Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/89314
標題: Studies on Forest Succession under Natural Disturbances with Landscape Model -An Example of Jiou-Jiou Peaks Nature Reserve
以地景模式探討受自然干擾下的森林演替-以九九峰自然保留區為例
作者: Jeng-I Tsai
蔡正一
關鍵字: 九九峰自然保留區;森林恢復年齡推估;台灣蘆竹;演替;馬可夫細胞自動化模式;Jiou-Jiou Peaks Nature Reserve;forest age estimation;Arundo formosana;succession;CA-Markov
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摘要: 
1999年的921地震對地形陡峻的九九峰地景與植生產生何種影響,十幾年來地景與植生又是如何恢復與變化皆讓人好奇,九九峰自然保留區於2000年成立以保護特殊地形,人為活動被禁止,但地景與演替仍因為「時間」與「空間」因子而變異,研究整合地景模式、地形因子與現場調查等方法與資料進行探索,期望瞭解該處受自然干擾下,這15年來陡峻區域的地景變遷、森林恢復時間與過程、台灣蘆竹區位與影響、森林演替發展與預測未來狀況等。
研究依據影像色調(image tone)與組織(texture),繪製1998年7月、1999年12月與2009年12月,包含森林地、台灣蘆竹(Arundo formosana)、水體(包含河岸)、裸露地、陰影與其他地覆的土地利用/土地覆蓋型圖(land-use / land-cover map, LU / LC map),做為SPOT影像分析地真資料,並進行套疊,初步分析該保留區地景變遷,結果顯示,地震前、後至2009年,約126.60 ha (10.56 %)的森林保持不變。變動較大者,如2009年的台灣蘆竹,有114.78 ha (9.57 %)係由原本的森林,變成裸露地,再發育成台灣蘆竹,而有10.65 ha (0.89 %)森林可能因為颱風影響變成河道。
將多時期SPOT衛星影像,包括1999年7月、1999年9月、2000年09年、2001年7月、2003年7月、2006年2月、2008年8月與2012年9月,以最大概似法(maximum likelihood classifier)與影像相減法(image differencing algorithm)處理,疊合推估森林恢復時間(以森林年齡代表)與過程,並繪製成森林恢復年齡推估圖,藉以瞭解「時間」序列下的森林變化趨勢,結果顯示,森林可靠自生力量漸漸恢復,且由山谷向上覆蓋;透過地景指數與後生族群理論,目前森林嵌塊體間的發展較相似「非平衡態後生族群(nonequilibrium metapopulation)」,即嵌塊體與嵌塊體間關聯性低。
台灣蘆竹發達根系使本體可於陡坡垂掛生長,山壁幾乎皆由該物種覆蓋,但是相關研究甚少,研究將2009年的LU / LC型圖,套疊地形因子,經雙因子變異數分析(two way-ANOVA)與群集分析(cluster analysis),探討該物種潛在區位與影響,結果顯示台灣蘆竹生長常覆蓋於坡度66°至78°間,以及東南與正南兩個坡向,且推測對演替有幫助。
將森林恢復年齡推估圖,以Neyman與Deming配置法決定各分層最適樣區位置。配合GPS(global position system)至現場調查各年齡樹種組成,分析森林演替發展,結果顯示,乾溪流域周邊的樹種有些許差異,仍以陽性樹種為主,但因為時間尺度較短,無法將演替序列完整拼湊。
最後,整合地景分析、現場調查與地形因子等結果與資訊,以馬可夫細胞自動化(CA-Markov)模式預測未來的地景恢復情形與空間分布,結果顯示,總量推估可做參考,但空間分布推估結果無法與實際情況完全符合。森林地於2029年後變動小,面積趨近504.20 ha(42.05 %),但恢復成地震前森林覆蓋程度機率甚小。

921 earthquake how to impact the landscape and vegetation in steeper areas of Jiou-Jiou Peaks, and the restoration and variation of landscape and vegetation over these years are curious. Jiou-Jiou Peaks Nature Reserve was established in 2000 to protect the unique topography. Although human disturbances were prohibited, the landscape and vegetation succession were still influenced by temporal and spatial factors. We integrated landscape model, terrain factors and field survey data to realize the landscape change, temporal and processing of forest restoration, niche of Arundo formosana, forest succession and prediction of future condition.
Ortho-aerial photographs, taken in 1998/07, 1999/12 and 2009/12, were applied to digital land-use / land-cover maps (LU / LC map), including forest, A. formosana, bared soil, water body (including river side) shadow and others, based on image tone (color) and texture. These maps could be become ground truth for SPOT analysis. We also overlapped the maps to analysis the landscape change preliminary. Results of landscape change showed 126.06 ha (10.56%) of forest never change after earthquake to 2009. 114.78 ha (9.57 %) from the forest became bared soil, and then developed into A. formosana until 2009. Moreover, 10.65 ha (0.89 %) areas of rivers side was changed from forest during 1999 to 2009 might because of typhoons.
Multi-temporal SPOT images, taken in 1999/07, 1999/09, 2000/09, 2001/07, 2006/02, 2008/08 and 2012/09, were collected. The supervised classification, image differencing algorithm and overlapped analysis were applied to estimate the temporal of forest restoration (forest age) and proccessing. A map showing the forest estimation age and location was generated to recognize the tendency of forest changes. The result showed that the forest restoration occurred from the bottom to the top. According to landscape indices and metapopulation theory, the restoration condition of this reserve was nonequilibrium metapopulation, which means almost no relations between patchs and patchs of forest.
A. formosana can hang downward grow and thrive from cliffs because of developed roots. Almost all the steeper slopes of the reserve were covered by this species, but knowledge of this species' niche was lack. Therefore, we applied data of LU / LC and topographic factors to analyze the niche of A. formosana with two way-ANOVA and cluster analysis. The result exhibited that surfaces facing south and southeast, and slopes from 74° to 78° were were more suitable for the growth of A. formosana. We also think the species might help the succession.
The sampling locations were chosen with forest estimation age map, Neyman and Deming methods. We tried to identify forest succession based on field survey data of sampling locations near Gan River. We found the species compositions varied temporally, and the compostion was gathered by heliophilous species. The forest succession sere could not recognize completely in such short periods.
Finally, all results were integrated, including landscape analysis, field survey data and topographic factors, to predict the future condition with CA-Markov model. Althought the spatial distribution of simulation do not match the actual situation perfectly, the simulation area was believed. The forest area might be stable at 2029 around 504.20 ha (42.05 %) but forest will be difficult to recover as pre-earthquake condition.
URI: http://hdl.handle.net/11455/89314
其他識別: U0005-2811201416175064
Rights: 同意授權瀏覽/列印電子全文服務,2017-08-31起公開。
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