Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/66177
標題: 不同統計方法對生態棲位模式之影響-以卡氏櫧分佈模式為例
The effects of different statistical methods on the ecological niche model-a case study of Castanopsis carlisii distribution models
作者: 凃俊豪
Tu, Chun-Hao
關鍵字: 遙感探測
Remote sensing
卡氏櫧
區別分析
抉擇樹
支持向量機
最大熵值法
規則式基因演算法
long-leaf chinkapins (Castanopsis carlisii)
discriminant analysis (DA)
decision tree (DT)
support vector machine (SVM)
maximum entropy (MAXENT)
BIOCLIM
and genetic algorithm for rule-set prediction (GARP)
出版社: 森林學系所
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H. Graham, A. Guisan and NCEAS Predicting Species Distributions Working Group (2008) Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14:763-773.
摘要: The predictions of species distribution has become a focus in ecology in recent years. We can use the ecological niche models combined with remote sensing (RS) and geographic information system (GIS) to obtain more species and environmental information over the area that hard to arrive due to the high complexity of topographic. The ecological niche models can also be applied in finding the suitable area for suitable tree species to assist the works of plantation, and it will very helpful for the serious problem of global climate changing. To have a robust predictive model, many studies have tested every element in the model, including variable selecting, sampling design, statistical method, etc, to find the best operational procedure for any kind of applications. This study were to evaluate the performance and applicability of six statistical algorithms, discriminant analysis (DA), decision tree (DT), support vector machine (SVM), maximum entropy (MAXENT), BIOCLIM, and genetic algorithm for rule-set prediction (GARP). We build the model with long-leaf chinkapins (Castanopsis carlisii, LLC) in the Huisun Forest Station in Taiwan. We use GIS to overlay the tree sample on the altitude, slope, aspect, terrain position, and SPOT-5 vegetation index layers, and establish six statistic model to predict the potential habitat of LLC. The results show that five of six models (except DA) have excellent predictive power, the Kappa values of four models were higher than 0.8. DA and BIOCLIM models can be built easier and quicker, so they can be chosen as the models in first step. The accuracies of DT and SVM models were decreased with the increasing of the number of background samples, means that these two models were hard to work well over the complex forest. But on the other hand, they still can be suitable in the applications of rare species and the works of image classification. GARP and MAXENT models output the predictive maps with continuous value, and they can be used in different kinds of applications with different thresholds. We must build the predictive model with the most appropriate data and methods according to the goal of study, that will be very useful for developing a robust model. In future, we can add more tree samples from central and south of Huisun area that were the high potential suitable habitat of LLC by current results. And we also can incorporate high spatial resolution data and hyperspectral images in the models, that will promote the accuracy and reliability of predictive models.
生態棲位模式在近年來有蓬勃的發展,結合航遙測技術與地理資訊系統建立生態棲位模式可快速模擬出未知地區的環境狀況,對於生態保育、天災預防或農業等領域上皆為非常有用之工具。於森林經營層面上,從森林調查、林地規劃到物種保育等作業,都需要了解森林之分佈概況,生態棲位模式也因而對森林經營也有莫大幫助。且現今氣候變遷問題嚴重,各國對於碳排放相當重視,於造林作業上,依適地適木原則執行,可有效提升造林成效,增加固碳效率,而此也有賴於應用生態棲位模式尋找樹種適生育環境來協助作業。生態棲位模式於近年來越來越重要,許多研究者皆陸續測試各種模式,以求找出最有效率的作業程序。本研究旨在測試數種近年來廣泛使用之統計模式於生態棲位模式上之執行成效與適用狀況,並藉由背景樣本數量之變化測試模式於複雜森林環境之適用性。本研究選用惠蓀林場之卡氏櫧 (Castanopsis carlisii) 分佈進行測試,將現場調查之卡氏櫧樣本疊合於海拔、坡度、坡向、坡面位置及SPOT-5導出之植生指標五項環境因子上,分析卡氏櫧適合生長之環境概況。並分別納入區別分析 (Discriminant Analysis, DA)、抉擇樹 (Decision Tree, DT)、支持向量機 (Support Vector Machine, SVM)、最大熵值法 (Maximum Entropy, MAXENT)、BIOCLIM與規則式基因演算法 (Genetic Algorithm for Rule-set Prediction, GARP) 六種統計模式推測卡氏櫧之潛在分佈區概況。推測結果顯示六種統計模式對於卡氏櫧潛在生育地之推測皆有相當良好的成果,除DA模式外,其餘模式之Kappa值可皆達到0.8以上。其中DA與BIOCLIM建立上較為簡便,可作為初步階段之推測使用。DT與SVM兩模式推測能力會隨背景樣本數量增加而降低,縮限推測範圍,但仍適合應用於稀有物種相關經營作業,亦能夠應用至影像分類、物種辨識等作業。MAXENT與GARP兩模式具有連續值之分佈,其可代表物種出現機率,應用上可根據不同須由調整機率門檻而得到不同之推測分佈。各種統計方法具有不同之演算優點,選用上須根據不同需求做最適當的選擇。而本研究現階段僅於林場東側取樣,模式推測上於林場中、南部皆為分佈高潛力區,未來亦可於該處取得更多樣本以對卡氏櫧有更完整的了解。亦可進一步使用高空間解析度或高光譜影像,其具有更高潛力區分出複雜森林內之細微物種差異,將有助於提升模式推測能力。
URI: http://hdl.handle.net/11455/66177
其他識別: U0005-0402201318202200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-0402201318202200
Appears in Collections:森林學系

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