Rainfall and Landslide Correlation Analysis and Prediction of Future Rainfall Base on Climate Change

The aim of this study is to analyze the quantitative relationship between the volume of rainfall and landslide occurrence in South Korea. To predict future rainfall, a future climate scenario was developed by downscaling the regional climate model (RCM) from the global climate model (GCM) based on the Intergovernmental Panel on Climate Change (IPCC) A1B scenario. In this study, for a quantitative analysis of correlation between rainfall and landslides occurrence, data on rainfall and landslides in Korea in the 2000s was analyzed using the correlation between the occurrence of landslides and rainfall volume (daily and accumulated) and the maximum hourly intensity of rainfall. Daily rainfalls exceeding 164.5 mm is categorized as high risk for landslide. A rainfall that continued for 3 days was found to affect the occurrence of landslide in Korea in the 2000s more than any other number of days during which rainfall lasted. The research area for the future climate change scenarios (A1B) covers the entire area of South Korea. Annual average rainfall had increased by 271.23 mm during 1971–2100. The development of downscaling method using GIS and verification with observed data could reduce the uncertainty of future climate change projection

Comparisons of Multi Resolution Based AI Training Data and Algorithms Using Remote Sensing Focus on Landcover

The purpose of this study was to construct artificial intelligence (AI) training datasets based on multi-resolution remote sensing and analyze the results through learning algorithms in an attempt to apply machine learning efficiently to (quasi) real-time changing landcover data. Multi-resolution datasets of landcover at 0.51- and 10-m resolution were constructed from aerial and satellite images obtained from the Sentinel-2 mission. Aerial image data (a total of 49,700 data sets) and satellite image data (300 data sets) were constructed to achieve 50,000 multi-resolution datasets. In addition, raw data were compiled as metadata in JavaScript Objection Notation format for use as reference material. To minimize data errors, a two-step verification process was performed consisting of data refinement and data annotation to improve the quality of the machine learning datasets. SegNet, U-Net, and DeeplabV3+ algorithms were applied to the datasets; the results showed accuracy levels of 71.5%, 77.8%, and 76.3% for aerial image datasets and 88.4%, 91.4%, and 85.8% for satellite image datasets, respectively. Of the landcover categories, the forest category had the highest accuracy. The landcover datasets for AI training constructed in this study provide a helpful reference in the field of landcover classification and change detection using AI. Specifically, the datasets for AI training are applicable to large-scale landcover studies, including those targeting the entirety of Korea

Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology

The purpose of this study was to analyze geospatial information (GI) research trends using text-mining techniques. Data were collected from 869 papers found in the Korea Citation Index (KCI) database (DB). Keywords extracted from these papers were classified into 13 GI domains and 13 research domains. We conducted basic statistical analyses (e.g., frequency and time series analyses) and network analyses, using such measures as frequency, degree, closeness centrality, and betweenness centrality, on the resulting domains. We subdivided the most frequent GI domain for more detailed analysis. Such processes permit an analysis of the relationships between the Research Fields associated with each GI. Our time series analysis found that the Climate and Satellite Image domain frequencies continuously increased. Satellite Image, General-Purpose Map, and Natural Disaster Map in the GI domain and Climate and Natural Disaster in the Research Field domain appeared in the center of the GI-Research Field network. We subdivided the Satellite Image domain for detailed analysis. As a result, LANDSAT, KOMPSAT, and MODIS displayed high scores on the frequency, degree, closeness centrality, and betweenness centrality indices. These results will be useful in GI-based interdisciplinary research and the selection of new research themes

Flood susceptibility mapping using integrated bivariate and multivariate statistical models

Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %

Habitat Mapping of the Leopard Cat (Prionailurus bengalensis) in South Korea Using GIS

The purpose of this study was to create maps of potentially sustainable leopard cat (Prionailurus bengalensis) habitats for all of South Korea. The leopard cat, which is on the International Union for Conservation of Nature (IUCN) Red List, is the only member of the Felidae family in Korea. To create habitat potential maps, we selected various environmental factors potentially affecting the species’ distribution from a spatial database derived from geographic information system (GIS) data: elevation, slope, distance from a forest stand, road, or drainage, timber type, age, and land cover. We analyzed the spatial relationships between the distribution of the leopard cat and the environmental factors using a frequency ratio model and a logistic regression model. We then overlaid these relationships to produce a habitat potential map with a species potential index (SPI) value. Of the total number of known leopard cat locations, we used 50% for mapping and the remaining 50% for model validation. Our models were relatively successful and showed a high level of accuracy during model validation with existing locations (frequency ratio model 82.15%; logistic regression model 81.48%). The maps can be used to manage and monitor the habitat of mammal species and top predators

Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea

Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future

Rapid Change Detection of Flood Affected Area after Collapse of the Laos Xe-Pian Xe-Namnoy Dam Using Sentinel-1 GRD Data

Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and flooded area caused by the collapse of the Xe-Pian Xe-Namnoy Dam in Laos on 23 July 2018, using Sentinel-1 ground range detected (GRD) images. The collapse of this dam gained worldwide attention and led to a large number of casualties at least 98 people, as well as enormous economic losses. Thus, it is worth noting that this study quantitatively analyzed changes in both the Hinlat area, which was flooded, and the Xe-Namnoy reservoir. This study aims to suggest a practical method of change detection which is to simply compute flood extent and water volume in rapidly analysis. At first, a α -stable distribution was fitted to intensity histogram for removing the non-water-affected pixels. This fitting differs from other typical histogram fitting methods, which is applicable to histograms with two peaks, as it can be applied to histograms with not only two peaks but also one peak. Next, another type of threshold based on digital elevation model (DEM) data was used to correct for residual noise, such as speckle noise. The results revealed that about 2.2 × 108 m3 water overflowed from the Xe-Namnoy reservoir, and a flooded area of about 28.1 km3 was detected in the Hinlat area shortly after the dam collapse. Furthermore, the water quantity and flooded area decreased in both study areas over time. Because only SAR GRD images were used in this study for rapid change detection, it is possible that more accurate results could be obtained using other available data, such as optical images with high spatial resolution like KOMPSAT-3, and in-situ data collected at the same time

Habitat Mapping of the Leopard Cat (Prionailurus bengalensis) in South Korea Using GIS

The purpose of this study was to create maps of potentially sustainable leopard cat (Prionailurus bengalensis) habitats for all of South Korea. The leopard cat, which is on the International Union for Conservation of Nature (IUCN) Red List, is the only member of the Felidae family in Korea. To create habitat potential maps, we selected various environmental factors potentially affecting the species’ distribution from a spatial database derived from geographic information system (GIS) data: elevation, slope, distance from a forest stand, road, or drainage, timber type, age, and land cover. We analyzed the spatial relationships between the distribution of the leopard cat and the environmental factors using a frequency ratio model and a logistic regression model. We then overlaid these relationships to produce a habitat potential map with a species potential index (SPI) value. Of the total number of known leopard cat locations, we used 50% for mapping and the remaining 50% for model validation. Our models were relatively successful and showed a high level of accuracy during model validation with existing locations (frequency ratio model 82.15%; logistic regression model 81.48%). The maps can be used to manage and monitor the habitat of mammal species and top predators

Habitat Potential Mapping of Marten (Martes flavigula) and Leopard Cat (Prionailurus bengalensis) in South Korea Using Artificial Neural Network Machine Learning

This study developed habitat potential maps for the marten (Martes flavigula) and leopard cat (Prionailurus bengalensis) in South Korea. Both species are registered on the Red List of the International Union for Conservation of Nature, which means that they need to be managed properly. Various factors influencing the habitat distributions of the marten and leopard were identified to create habitat potential maps, including elevation, slope, timber type and age, land cover, and distances from a forest stand, road, or drainage. A spatial database for each species was constructed by preprocessing Geographic Information System (GIS) data, and the spatial relationship between the distribution of leopard cats and environmental factors was analyzed using an artificial neural network (ANN) model. This process used half of the existing habitat location data for the marten and leopard cat for training. Habitat potential maps were then created considering the relationships. Using the remaining half of the habitat location data for each species, the model was validated. The results of the model were relatively successful, predicting approximately 85% for the marten and approximately 87% for the leopard cat. Therefore, the habitat potential maps can be used for monitoring the habitats of both species and managing these habitats effectively