Spatial Vegetation Patterns Determined by Decision Tree Analysis in the Tseng-Wen Watershed

• Main Authors: CHEN,YUAN-HAO, 陳元豪
• Other Authors: HONG,NIEN-MING
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/4t93m3

碩士 === 中國文化大學 === 地學研究所地理組 === 105 === The spatial distribution of plants is related to the climate and the environment. To assess the current situation, distribution and health status of forest resources are important issues in forest managements. Because of climate change, the increase of the natural disaster frequency and the change of the intensity of the natural disasters in Taiwan affect the changes of the vegetation and the ecosystem. This study explores the relation between environment factors and spectral characteristics of various plant populations in Tseng-Wen reservoir watershed. In the geographical area, the type of vegetation can be used as a representative of the ecosystem type; so we must first understand the plant population status and climate status of forest land, each forest community has its special growth conditions. Understand the status is helpful to assess the condition of the vegetation and to monitor forest resources. First, the study investigated the literature to explore the relevant factors of vegetation. Using topographical factors, meteorological factors, spectral characteristics and making statistical charts with various types of vegetation. The results show that it is difficult to distinguish or explain the spatial distribution of all the vegetation only by a single factor. Therefore, the study use a variety of data for vegetation classification, including the Maximum Likelihood Classifier Supervised and Decision Tree. The results show that the kappa value is 0.41, which indicates that the spectral information is not enough to meet the classification of the vegetation when only use the maximum likelihood classifier supervised. Using the decision tree method with terrain, the index of the vegetation and the meteorological factors. In results, the correct rate of CART classification is 76.9%. The classification results also clearly indicate the classification rules and the importance of the factors which importance from high to low is terrain, annual temperature difference, summer temperature, rainfall. In future, vegetation classification can be followed by the maximum likelihood classifier method and then combined with the decision tree practice. And use terrain, climatic factors to predict and trace the space changes of vegetation, to understand the long-term sequence changes of the vegetation in the catchment area.