Alternative Titleインド・チャンバル渓谷におけるガリー侵食の起こりやすさの環境評価 : 地理空間情報および機械学習によるアプローチ
Note (General)The study of ravine and gully erosion is one of the significant aspects of environmental science. Ravine and gully erosion is the most hazardous form of land degradation caused by the water-induced soil erosion process. It may impact ecosystem function, soil productivity, water quality, crop failure, and the quality of human life surrounding it, well known as Badland. Especially in India, as an Agricultural country and high population country, Badland is the biggest threat to food security and economic development. Hence, the government and several national and international organizations are trying to manage and mitigate this problem through the ravine reclamation program. One of the most crucial parts of ravine reclamation is gully erosion assessment, erosion susceptibility, and the accurate estimation of its magnitude. Also, gully erosion assessment gained huge scientific and social interest owing to its severe consequences. Geospatial data with machine learning algorithms has been accepted as the most efficient and effective way to monitor ravine and gully erosion. In this study, the lower Chambal valley of the Indian ravine has been considered to study gully erosion susceptibility using geospatial data and machine learning methods. Chapter 1 focuses on the Introduction and background information on ravine and gully erosion, the motivation of this study, the goal and objective, and the content of this thesis. While Chapter 2 describes the study area i.e., the Bhind region in central India. This chapter gives details about the location of the area, geomorphology, geology, climate, flora and fauna, environmental condition, and socio-economic condition. It also includes information about ravines reclamation projects for Chambal ravines in India. Chapter 3 covers the literature review part, which synthesized and summarized the comprehensive review of methodologies applied for the Ravines and Gully erosion assessment. It also discusses the decadal change in satellite sensors and the advancement of methods and their pros and cons. The literature review was used to select the study area, data, and methodology to pursue the research. Chapter 4 focuses on gully erosion assessment through gully erosion volume changes analysis and erosion susceptibilities of Badland in Chambal, India using the multi-temporal TerraSAR-X DEM (TanDEM-X) dataset acquired for 2012 and 2017. This chapter addresses the quantification of gully erosion volume change with a framework to predict the gully erosion volumes and soil erosion rate in the area of interest. It also evaluates the factors that control gully erosion and maps the gully erosion susceptibilities. The result shows that about 40% of the area is highly affected by gully erosion, with the maximum gullying process in north-central and lowest in the west-south location of the testing area. Plus, the rate of gully erosion that causes volume change in the study area is 283 t ha-1 yr-1. The research framework presented in this study can be helpful in the erosion rate estimation of the Chambal ravine and other ravenous areas and can be utilized effectively in ravine reclamation projects. Chapter 5 of the thesis focuses on the effect of DEM (Digital Elevation Model) characteristics on machine learning in gully erosion susceptibility. It is toward developing a concept about the selection of the DEM and the suitable DEM resolution in gully erosion assessment. The study in this chapter reveals, the unexplored effect of the DEM resolution from different sources on the accuracy of gully erosion susceptibility mapping (GESM) using the Random Forest (RF) algorithm. The six different DEMs has been considered for this analysis are TanDEM-X (5m), SRTM (30m), ALOS PALSAR (12.5m), ASTER GDEM (30m), AW3D (30m), MERIT (90m). The 5m TanDEM-X confirmed the highest accuracy. However, the order of accuracy with respect to DEM resolution is TanDEM-X (5m)> AW3D (30m) > SRTM (30m)> ALOS PALSAR (12.5m)> MERIT (90m)> ASTER GDEM (30m). Hence, this evaluation predicted that the finer resolution of DEM data favors attending high accuracy to study GESM but not necessarily because the DEM source, type of sensors, and other satellite features are also influential in gaining good quality topographic data. Chapter 6 is the conclusion and the key finding of this study. This chapter includes the contribution of the study in scientific, environmental, and social aspects. It also includes the significance, novelty, and future recommendations of this study.
(主査) 准教授 Ram Avtar, 教授 渡邉 悌二, 教授 露崎 史朗, 准教授 石川 守, 准教授 早川 裕弌, 助教 Yunus P. Ali （Indian Institute of Science Education and Research Mohali )
環境科学院（環境起学専攻）
Collection (particular)国立国会図書館デジタルコレクション > デジタル化資料 > 博士論文
Date Accepted (W3CDTF)2023-03-02T11:18:11+09:00
Data Provider (Database)国立国会図書館 : 国立国会図書館デジタルコレクション