy of FVC and explore the relationship between FVC and meteorological factors. Therefore, we adopted unmanned aerial vehicle remote sensing (UAVRS) technology to verify the inversion results and analyse the practicability of MODIS-NDVI (where NDVI = normalized difference vegetation index) products in desert areas. To provide a new method for the estimation of vegetation coverage in the natural state, the relationships between vegetation coverage and four meteorological factors, namely, land surface temperature, temperature, precipitation and evaporation were analysed. The results showed that using the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (coefficient of determination (R²) = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007) but underestimates vegetation coverage in the study area. MODIS-NDVI data products are different from the real NDVI in the study area. Correcting MODIS-NDVI data products can effectively improve the accuracy of the inversion. When extracting vegetation coverage in this area, the scale has little effect on the results. There is a significant correlation between precipitation, evaporation and FVC in the area, but the interaction of temperature and land surface temperature with precipitation and evaporation also has a considerable impact on FVC, and evaporation has a substantial impact on FVC values inverted from MODIS-NDVI data (FVC_M), When exploring the relationship between vegetation coverage and meteorological elements, if vegetation coverage is retrieved from MODIS-NDVI data products or MODIS-NDVI data, when considering temperature and precipitation, the effect of evaporation should also be considered. In addition, meteorological factors can be used to predict FVC (R² = 0.7364, RMSE = 0.0623), which provides a new method for estimating FVC in areas with less manual intervention. Full article

(This article belongs to the Special Issue Silk-Road Disaster Monitoring and Risk Assessment Using Remote Sensing and GIS)

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22 pages, 9683 KiB

Open AccessArticle

Fine-Scale Coastal Storm Surge Disaster Vulnerability and Risk Assessment Model: A Case Study of Laizhou Bay, China

by Yueming Liu, Chen Lu, Xiaomei Yang, Zhihua Wang and Bin Liu

Remote Sens. 2020, 12(8), 1301; https://doi.org/10.3390/rs12081301 - 20 Apr 2020

Cited by 18 | Viewed by 3562

Abstract

In the assessment of storm surge vulnerability, existing studies have often selected several types of disaster-bearing bodies and assessed their exposure. In reality, however, storm surges impact all types of disaster-bearing bodies in coastal and estuarine areas. Therefore, all types of disaster-bearing bodies exposed to storm surges should be considered when assessing exposure. In addition, geographical factors will also have an impact on the exposure of the affected bodies, and thus need to be fully considered. Hence, we propose a fine-scale coastal storm surge disaster vulnerability and risk assessment model. First, fine-scale land-use data were obtained based on high-resolution remote sensing images. Combined with natural geographic factors, such as the digital elevation model (DEM), slope, and distance to water, the exposure of the disaster-bearing bodies in each geographic unit of the coastal zone was comprehensively determined. A total of five indicators, such as the percentage of females and ratio of fishery products to the gross domestic product (GDP), were then selected to assess sensitivity. In addition, six indicators, including GDP and general public budget expenditure, were selected to assess adaptability. Utilizing the indicators constructed from exposure, sensitivity, and adaptability, a vulnerability assessment was performed in the coastal area of Laizhou Bay, China, which is at high risk from storm surges. Furthermore, the storm surge risk assessment was achieved in combination with storm water statistics. The results revealed that the Kenli District, Changyi City, and the Hanting District have a higher risk of storm surge and require more attention during storm surges. The storm surge vulnerability and risk assessment model proposed in this experiment fully considers the impact of the natural environment on the exposure indicators of the coastal zone’s disaster-bearing bodies, and combines sensitivity, adaptability indicators, and storm water record data to conduct vulnerability and risk assessment. At the same time, the model proposed in this study can also realize multi-scale assessment of storm surge vulnerability and risk based on different scales of socioeconomic statistical data, which has the advantages of flexibility and ease of operation. Full article

(This article belongs to the Special Issue Silk-Road Disaster Monitoring and Risk Assessment Using Remote Sensing and GIS)

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21 pages, 9034 KiB

Open AccessArticle

Spatiotemporal Distribution and Risk Assessment of Heat Waves Based on Apparent Temperature in the One Belt and One Road Region

by Cong Yin, Fei Yang, Juanle Wang and Yexing Ye

Remote Sens. 2020, 12(7), 1174; https://doi.org/10.3390/rs12071174 - 06 Apr 2020

Cited by 17 | Viewed by 3674

Abstract

Heat waves seriously affect the productivity and daily life of human beings. Therefore, they bring great risks and uncertainties for the further development of countries in the One Belt and One Road (OBOR) region. In this study, we used daily meteorological monitoring data to calculate the daily apparent temperature and annual heat wave dataset for 1989–2018 in the OBOR region. Then, we studied their spatiotemporal distribution patterns. Additionally, multi-source data were used to assess heat wave risk in the OBOR region. The main results are as follows: (1) The daily apparent temperature dataset and annual heat wave dataset for 1989–2018 in the OBOR region at 0.1° × 0.1° gridded resolution were calculated. China, South Asia and Southeast Asia are suffering the most serious heat waves in the OBOR region, with an average of more than six heat waves, lasting for more than 60 days and the extreme apparent temperature has reached over 40 °C. Additionally, the frequency, duration and intensity of heat waves have been confirmed to increase continuously. (2) The heat wave risk in the OBOR region was assessed. Results show that the high heat wave risk areas are distributed in eastern China, northern South Asia and some cities. The main conclusion is that the heat wave risk in most areas along the OBOR route is relatively high. In the process of deepening the development of countries in the OBOR region, heat wave risk should be fully considered. Full article

(This article belongs to the Special Issue Silk-Road Disaster Monitoring and Risk Assessment Using Remote Sensing and GIS)

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19 pages, 19801 KiB

Open AccessArticle

Exceptional Drought across Southeastern Australia Caused by Extreme Lack of Precipitation and Its Impacts on NDVI and SIF in 2018

by Feng Tian, Jianjun Wu, Leizhen Liu, Song Leng, Jianhua Yang, Wenhui Zhao and Qiu Shen

Remote Sens. 2020, 12(1), 54; https://doi.org/10.3390/rs12010054 - 21 Dec 2019

Cited by 45 | Viewed by 5123

Abstract

Increased drought frequency in Australia is a pressing concern for scholars. In 2018, a severe drought in eastern Australia was recorded by the Emergency Events Database (EM-DAT). To investigate the main causes and impacts of this drought across southeastern Australia, this work presents an overview of the drought mechanism and depicts its evolutionary process. The Standardized Precipitation Evapotranspiration Index (SPEI) from the Global Drought Monitor was used to identify the drought event and characterize its spatiotemporal distribution. The Normalized Difference Vegetation Index (NDVI) and the sun-induced chlorophyll fluorescence (SIF) were used to investigate the drought impacts on vegetation growth. In addition, the effects of drought response measures on Sustainable Development Goals (SDGs) were analyzed. Our results showed that the exceptional drought occurred across southeastern Australia from April to December, and it was most severe in July, owing to an extreme lack of precipitation and increase in temperature. Moreover, we identified profound and long-lasting impacts of the drought on NDVI and SIF levels, especially for cropland. Furthermore, we also found that SIF was superior to NDVI in detecting drought impacts. This study advised on how to formulate timely and effective drought-response measures and supports sustainable socioeconomic development in Australia. Full article

(This article belongs to the Special Issue Silk-Road Disaster Monitoring and Risk Assessment Using Remote Sensing and GIS)

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