Investigation of drought-vulnerable regions in North Korea using remote sensing and cloud computing climate data

Abstract

Drought is one of the most severe natural disasters in the world and leads to serious challenges that affect both the natural environment and human societies. North Korea (NK) has frequently suffered from severe and prolonged droughts since the second half of the twentieth century. These droughts affect the growing conditions of agricultural crops, which have led to food shortages in NK. However, it is not easy to obtain ground data because NK is one of the most closed-off societies in the world. In this situation, remote sensing (RS) techniques and cloud computing climate data (CCCD) can be used for drought monitoring in NK. RS-derived drought indices and CCCD were used to determine the drought-vulnerable regions in the spring season in NK. After the results were compared and discussed, the following conclusions were derived: (1) 10.0% of the total area of NK is estimated to be a drought-vulnerable region. The most susceptible regions to drought appear in the eastern and western coastal regions, far from BaekDu-DaeGan (BDDG), while fewer drought regions are found near BDDG and the Nahngrim Mountains. The drought-vulnerable regions are the coastal regions of South Hamgyong Province, North Hamgyong Province, South Pyongan Province, and South Hwanghae Province. The latter region is the food basket of NK. (2) In terms of land cover, the drought-vulnerable regions mainly consisted of croplands and mixed forest.

Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis

“…Many studies using EO data for North Korea have been proposed for monitoring land use and land cover (LULC) over the Remote Sens. 2020, 12, 255 7 of 26 past several decades (for example, [51][52][53][54][55]). Furthermore, a number of government institutes and think-tanks have already established different types of thematic maps in North Korea using EO data (e.g., agricultural maps; deforestation maps; land cover maps, etc.).…”

Cloud Computing in natural hazard modeling systems: Current research trends and future directions

“…For the disasters whose occurrences can be prevented, the complex disaster simulations based on different disaster models can be run over the Clouds as end services to determine the key causes of the disasters [52] [53]. Accordingly, measures can be prioritized in a particular region for preventing the disaster.…”

“…Researchers have relied on risk identification, historical information, monitoring based on data processing and analyses and simulation of processes for preventing the actual occurrences of the disaster such as flood and droughts. The studies by Yu and Kim [52] as well as Jeyaseelan [53] identified the vulnerable regions for possible floods and droughts and helped concerned authorities to take effective measures to prevent the occurrence of the disasters. The historical information about the occurrences of the disasters was emphasized to make effective strategies to prevent the occurrence of flooding events by Wan et al [54] and health issues related disasters by Shen et al [85].…”

“…Cloud computing has been used extensively in Goespatial processing for calculating various indexes, spatial and statistical processing and data analysis for better decision support. The computational task of calculating standardized precipitation index, drought index and vegetation index was carried out by Yu et al [52] within the Cloud environment. Zou [88] provided a Cloud solution of MapReduce for data analysis of massive RS data including the preprocessing such as radiometric correction, geometric correction, mosaic and fusion and information extraction processes such as classification, transformation and index calculation.…”

“…Ji et al [105] incorporated interactive mineral maps for visualization of results obtained after relevant data analysis. Yu et al [52] used the Cloud environment for visualization of processed data with mapping tools integrated into the system for better understanding of the results. The web-interface in the HCC developed by Huang et al [17] was capable of displaying the results obtained after spatial analysis or run of Geospatial models using maps.…”