臺灣博碩士論文加值系統

物種分布模式 (species distribution model, SDM) 的前瞻性能在生態保育、生物決策層面等有重要貢獻而被廣泛使用。SDM研究多半考慮與物種分布有直接因果關係的環境因子，但因其獲取困難或成本高或數量稀少，故以遙測獲取之地文因子數據作為環境替代因子較為合適。地形濕度指標 (topographic wetness index, TWI) 是一種描述局部地形水文累積的指標，地形水文與地表表面水流、土壤濕度有密切關係；太陽輻射指標 (Solar radiation index, SRI) 則是一種表現太陽輻射於局部地區的標的，太陽輻射直接影響地表的溫度、水氣狀態，間接改變該地區的微氣候；兩者進而對植物生長有重大影響。本研究試圖納入土壤水分的替代指標TWI (包含單流向演算，Single flow direction，TWI-S以及多流向演算，multiple flow direction，TWI-M) 及太陽輻射的替代指標SRI 並採用經常使用地文因子以及統計分類演算法如區別分析 (Discriminant Analysis, DA)、抉擇樹 (Decision Tree, DT)、最大熵值法 (Maximum Entropy, MaxEnt)、支持向量機 (Support Vector Machine, SVM) 等四模式，模擬台灣中部地區惠蓀林場內的薯豆 (Elaeocarpus japonicas)、台灣杜鵑 (Rhododendron formosanum)、木荷 (Schima superba) 的適生育地分布。此外，以非線性內插與普通克利金內插 DEM 產出的TWI (與 krig-TWI)、SRI (與 krig-SRI) 對三物種分布模擬的影響。結果顯示，SRI 對模式模擬精度沒有顯著改善，krig-SRI 則不然。TWI-M對於提升模擬精度較有潛力，TWI-S則半數使精度持平。DT、DA的檢測中，krig-TWI-S、krig-TWI-M、krig-SRI 三者只有krig-SRI對模擬精度有顯著提升效果；krig-TWI-S 雖有提升，但沒有達顯著。四模式中，薯豆以MaxEnt、DT最佳，其次DA，SVM最差。台灣杜鵑，以MaxEnt最佳，其次SVM及DT，最低DA。木荷以MaxEnt最佳，其次DT，再其次DA，SVM最差。三樹種的MaxEnt皆有最佳表現，能較正確描述三物種分布；SVM在薯豆、木荷預測最差，但在台灣杜鵑與DT同等級。重要的是，TWI於統計值上能表現出三物種的環境狀況，其中TWI-M較TWI-S更穩健，不易受DEM內插方式的影響。本研究證實TWI、krig-SRI可以改善SDM的預測精確度，然改善幅度在各模式間不同，以放寬模式 (如DA) 或是特異度高的模式 (如SVM) 表現優異，但在MaxEnt、DT 等嚴謹的模式中，精度上升較小且幅度有限。物種分布模擬需由迭代方式實施，且首輪模擬優先使用地文變數，於首輪結果中縮小實地調查面積，針對優先區域來獲取環境因子作為後續實施SDM模擬的輸入變數。此迭代方式的模擬在森林生態上改善SDM實屬必要。

Species distribution models (SDMs) are widely used in ecological conservation and decision-making of biological resource management because of SDM’s prospective and great contribution. In most SDMs study, researchers probably usually consider causal factors. However, data for such factors are expensive or even difficult to collect. Instead, it is proper to use Terrain-related variables which can be easily obtained by remote sensing as proxies of ecological factors. Topographic wetness index (TWI) is one kind of measurements that can represent and control the local situation of topography on hydrological processes; solar radiation index (SRI) is a proxy factor that indicate local amount of solar radiation, which affects the ground surface temperature and moisture, and lead to different micro-climate. They both are important to plant growth. Thus, this study take TWI and SRI as new predictors into the model which developed with discriminant analysis (DA), decision tree (DT), maximum entropy (MaxEnt), and support vector machine (SVM), in order to predict the potential habitats of three species, which are Schima superba var. superba (Chinese guger tree, CGT), Rhododendron formosanum (RF) and Elaeocarpus japonica (JE) in the Huisun Experimental Forest Station (HEFS) in central Taiwan. Besides, it also applied two interpolated methods into DEM to regenerate TWI (called krig-TWI), SRI (called krig-SRI) and tried to evaluate the effects of TWI, krig-TWI, SRI and krig-SRI on the performance of SDMs. The results show that SRI did not promote accuracy of model, while krig-SRI did. TWI-M potentially raised the accuracy of model, while TWI-S did not advance the accuracy in most model. In DT and DA model, such significantly improved as krig-SRI be, krig-TWI-S did not come up with it, and either did krig-TWI-M. Among the performance of four models, MaxEnt is the best for three species; DT is second for RF and CGT; SVM is worst for JE and CGT, but it is as second as DT for RF. More importantly, TWI value can represent the ecological traits of three target species statistically, and agrees with our observations from field surveys. TWI-M is more robust than TWI-S due to TWI-M is quiet not affected by different interpolation. Consequently, this study confirms that TWI and krig-SRI can somehow improve the performance of SDMs, where they are different in any single type of model result. In models with high specificity (like SVM) or with high omission error (like DA) pefromed well, while models with strict rule (like MaxEnt and DT) did not arise apparently. SDM must be considered to run on an iterative basis with topographic data in initial rounds, and thereby saving both cost and labor. This process is inevitable in impoving the predictive performance of SDM in mountain area.

【摘要】-----------------------------------------i
ABSTRACT----------------------------------------ii
目　次-------------------------------------------iii
圖目次-------------------------------------------vii
表目次-------------------------------------------ix
第一章　前　言------------------------------------1
第二章　前人研究----------------------------------5
2.1 物種分布研究程序------------------------------5
2.2 生育地及與物種分布模擬關係---------------------7
2.2.1 平衡與非平衡--------------------------------7
2.2.2 生育地與生態棲位----------------------------7
2.2.3 生態棲位-----------------------------------8
2.2.4 生態棲位與物種分布模擬類型-------------------10
2.3 影響模式表現的主要因子-------------------------11
2.3.1 環境因子及其分類----------------------------13
2.3.2 SDM特性與地文替代變數-----------------------14
2.4 目標變數與樹種-------------------------------16
2.5 地形濕度指數及地形遮蔭------------------------18
2.5.1 地形濕度指數-------------------------------18
2.5.1.1 地形濕度指數演算--------------------------19
2.5.1.2 地形濕度指數特性--------------------------22
2.5.2 地形遮蔭-----------------------------------22
2.5.2.1 地形遮蔭計算-----------------------------23
2.6 物種分布模式演算法的選擇及應用案例-------------25
2.6.1 多模式的演算-------------------------------25
2.6.2 模式迭代操作-------------------------------25
2.7 模式驗證評估---------------------------------26
第三章　研究目標、區域及物種-----------------------28
3.1 研究區域------------------------------------28
3.1.1 研究區域範圍------------------------------28
3.1.2 地形-------------------------------------28
3.1.3 立地條件與土壤資料-------------------------29
3.1.4 氣候與氣象、雨量資料------------------------30
3.1.5 土地利用-----------------------------------31
3.1.6 林場內主要林相------------------------------31
3.2 目標樹種-------------------------------------32
3.2.1 薯豆---------------------------------------32
3.2.2 台灣杜鵑-----------------------------------32
3.2.3 木荷---------------------------------------33
第四章 材料與方法---------------------------------34
4.1 研究架構-------------------------------------34
4.2 資料收集-------------------------------------35
4.2.1 數值高程模型--------------------------------35
4.2.2 像片基本圖----------------------------------35
4.2.3 現場定位目標樹種之數據------------------------35
4.2.4 研究使用的軟體、儀器設備----------------------36
4.3 資料處理--------------------------------------37
4.3.1 海拔---------------------------------------37
4.3.2 坡度---------------------------------------38
4.3.3 坡面位置-------------------------------------38
4.3.4 地形濕度指數---------------------------------40
4.3.5 地形遮蔭-------------------------------------42
4.4 點位資料處理及取樣設計--------------------------43
4.4.1 點位資料處理---------------------------------43
4.4.2 取樣設計------------------------------------43
4.5 圖層疊合及樣本切取-----------------------------44
4.6 模式、新變數功效檢測設計------------------------44
4.7 物種分布模式建立-------------------------------45
4.7.1 區別分析法 (DA)-----------------------------46
4.7.2 抉擇樹 (DT)---------------------------------47
4.7.3 最大熵值法 (MAXNT)---------------------------47
4.7.4 支持向量機 (Support Vector Machine, SVM)-----48
4.8 模式驗證指標-----------------------------------50
4.8.1 Kappa 指標----------------------------------51
4.8.2 整體精度 (OA)--------------------------------51
4.8.3 真技術統計 (TSS)-----------------------------51
第五章　結果與討論----------------------------------52
5.1 樹種點位的地文因子統計--------------------------52
5.1.1 研究區域------------------------------------52
5.1.1 薯豆----------------------------------------53
5.1.2 台灣杜鵑------------------------------------55
5.1.3 木荷----------------------------------------56
5.2. 預測變數組合----------------------------------58
5.2.1 薯豆----------------------------------------59
5.2.2 台灣杜鵑------------------------------------60
5.2.3 木荷---------------------------------------62
5.3 網格解析度對地形濕度指數 (TWI) 的影響------------63
5.3.1 六個地文因子在兩種內插法下的變化--------------64
5.3.1.1 薯豆-------------------------------------65
5.3.1.2 台灣杜鵑----------------------------------66
5.3.1.3 木荷-------------------------------------67
5.3.1.4 三個樹種----------------------------------68
5.3.2 目標樹種模擬的精度----------------------------69
5.3.2.1 薯豆--------------------------------------69
5.3.2.2 台灣杜鵑-----------------------------------71
5.3.2.3 木荷---------------------------------------72
5.3.3 克利金處理的影響------------------------------74
5.3.4. 三物種克利金處理後TWI、SRI對模擬的效果---------75
5.4 物種生態特性------------------------------------78
5.5 各模式效力排序與最佳變數組合----------------------81
5.5.1 各物種最佳模式與最佳組合模擬圖------------------82
第六章　結論與建議-----------------------------------86
6.1 結論------------------------------------------86
6.2 建議------------------------------------------87
第七章　參考文獻------------------------------------89
附錄-----------------------------------------------99
附錄1 樣本座標及其各項地文因子數值-------------------99
附錄1-1 薯豆樣木及各項地文因子----------------------99
附錄1-2 台灣杜鵑樣木及各項地文因子------------------105
附錄1-3 木荷樣木及各項地文因子----------------------110
附錄2 兩種內插處理後的六變數組合kappa精度成對比較表---115
附錄3 三物種於四模式六種組合中各自表現最佳生育地-------117
附錄3-1 薯豆--------------------------------------117
附錄3-2 台灣杜鵑----------------------------------118
附錄3-3 木荷--------------------------------------118

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