應用LANDSAT衛星資料偵測及預估全球紅樹林之變遷;DETECTING AND PREDICTING CHANGES IN MANGROVE FOREST AROUND THE WORLD USING LANDSAT SATELLITE DATA

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題名:	應用LANDSAT衛星資料偵測及預估全球紅樹林之變遷;DETECTING AND PREDICTING CHANGES IN MANGROVE FOREST AROUND THE WORLD USING LANDSAT SATELLITE DATA
作者:	陳清民;DAN,TRAN THANH
貢獻者:	遙測科技碩士學位學程
關鍵詞:	LANDSAT衛星資料;LANDSAT
日期:	2015-07-23
上傳時間:	2015-09-23 14:45:30 (UTC+8)
出版者:	國立中央大學
摘要:	紅樹林位於熱帶及副熱帶地區，它連接海洋與人類居住的土地，提供人類多樣的生態及社會經濟功能。同時，在世界上許多地方的紅樹林正以驚人的速率在減少。但減少的比率及原因不得而知。監測紅樹林的時空變化對於紅樹林自然資源管理是相當重要的。因此，本研究的目的有以下幾項：(1) 繪製全球現今紅樹林的分布現況：西非及中非地區、東南亞、澳洲及中美洲地區；(2) 利用Landsat衛星影像判釋紅樹林1998至2014年的變遷情形(增加或減少)；(3) 預測未來紅樹林變遷的情形。研究方法分為以下五個步驟：(1) 資料前處理，包含大氣校正及影像常態化；(2) 利用支持向量機(Support Vector Machine - SVM)進行監督式影像分類；(3) 影像分類結果的精度評估；(4) 紅樹林變遷偵測分析；(5) 紅樹林預測結果分析。對於本研究的四個地區，紅樹林的面積大多維持相同或些許的增加/減少。西非及中非地區：1998年至2014年紅樹林減少的比率約16.9%，然而在同時間約有2.5%的紅樹林復育或新種植；澳洲西部：整體而言，此地區27年來紅樹林消失非常快速(減少比率約20.85%)；中美洲- Gulf of Fonseca地區：此地區26年來紅樹林的約減少16.24%，然而有一小部分(4.32%)為新種植或復育；東南亞地區- Sundarbans delta：此地區的紅樹林約增加15.3%。紅樹林的減少是因為森林濫伐、過度開發、自然災害及紅樹林的復育。都市化造成許多環境問題，包含棲地的喪失、生物多樣性的減少及加速海岸侵蝕。本研究利用Probabilistic Landscape Modelling and Simulation Tool (ProLAMS)預測中美洲地區(Gulf of Fonseca)紅樹林至2027年的變化情形，此模式為機率預測的方法，整合遙測資料及地理資訊資料進行地景的模擬。結果顯示，預測的結果與2001年和2014的分類結果進行比較，顯示紅樹林的總面積些微的增加。然而，未來的紅樹林的面積維持相同或些許的減少。紅樹林預測的結果受到模式中許多參數的影響。因此，制度及政策的干預可以考慮做為一區域紅樹林管理的因素。 ;Tropical mangrove forests are located in the tropical and subtropical regions, and they connect our land and people with the sea, providing various ecological and socioeconomic services for humans. At the same time, mangrove forests in many parts of the world are declining at an alarming rate-possibly. Monitoring spatiotemporal distribution of mangrove forests is thus critical for natural resource management. Therefore, the research objective are: (i) mapping mangrove forests in the four regions around the world: West and Centre Africa, Southeast Asia, Australia and Central America using Landsat data from 1988 to 2014; (ii) identifying mangrove forests cover change (gain and loss) in three past decades; and (iii) predicting changes of mangrove forests in the future. The data were processed through five main steps: (1) data pre-processing including atmospheric correction and image normalization; (2) image classification using supervised classification approach (Support Vector Machine – SVM); (3) accuracy assessment for the classification results; (4) change detection analysis; and (5) change projection analysis. In all four cases of study, mangrove area have increased or decreased seriously. In West and central Africa: The loss of mangrove forests from 1988 to 2014 was approximately 16.9%, while only 2.5% was recovered or newly planted at the same time; Australia –Western: the overall changes in the area is decreased of mangrove forests very dramatically (loss about 20.85% of mangrove forests) from 27 years study. And Central America – Gulf of Fonseca: the overall change within the study area during this 26 years indicated the loss of approximately 16.24% of mangrove forest, while a small proportion of mangrove forests in the region (4.32%) was newly planted or rehabilitated while Southeast Asia - in Sundarbans delta: the overall change of mangrove forests was increased approximately 15.3% of total mangrove forests area. The decline of mangroves due to the deforestation, overexploitation, natural catastrophes deforestation and mangrove rehabilitation programs. Urbanization has caused environmental issues including habitat loss, reduction of biodiversity, and increased coastal erosion. For mangroves change projection, this research was projected changes until 2027 within the in-situ area that was selected in four sites of study by using Probabilistic Landscape Modelling and Simulation Tool (ProLAMS) which integrates the remote sensing data and geographic information data for landscape modeling and prediction by using probabilistic simulation approach. The results shown that the total area of mangrove forests increased a little bit when compared with classification results in 2001 and 2014. However, mangroves area was remain unchanged or slightly decreased in the future. Mangrove prediction result effected by the input variables as well as the parameters used within the model. Thus, institutional or policy interventions may be taken into account to improve management of mangrove forests in the regions. The overall efforts in this study demonstrate the effectiveness of the proposed method used for investigating and predicting the spatiotemporal changes of mangrove forests.
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