福衛二號衛星影像應用於印尼油棕植區分類之研究

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高德培(Laju Gandharum) 查詢紙本館藏

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國際永續發展碩士在職專班

論文名稱

福衛二號衛星影像應用於印尼油棕植區分類之研究
(Classification of Oil Palm in Indonesia Using FORMOSAT-2 Satellite Image)

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摘要(中)

自2006年以來，印尼是世界最大的油棕原油輸出國。在2007年，其輸出油棕原油及相關產品約1千1百萬噸，其產值達62億美元。對印尼而言，油棕產業是至為重要的經濟支柱，但也為印尼帶來環境與社會的衝擊。其中，油棕墾殖區的擴張，就引發了森林濫墾濫伐的敏感議題。為了減少對環境的衝擊與符合國家經濟發展的需求，印尼必須要有一套永續油棕發展墾殖的規劃。本研究將嘗試應用遙測的技術，來輔助油棕墾殖永續發展的規劃。
本研究所選取的研究區位於印尼西爪哇省勃羅區西密量的油棕墾殖區。研究所使用的衛星影像是採用福衛二號的影像資料，其中包括8公尺解析度的4波段多光譜影像以及2公尺解析度的全色態影像。本研究的目標是測試先以多光譜影像的光譜資訊分類種植區域的油棕樹；接著再用紋理資訊加入光譜資訊分類油棕樹，然後再比較兩種方式的分類精度。本研究的紋理資訊主要是利用相關係數辨識2公尺全色態影像的的紋理，而分類方法是採用最大相似監督分類法。結果顯示以多光譜影像光譜資訊分類的總精度為66.4%，而由於福衛二號2公尺的全色態影像中，可辨識出9米的油棕種植間距，因此藉由全色態影像紋理分析的相關係數之加入，油棕樹的分類總分類精度可提升至76%，約增加了10.4%的分類精度。本研究未來希望可以在油棕墾殖永續發展規劃中，作為建立並更新油棕土地利用區的地圖製作使用。

摘要(英)

Indonesia is the biggest exporter crude palm oil (CPO) in the world since 2006. Total export of Indonesian’s CPO and its derivatives in 2007 was about 11 million tons or equal to US$ 6.2 billion. It is a valuable sector that supports Indonesian economics, but it also causes environmental and social impacts. Deforestation is a sensitive issue related to oil palm plantation expansion. Sustainable oil palm development is needed to reduce environmental impacts and to meet economics purpose. Through the utilization of remote sensing (RS) technology, this study has tried to support sustainable oil palm development.
Cimulang oil palm plantation that lies in district of Bogor, West Java Province, Indonesia was chosen as study area. High spatial resolution FORMOSAT-2 satellite image that has 4 multispectral bands (8 m spatial resolution) and 1 panchromatic band (2 m spatial resolution) was used in this study. The objectives of this study are to classify growing stages of oil palms using only multispectral bands and to classify growing stages of oil palms using multispectral bands plus texture information of FORMOSAT-2 data, to test the accuracy of both classification results, and to support sustainable palm development by providing more often updated oil palm land use map. Texture extraction through image matching by correlation and maximum likelihood supervised classification method has been applied in this study. The result shows that overall accuracy for multispectral image classification is 66.4%. Triangular oil palms planting pattern that has space 9 m apart between trees can be seen visually in 2 m panchromatic image of FORMOSAT-2 data and it also can be extracted automatically by texture analysis through image matching by correlation. This texture information then added to multispectral bands for classification. The overall accuracy result of multispectral bands with texture information is 76.8%. Image classification accuracy has improved (10.4 %) if the classification process employed not only multispectral bands but also added with the texture information.

關鍵字(中)

★ 印尼
★ 福衛二號
★ 油棕
★ 分類研究

關鍵字(英)

★ Indonesia
★ Classification Studies
★ FORMOSAT-2 Satellite，Oli Palm

論文目次

Chinese Abstract i
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
List of Tables x
1.Introduction 1
2.Background Information 3
2.1.General information of Indonesia 3
2.2.Oil palm 4
2.2.1.The oil palm 5
2.2.2.Land suitability 7
2.2.3.Agro-industry process 8
2.2.4.Uses 8
2.2.5.Oil palm plantation in Indonesia 9
2.3.Brief description of remote sensing 11
2.4.Type of remote sensing image 14
2.5.Application of remote sensing for oil palm 16
2.6.Merging spectral and textural information for image classification 19
3.Study Area and Data Collection 21
3.1.Study area 21
3.2.Data collection 23
3.2.1.FORMOSAT-2 satellite data 23
3.2.2.Ground truth 26
4.Methodology 31
4.1.Image preparation 32
4.2.Multispectral image classification 33
4.2.1.Training fields 34
4.2.2.Maximum likelihood classification 35
4.2.3.Generalization (regrouping classes and smoothing zone edges) 35
4.3.Image classification with texture information 37
4.3.1.Applying High-pass filter 38
4.3.2.Texture extraction through image matching by correlation 39
4.4.Accuracy assessment 43
4.4.1.Overall’s, User’s and Producer’s accuracy 43
4.4.2.Kappa statistic 44
5.Result and Discussion 46
5.1.Geometric correction results 46
5.2.Results of multispectral images classification 47
5.3.Results of multispectral and texture information images classification 53
5.4.Comparison 57
6.Conclusion and Recommendation 60
6.1.Conclusion 60
6.2.Recommendation 61
References 63

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指導教授

陳繼藩(Chi-Farn Chen)

審核日期

2010-7-22

推文