Spatial Extrapolation: 3S Coupled with GARP and Decision Tree for Predicting the Suitable Habitat of Castanopsis carlesii
- 作者：C. H. TuN. J. LoW. I. ChangK. Y. Huang
- 会议时间：2011-08-01
- 关键词：Long-leaf chinkapin ; Genetic Algorithm for Rule-set Prediction (GARP) ; Decision Tree (DT)
- 作者单位：C. H. Tu,K. Y. Huang(Dept. Of Forestry, Chung-Hsing University, Taichung 402, Taiwan)N. J. Lo(Experimental Forest Management Office, Chung-Hsing University, Taichung 402, Taiwan)W. I. Chang(Forest Planning Section, Forest Bureau, Council of Agriculture, Taipei 100, Taiwan)
- 母体文献：第五届海峡两岸遥感遥测会议论文集
- 会议名称：第五届海峡两岸遥感遥测会议
- 会议地点：哈尔滨
- 主办单位：中国地理学会
- 语种：chi
- 分类号：P34;S5
摘要
Spatial extrapolation has always been a part of ecology, but it became a sine qua non and a major research focus in applied ecology in the latter half of the 20th century. Technological innovations over the last few decades, especially in the fields of remote sensing and GIS,have greatly enhanced scientists ’ ability to describe patterns over broader spatial scales and at a greater level of detail. From the standpoint of ecology, long-leaf chinkapin (Castanopsis carlesii), whose seeds have long been identified as an important food source for animals in the Huisun study area, thus was chosen as a target for the study. This study attempted to predict the potential habitat of the species accurately by coupling 3S technology and multivariate statistics. The layer of tree samples collected with GPS was overlaid on the layers of vegetation indices derived from SPOT images, elevation, slope, aspect, and terrain position to analyze its spatial distribution in a GIS. Genetic algorithm for rule-set prediction (GARP), decision tree (DT), and discriminant analysis (DA) models were developed to predict its potential habitat and evaluate the effect of vegetation indices on model accuracy. Accuracy assessment results indicated that the accuracy of DT model was the best, followed by GARP model, and the DA was the worst since DA is based upon several statistical assumptions. DT and GARP models greatly reduced the area of field survey to less than 10% of the entire study area at the first stage, and thus they were better suited than DA for potential habitat modeling. However, further confirmation will have to be done by using independent datasets of the species taken from the Kuan-Dau watershed, far away from the Tong-Feng watershed. These three models were efficient in implementation of model development and validation.