請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/65881
標題: 應用GIS及多變量統計於惠蓀林場卡氏櫧與木荷潛在生育地之推估
Application of GIS and Multivariate Statistics to Predict the Potential Habitats of Castanopsis calesii and Schima superba var. superba in Huisun Forest Station
作者: 許浩銓
Shu, How-Chuan
關鍵字: 地球空間資訊系統
Geo-spatial Information System (GIS)
邏輯思複迴歸 (LMR)
分類迴歸樹 ( CART)
等權模式
非等權卡方模式
潛在生育地
Logistic Multiple Regression (LMR)
Classification and Regression Tree (CART)
Equal Weight Model
Unequal Weight Model
Potential Habitat
出版社: 森林學系所
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摘要: 卡氏櫧的種子為動物的重要食物來源之一,故其在生態體系上具顯著之意義和價值,似更甚於過往所具之經濟價值,分布於臺灣中、高海拔地區,樹形高大,為中海拔優勢樹種。木荷分布於全臺中、低海拔闊葉林內常見之樹種,其木材細緻,為優良闊葉樹種之ㄧ,故成為著名的家具用材。植物在生態環境上的分布,是植物和環境間交互作用後產生的結果。然而環境可細分為多種生育地因子,各環境因子間作用下形成該種植物所需要的生育地。大多數研究將地球空間資訊系統 (Geo-spatial Information System, GIS) 結合統計應用在瀕危珍稀動植物生育地之模擬,反而少有用在廣泛分布型的一般植物上,所以本研究選擇卡氏櫧與木荷為探討對象,以瞭解GIS方法的適用性。本研究以GIS與統計整合分析卡氏櫧與木荷於海拔、坡度、坡向及坡面位置等四個地文因子之空間分布特性,並評估四因子對其生育地之相對重要性。研究目標係以這四個因子建立分類迴歸樹 (Classification And Regression Tree, CART)、邏輯思複迴歸 (Logistic Multiple Regression, LMR)、等權、非等權四種模式,用以推測惠蓀林場試區卡氏櫧與木荷之潛在生育地,並比較四者的推測準確度與執行效率,從而決定最佳模式。準確度評估顯示CART和LMR模式推測準確度相近,然而二者明顯高於等權和非等權模式。CART及LMR模式的效率遠優於等權和非等權模式,後兩者皆耗時費力。因此就準確度評估及效率而言,CART 和LMR模式同屬優等,明顯優於同屬另一等級之等權與非等權模式。若純就準確度評估結果來看,四模式皆可用在廣泛型分布卡氏櫧與木荷潛在生育地之推測,惟因驗模與建模樣本都來自同一區位難免受空間自相關之影響,加上驗模組樣本數尚非充足,故此論點仍存疑而有所保留。因此,建議後續研究使用來自不同區位之獨立樣本執行驗證,以確認模式之可靠性。
Long-leaf chinkapin (Castanopsis carlesii) has its significance and value in the ecological system, and its value in ecology seems more important than its economic value in the past because the seeds of the tree species are one of the important food sources for the animal. Long-leaf chinkapins, normally with big tree size, distribute from the medium to high elevation areas and become a dominating species over these areas in Taiwan. Chinese guger-tree (Schima superba var. superba) is one of the fine broad-leaf tree species, having economic value. It is a good wood with light red color and is good for fitment. Chinese guger-trees distribute from low to medium elevation areas in Taiwan. The distribution of plants on the ecological environment resulted from the interaction between plants and environment, and the environment could be divided into several habitant factors. All habitant factors act to form the plant habitat. Most studies have applied a geo-spatial information system (GIS) combined with statistical techniques to model the habitat of the endangered rare species of either plant or animal, whereas a limited number of studies have done the same work on the common tree species with wide-distribution pattern. Therefore, long-leaf chinkapin and Chinese guger-tree were chosen as a target for the study to understand the applicability of the GIS techniques. The study examined the spatial distribution characteristics of the two species on the four topographic factors including elevation, slope, aspect, and terrain position, as well as assessed their relative importance to the chinkapin's and guger-tree's habitat. The research objective was to build the classification and regression tree (CART) model, logistic multiple regression (LMR) model, equal weight model, and unequal weight model for predicting the potential habitat of the two species in the Huisun study area, and to determine the best one in terms of accuracy and efficiency. Accuracy assessment results indicated that the accuracies of the CART and LMR models are much higher than those of the equal-weight and unequal-weight models. Furthermore, CART and LMR are more efficient in implementation than the other two models that are difficult to implement and thus are labor-intensive, time-consuming. Thus CART and LMR models rated as the same good class are much better than equal-weight and unequal-weight models rated as another poor class in terms of accuracy and efficiency. Hence, the four models are suitable for predicting the potential habitat of widely distributed chinkapins and guger-tree in terms of model's accuracy. However, the conclusion remains questionable because spatial autocorrelation biasing accuracy assessment may exist between the sample sets of model development and model validation chosen from the same area and because the samples for model validation are likely to be insufficient. Thus model validation shall be done with independent samples chosen from the different areas far away from the areas of model development samples but in the same study area in follow-up studies so that the reliability of model can be confirmed.
URI: http://hdl.handle.net/11455/65881
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0108200716543200
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