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| 題 名 | 評估兩種取樣設計對於香桂適生育地模式預測能力之影響=The Effects of Different Sampling Designs on the Ability of Model for Predicting the Suitable Habitat of Cinnamomum subavenium |
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| 作 者 | 羅南璋; 張偉顗; 黃凱易; | 書刊名 | 林業研究季刊 |
| 卷 期 | 34:3 2012.09[民101.09] |
| 頁 次 | 頁193-214 |
| 分類號 | 436.19 |
| 關鍵詞 | 香桂; 地理資訊系統; 遙感探測; 全球定位系統; 數值高程模型; 抉擇樹; 邏輯思複迴歸; 區別分析; 適生育地; Randaishan cinnamons; Geographic information system; GIS; Remote sensing; Global positioning system; GPS; Digital elevation model; DEM; Decision tree; DT; Logistic multiple regression; LMR; Discriminant analysis; DA; Suitable habitat; |
| 語 文 | 中文(Chinese) |
| 中文摘要 | 香桂 (Cinnamomum subavenium Miq.) 為常綠闊葉樹種,廣泛分布於台灣中、南部山區。本研究矩形試區位於台灣中部,涵蓋惠蓀林場,香桂為此試區的優勢樹種之一,故選為研究的對象。研究目標係藉GIS疊合GPS定位之香桂樣株圖層與海拔、坡度、坡向、坡面位置及SPOT-5影像導出植生指標圖層,協同多變量統計模擬試區香桂之空間分布型態。研究建立邏輯思複迴歸 (LMR)、抉擇樹 (DT) 及區別分析 (DA) 三種模式,預測並繪製全區的香桂適生育地。建模與驗模採兩種取樣設計,兩者建模樣本相同,取自東峰溪流域,惟驗模樣本分別取自東峰溪與關刀溪兩流域。準確度評估顯示,DT優於LMR,而前二者又遠優於DA。三者於建模、驗模與繪圖之執行效率相當。重要的 是DT和LMR於首次模擬,大幅縮小實地調查面積,節省可觀的經費及人力,故兩者更適用於香桂適生育地之模擬。SPOT-5影像導出植生指標改善模式預測能力效用很小,乃因其光譜及空間解析度皆不足,無法分辨散生香桂。三種統計法建立「東峰模式」雖都通過東峰驗模組檢測,但皆未通過關刀驗模組檢測,凸顯此模式無法僅透過地形變數跨越空間精確外推無建模樣本區域。未來研究將嘗試從高空間、高光譜解析度遙測資料萃取物種光譜資訊作建模用變數,期能跨越空間精確外推無建模樣本區域。 |
| 英文摘要 | Randaishan cinnamons (Cinnamomum subavenium Miq.), one of the evergreen broad-leavedtree species, are generally distributed in central and southern Taiwan. The species was chosen as targetfor this study because it is one of the dominant species in the Huisun study area in central Taiwan. GIStechnique was applied to overlay the tree samples positioned by GPS on the layers of elevation, slope,aspect, terrain position, and vegetation indices derived from SPOT-5 images for modeling the tree’s suitablehabitat. Decision tree (DT), discriminant analysis (DA), and logistic multiple regression (LMR) modelswere developed to predict and map the tree’s suitable sites in the study area, and to determine the optimumone in terms of accuracy and efficiency. Two sampling designs were created for model development andvalidation. They used the same set of training samples from Tong-Feng watershed for model developmentbut different sets of test samples for model validation, one from Tong-Feng and the other from Guan-Dauwatershed. Accuracy assessment showed that the accuracy of DT was slightly better than that of LMR,accuracies of the two models were much better than that of DA; and the three models were highly efficientin implementation of model development and validation. More importantly, DT and LMR can be appliedto predict the tree’s suitable habitat because they greatly reduced the area of field survey to 4-7 % of theentire study area at the first stage. Vegetation indices derived from SPOT-5 images could not improvethe predicting ability of models for the widely distributed species because of SPOT imagery lacking finespectral resolution and spatial resolution. The “Tong-Feng models” developed from three methods failedto pass validation by Guan-Dau test samples despite passing validation by Tong-Feng test samples. Theoutcome emphasized that the “Tong-Feng models” only based on topographic variables could not performspatial extrapolation accurately from a smaller area with training data to a larger area without any trainingdata. Follow-up studies will attempt to extract spectral information associated with the species from highspatial, spectral resolution remotely sensed data and use it as variable for model development so that theability of spatial extrapolation with a model can be improved. |
本系統中英文摘要資訊取自各篇刊載內容。