No abstract

Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced extensive damage to life and property almost every year due to a levee breach resulting from typhoons and heavy rainfall during the summer monsoon season. Recently, Hoeryeong City (2016) experienced heavy rain during Typhoon Lionrock, and the resulting flood killed and injured many people (68,900) and destroyed numerous buildings and settlements (11,600). The NK state media described it as the most significant national disaster since 1945. Thus, almost all annual repeat occurrences of floods in NK have had a severe impact, which makes it necessary to figure out the extent of floods to restore the damaged environment. However, this is difficult due to inaccessibility. Under such a situation, optical remote sensing (RS) data and radar RS data along with a logistic regression were utilized in this study to develop modeling for flood-damaged area delineation. High-resolution web-based satellite imagery was also interpreted to confirm the results of the study.

Abstract:This review was to determine a standard post-fire restoration strategy for use in South Korea according to the magnitude of the damage and the condition of the affected site. The government has strongly enforced reforestation in deforested areas as well as fire prevention and suppression since the 1960s. These efforts have successfully recovered dense even-aged forests over the last five decades. However, high fuel loading and the homogeneous structure have made forests vulnerable to large fires. In recent years, large forest fires have occurred in the eastern coastal region of Korea. Forest fires can significantly influence the economic and social activities of the residents of such affected forest regions. Burned areas may require urgent and long-term restoration strategies, depending on the condition of the affected site. Erosion control is the most important component of an urgent restoration and should be completed before a rainy season to prevent secondary damage such as landslides and sediment runoff in burned areas. Long-term restoration is necessary to renew forest functions such as timber production, water conservation, ecosystem conservation, and recreation for residents. Sound restoration for burned areas is critical for restoring healthy ecological functions of forests and providing economic incentives to local residents.

Every summer, North Korea (NK) suffers from floods, resulting in decreased agricultural production and huge economic loss. Besides meteorological reasons, several factors can accelerate flood damage. Environmental studies about NK are difficult because NK is inaccessible due to the division of Korea. Remote sensing (RS) can be used to delineate flood inundated areas in inaccessible regions such as NK. The objective of this study was to investigate the spatial characteristics of flood susceptible areas (FSAs) using multi-temporal RS data and digital elevation model data. Such study will provide basic information to restore FSAs after reunification. Defining FSAs at the study site revealed that rice paddies with low elevation and low slope were the most susceptible areas to flood in NK. Numerous sediments from upper streams, especially streams through crop field areas on steeply sloped hills, might have been transported and deposited into stream channels, thus disturbing water flow. In conclusion, NK floods may have occurred not only due to meteorological factors but also due to inappropriate land use for flood management. In order to mitigate NK flood damage, reforestation is needed for terraced crop fields. In addition, drainage capacity for middle stream channel near rice paddies should be improved.