跨尺度分析與監測近熱帶人工林生長效應之研究

Cross scale analyses and monitoring growth of forest plantations in near tropical forests

鍾智昕(Chih-Hsin Chung)

指導教授 ： 黃倬英

國立臺灣大學/理學院/地理環境資源學研究所/博士(2017年)

https://doi.org/10.6342/NTU201702931

摘要

清楚與充分的瞭解森林的現況與生長趨勢，是提供森林經營管理者決定施行育林措施的重要依據。整合運用現代科技與科學方法，發展有效率可提供森林經營者跨空間和時間尺度獲取林分資料的工具，對於進行森林經營規劃與策略擬定有所必要。本研究嘗試評估森林水平方向與垂直方向的生長動態，瞭解近熱帶人工林生長歷程，以及林分受到人類經營管理所產生的影響。以森林調查資料為基礎，結合樹輪學、歷史事件分析、無人機系統，完成3個實證研究(1)臺灣南部大葉桃花心木徑向生長量季節性差異之研究、(2)應用歷史事件分析確立柳杉人工林行列疏伐的理想時間與(3)應用追算法與無人機系統評估樹高生長動態，建立跨時間與空間尺度模式，提供評估、預測的模式，以瞭解森林生長的動態。
(1) 本研究藉由創傷開窗法調查臺灣南部大葉桃花心木(Swietenia macrophylla)造林木，徑向生長量及其相對樹輪密度，在年度不同生長期之間的變化。研究期間自2008年12月到2009年11月，對試驗地樣木施行四次創傷開窗法，分析時將一年區分為四個階段，分別為2008年12月到2009年2月(P1)、2009年3月到2009年6月(P2)、2009年7月到2009年8月(P3)及2009年9月到2009年11月(P4)，藉此瞭解不同季節對樹木徑向生長量及樹輪密度的影響。結果顯示四個階段徑向生長量大小的變化為P3 > P4 > P2 > P1，相對的樹輪密度在四個階段的趨勢為P1 > P4 > P2 > P3，顯示較大的徑向生長量及較低的樹輪密度發生在潮濕高溫的7~8月，較小的生長量及較高的樹輪密度發生在低溫乾季的12~2月。變異數統計分析結果顯示，徑向生長量在不同季節之間有明顯的差異，雖然在樹輪密度表現上差異不顯著，透過取樣觀察端生薄壁細胞帶，形成時間在乾季的後期或雨季的早期，這個薄壁細胞帶在一個樹輪內的密度圖譜上會有最小的樹輪密度值。顯著正相關性存在於徑向生長量與氣候因素之間(包括平均每月氣溫、平均每月降水量及平均每月相對濕度)，然而，樹輪密度與氣候因素之間並沒有顯著相關存在，藉由複迴歸分析結果發現，平均每月相對濕度及平均每月日照時數是預測徑向生長量最好的參數。
(2)本研究選定以台灣棲蘭山地區，已完成行列疏伐(row thinning)作業的45年生柳杉(Cryptomeria japonica)人工林為研究對象。蒐集林分調查資料與鑽取樹芯(tree core)樣本，結合樹輪學方法及統計分析方法，完成確立疏伐促進林木生長時間的有效性，提供經營者制定經營疏伐作業的時間表。許多的經營措施中，疏伐是人工經營育林過程中的手段之一，目的是在提升輪伐期收穫時的林木生長與木材品質。疏伐後促進生長的有效時間，對於制定育林作業時間表是相當重要的資訊。徑向增加的百分率作是常做為評估疏伐的影響的依據，然而，由於樹木生長速率的變異，難以獲得一個明確的時間。本研究使用事件歷史分析量化，評估不同行列疏伐強度的最佳間隔時間。透過測量樹輪寬度(ring width)估計樹木每年生長，並將所得到的時間序列數據，轉化為斷面積增加量(basal area increment，BAI) 與存活資料，進行事件歷史分析，由分析所得到生存機率為50％的時間，做為適當的疏伐時間間隔的依據。此外，應用加速失敗迴歸模型(accelerated failure time model regression)來評估疏伐的時間效應，並透過模型驗證疏伐時間長短變化對人工林生長的影響。結果顯示，疏伐改變了林分的斷面積增加量，一般來說，在疏伐的寬度和促進生長的時間有效性之間，具有正相關的趨勢。模型驗證中，透過實際的樹輪資料，模擬不同長度時間的疏伐間隔與相對的樹木斷面積生長，證實藉由歷史事件分析的結果，可以精確的提供最佳的疏伐時間點。結合樹輪資料和事件歷史分析，可以有助於確定行列疏伐的時間間隔，提供改善森林林分的碳吸存。
(3)林分冠層高度是評估木材資源、物種多樣性、森林生物量和碳儲量的關鍵變數。樹高生長的監測對於瞭解生態過程是必需的資訊，但長期和大範圍的樹高測量非常耗時且耗費人力。透過現地測量是最常用以評估林分高度方法之一。然而，在林分冠層高大、冠幅寬闊且密集的森林中不容易獲得準確性數據。近年來，無人機系統遙測成為新的感測器平台，提供獲取大尺度空間資訊一個有效率的途徑，在森林特徵的量化已具成熟的技術，提供高操作彈性、高空間與時間解析度與多元的資料。本研究使用光達資料(light detection and ranging，LiDAR)、無人機系統(small unmanned vehicles，UAV)與攝影測量法(photogrammetry)，結合追算法(hindcast)，量化台灣中部山區蓮華池研究中心460公頃次生林和人工林分的歷史冠層高度。同時，分別建立了LiDAR與UAV冠層高度估計的迴歸模型，並比較了2者產製的樹冠高程模型(canopy height model)數據的精確性。LiDAR的CHM資料用作為追算法資料，提供UAV進行驗證，結果顯示預測的精度良好。我們的研究發現，闊葉樹和針葉樹種在研究期間每年增長為0.98和0.65公尺。本研究進一步的，將所估計歷史的生長樹高與氣象因子，透過標準化迴歸分析，描繪了蓮華池研究中心針葉樹與闊葉樹2種植被類型的對於氣候反應。因此，結合LiDAR 的地形高程模型(digital elevation model，DEM)、UAV的樹冠高程模型和追算法估計，可以有助於瞭解中小尺度森林區域的冠層高度動態，而且具有低成本和高效率的優勢。透過適當的科學量化工具，所發展的方法，可提供瞭解森林生長的現象，亦可作為未來人工林經營管理的參考資訊，期望可以解決必須面對的問題，並可提供其他相似的人工林在經營管理上作為參考。

關鍵字

森林生長 ； 人工林 ； 樹輪生態學 ； 大葉桃花心木 ； 端生薄壁細胞 ； 創傷開窗法 ； 直徑生長 ； 歷史事件分析 ； 加速失敗迴歸模型 ； 柳杉 ； 疏伐 ； 無人機系統 ； 樹冠高程模型

並列摘要

(1)The purpose of this study was to investigate the radial growth increment and ring density of different growth stages on mahogany (Swietenia macrophylla K.) plantation trees in southern Taiwan by wounding window method. From December 2008 to November 2009, one year can be classified into four stages (Dec. 2008-Fre. 2009 (P1), March 2009-June 2009 (P2), July 2009-Aug. 2009 (P3), and Sep. 2009-Nov. 2009 (P4), respectively) for understanding the effects of different seasons on radial growth increment and corresponding ring density. The radial growth increment showed a trend as follows: P3＞P4＞P2＞P1, however, the corresponding density displayed a trend as follows: P1＞P4＞P2＞P3. This indicates that the larger radial growth increment and lower density are associated with wet season, while smaller radial growth increment and higher density are typical for dry season. There was different between dry and wet season for radial increment, but not exist for ring density. The marginal axial parenchyma bands are formed during the latest dry season or early rainy season, and the bands have minimum density in a ring by ring density profile. There were very significant positive relationships between radial growth increment and climate factors (including air temperature, mean precipitation a month, and mean monthly relative humidity). However, there was no correlation between ring density and climate factors. The mean monthly relative humidity and mean sunshine hours a month are best predicts for radial growth increment by multiple stepwise regression.
(2)Effective time of thinning is essential for determining a silvicultural operation schedule. One of the most commonly used methods is the percentage of radial increase to assess the effect of thinning. However, it is difficult to determine the ideal time point due to variation in tree growth rates. Event history analysis was used to quantify the optimal timings for different row thinning types for a 45-year-old Cryptomeria japonica plantation in the mountainous region of Taiwan. The increase in tree-ring size was measured and converted to the basal area increment (BAI) to estimate annual tree growth; derived time-series data were entered into event history analysis to calculate the time to 50% probability of survival. Additionally, an accelerated failure time regression was applied to test the effects of thinning and its timing; model validation was carried out to examine the influence of thinning time variation on plant growth through time. Results showed that thinning modified the temporal dynamics of the BAI, and, in general, a positive trend was observed between strip-width and time of thinning. Simulated tree growth in the model validation corroborated that accurate timing may optimize thinning effects. Combining tree-ring measurement and event history analysis may facilitate determining the timing of row thinning, which can improve carbon sequestration of forest stands.
(3) Canopy height is essential for assessing terrestrial carbon cycle and biodiversity. Some of the most commonly used approaches are to acquire the information with the measurements from the ground. However, the field methods are very time consuming and labor intensive; it is impractical for the regional monitoring and long-term repeated measures. In the recent years, airborne LiDAR (Light Detection And Ranging) has been utilized for regional tree height mapping with high accuracy. However, the cost of LiDAR operation is high preventing systematic forest monitoring. Retrieving surface height from point cloud data (digital surface model, DSM) acquired by an unmanned aerial vehicle (UAV) has been rising technology due to the low cost and high mobility. With the availability of LiDAR measured high spatial resolution ground elevation data (digital terrain model, DTM), we may be able to estimate canopy height (known as a canopy height model, CHM) by subtracting UAV derived DSM from DTM. In addition, we might be able to estimate tree growth through time with the calibration of field observation. In this study, we utilized UAV-CHM acquired in 2014 from a 460-ha Lienhuachih Experimental Station (LES) occupied by a secondary broadleaf forest and broadleaf/conifer plantations located in the mountainous region of central Taiwan. The quality was compared with LiDAR-CHM of the same site. We then hindcasted the tree growth through time from 2010–2014 by calibrating UAV-CHM with historical records from 0.05 ha field plots (n = 10–18) using simple linear regression; investigate the relationships with growth (precipitation and air temperature) related monthly metrological attributes. We found that the performance of UAV-CHM was comparable with LiDAR-CHM acquired in 2011 explaining 74% of temporally corresponding field data variation, which permitted the use for mapping of canopy height through time. The regression analysis showed that, in general, the slope decreased (from 0.77 to 0.42) as the offset increased (from 0.18 to 2.3) when hindcasting back from the time of UAV data acquisition, which also reflected on the model fitness (R2 from 0.74 [p < 0.001] to 0.39 [p = 0.045]). We found that broadleaf and conifer species grew 0.98 and 0.65 m per year during the observation period. The meteorological analysis depicted that both vegetation types responded similarly to the climate. Monthly precipitation played a significant role in facilitating canopy growth all year round. Monthly mean temperatures of the spring (April) and summer (June and July) major growing seasons, and it of the leaf-out period (December) had a positive effect on tree growth. This study demonstrates the feasibility of utilizing UAV for forest management in a near-tropical humid region and the potential for investigating the effects of climatic attributes on canopy growth.

並列關鍵字

Forest growth ； plantation ； dendroecology ； Swietenia macrophylla ； marginal parenchyma ； wounding window method ； radial growth increment ； canopy height growth ； history event analysis ； accelerated failure time regression ； Kaplan-Meyer method ； Cryptomeria japonica ； Thinning ； UAV ； canopy height model

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