Predicting Land Use Changes Using Dyna-CLUE Model in Shihmen Sub-Watershed Taiwan

Abstract

Anthropogenic activities combined with the physiographical attributes in the watershed can largely affect the land use change pattern Land use change in one hand indicates the human development whereas in other hand if overexploited cause harm to ecosystem causing global warming climate change or even trigger natural disasters The purposes of this study are: (1) to analyze the relationship between driving factors and their response to land use change and (2) to investigate the possible land use changes in Shihmen sub-watershed by using Dynamic Conversion of Land Use and its Effects (Dyna-CLUE) model Firstly the land use pattern in 2004 is recognized as the base map from Landsat satellite image by using a classification algorithm Five different land use types are classified which are water forest built-up grassland and bare land Secondly a logistic regression model is built to quantify the relationship between estimated driving factors and land use types Simple linear extrapolation method is adopted to calculate the future demand of each land use types Then spatial allocation model Dyna-CLUE is used to simulate the evolvement of land use pattern from 2004 to 2011 and the parameters of Dyna-CLUE (i e conversion elasticities for various land use types) are tuned to match with the observed land use map in 2011 Conversion elasticities of 1 0 95 0 8 0 3 and 0 05 for water forest built-up grassland and bare land respectively give the most reliable result Relative operating characteristics (ROC) curve kappa and accuracy indices are selected for statistical validation of the generated maps Finally the study used tuned Dyna-CLUE to project the land use pattern in 2020 for four different scenarios: (1) Linear trend of land use demand without restriction areas (2) Linear trend of land use demand with restriction areas (3) Input of minimum and maximum time steps of conversion sequence in the conversion matrix and (4) Higher rate of land transformation All the results show an increase in built-up and bare land area on the expenses of forest and grassland area in 2020

Date of Award	2017 Aug 15
Original language	English
Supervisor	Pao-Shan Yu (Supervisor)