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姓名	陳正儒(Cheng-Ru Chen)  查詢紙本館藏	畢業系所	土木工程學系
論文名稱	以衛星影像物候資訊進行稻作分區之研究 (Satellite-Based Information for Rice Crop Monitoring)
相關論文	★ 多時期衛星影像之自動化監督性分類 ★ 大範圍地區土地使用分類之研究 ★ 高解析力衛星影像控制點座標之自動化萃取 ★ 影像最佳類別數目之研究 ★ 遙控直昇機應用於工程管理監測可行性之研究 ★ 以地理資訊系統輔助共同管道之最適設計 ★ 有理函數應用於空載多光譜影像幾何校正之研究 ★ SPOT自然色影像產生之研究 ★ 結合影像區塊及知識庫分類之研究-以IKONOS衛星影像為例 ★ 遙控飛機空載視訊影像自動化鑲嵌方法之研究 ★ 影像分割技術於高解析衛星影像分類之應用 ★ 小波多層次解析之影像融合應用 ★ 線性複合模式應用於變遷偵測之研究 ★ 改良式變異向量分析法於變遷偵測之探討 ★ 區塊分割變遷偵測法於多時期衛星影像之應用 ★ 資料挖掘技術應用於外來入侵植物研究 (以恆春地區銀合歡為例)
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摘要(中)	稻米是全世界最重要的經濟與糧食作物之一。在許多以稻作經濟為主的國家，稻米的生產與供需支持了農村勞力雇用與家戶收入的主要經濟來源。由制定農業經濟計劃與確保穩定供應的糧食安全層面來看，稻米產區每年稻作型態與其空間分布的估測資訊可說是至關重要的決策資料。因此，本研究的主要目的是利用多時序衛星影像資訊進行從地區到區域尺度稻米耕作系統之分區研究。研究內容以三個不同的稻米生長區域的狀況為案例展開。第一個案例的主要目標是，利用衛星影像推導之物候資訊發展以作物物候資訊為基礎的作物耕作型態分類方法，研究使用MODIS時序影像進行湄公河越南三角洲上游稻區的分類；第二個案例，為因應小面積稻作坵塊的監測需求，產生MODIS與SPOT的多時序融合影像，並用以驗證物候資訊分類演算法進行稻作系統之分區製圖適宜性；第三個案例，則是因應大區域尺度範圍下複雜多樣的稻作系統判定，發展利用MODIS推導的作物物候時序資料，並以類神經網路進行分類，產製下湄公河國家，包含越南、泰國、寮國與柬埔寨等國家的稻作系統分區地圖。 研究流程有四個主要的資料處理步驟：(1)影像資料前置處理:包括了衛星影像的幾何與輻射校正；並且使用時空適應反射率融合模式(STARFM)進行台灣地區的MODIS與SPOT影像的影像融合，產生高時空解析的合成影像；(2) 植生指數的時序合成影像依研究區域空間尺度範圍與應用的分類方法，各別以經驗模態分解法與小波轉換法濾除時序雜訊；(3)依區域詳細作物曆資訊的獲取性，分別以作物物候資訊為基礎的分類算法與類神經網路分類法進行稻作耕種系統的分類；(4)利用地面參考資料與政府統計數據進行分類精度評估並繪製稻作系統分區圖與產生量化資訊。 研究發現經驗模態分解與小波轉換皆可有效濾除植生指數時序資料上的雜訊。比較地面調查參考資料與研究分類的結果，驗證了本研究利用衛星時序資料應用不同資料前處理、資料濾噪與分類策略在不同空間尺度(從地區尺度到區域尺度)與作物曆的可得性進行稻作系統自動判釋的有效性。第一個研究案例在越南三角洲上游大塊且均質稻區，MODIS時序影像推導的物候資訊配合當地作物曆，直接使用物候資訊分類算法進行分類，總體分類精度可以達到93.8%，Kappa係數為0.90；由分類地圖推導的稻米種植面積與縣區尺度的政府稻米調查統計比較結果，顯示相當顯著的一致性(R2 = 0.91)，驗證了物候資訊分類法進行該研究區內稻作系統自動化判定分類的有效性。案例二則因應台灣小坵塊稻作區問題，先使用MODIS與SPOT融合出高空空與時間解析的合成影像，再以物候資訊分類算法進行分類，分類結果顯示與政府調查資料亦有很高的一致性，由分類圖推估的各期稻作耕種面積與政府稻作面積統計資料比較：第一期與第二期的R2 分別為 0.98與0.90，RMSE各為115.7 公頃與284.39公頃；第一期稻作的總體分類精度為89.6%，Kappa係數為0.79，第二期的總體分類精度為83.2%，Kappa係數為0.66。第三個研究屬於區域尺度的範圍；由於東南亞中南半島大範圍尺度下有著複雜多樣化的稻米種植型態，因此本研究使用衛星影像的物候資訊配合類神經網路進行稻米型態分布分類。分類結果的總體分類精度與Kappa係數分別為84.9%與0.8，並比較分類影像推導的稻作面積與省級尺度的地面調查/政府稻米統計資訊，其各國R2 皆高於0.91以上，顯示了以衛星物候資訊應用類神經網路進行稻區分類的有效性。 儘管某些誤差影響，如資料處理、混合像元問題、分類結果與參考地真資料(產生自高解析衛星資料與航照)的解析度差異等因素而產生部分分類精確度降低的情況；然本研究的分類成果與建議方法，能有效提供研究區內稻作型態分布與監測的量化數據，這些資訊有助於農業計畫規劃者制定稻作生產的管理策略，以強化國家層級的糧食安全與擬定稻米進出口政策；同時，本研究所提出的方法除驗證研究區的可應用性，亦可推廣至世界其他稻作區進行稻作生產活動的監測。
摘要(英)	Rice is globally one of the most important economic and food crops. It is the main source of employment and income for rural people in many countries worldwide. Yearly estimation of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of food supply. The main objective of this study is to develop approaches for mapping rice-cropping systems at sub-national and regional scales by using multi-temporal satellite data. Three case studies were carried out under different rice growing conditions. The objective of the first case study was to develop a phenology-based approach for rice crop mapping in the upper Mekong River Delta (MRD) region in South Vietnam using Moderate Resolution Imaging Spectroradiometer (MODIS). The second case study was to use time-series MODIS−SPOT fusion data for rice crop mapping using the phenology-based method. In the third case study, an approach was developed to map complex rice cropping systems in Low Mekong Countries (LMC), including Vietnam, Lao, Thailand and Cambodia, from the time-series MODIS data using artificial neural networks (ANN). The data processing was basically carried out through four main steps: (1) data preprocessing to account for geometric and radiometric errors, and to use MODIS data with Satellite Pour l′Observation de la Terre (SPOT) data using the spatial-temporal adaptive reflectance fusion model (STARFM) for the case study in Taiwan; (2) construction of the smooth time-series vegetation indices (VIs) using the empirical mode decomposition (EMD) and wavelet transform (WT),respectively regarding to the scale of study area and the application of the classifier; (3) rice crop classification using phenology-based algorithm and ANN due to availability of detail crop calendar, and (4) accuracy assessment of the mapping results using ground reference data and rice area statistics provided by the government. The research findings confirmed that EMD and WT algorithms were efficient at filtering out noise from the time-series VI MODIS data. The mapping results compared with the ground reference data indicated the validity of adapting strategy, including data preprocessing, noise filtering and classification methods, regarding to the availability of detailed regional crop calendar for rice crop mapping at the subnational and regional scales. The results achieved for the first case study in upper MRD, in which has larger and homogeneous rice fields, using the phenology-based approach revealed the overall accuracy and Kappa coefficient value of 93.8% and 0.90, respectively. A close agreement between the MODIS-based rice areas and the district-level rice area statistics was observed (R2 = 0.91), reaffirming the effectiveness of this approach for automatically delineating rice-cropping systems in the study region. The phenology-based approach was applied to the time-series MODIS−SPOT fusion data to delineate small-scale rice fields in Taiwan. The results indicated a close correlation between the mapping results and the government rice statistics (the R2 and RMSE for the first and second crops, were 0.98 and 115.7 ha. and 0.90 and 284.39 ha. respectively). The overall accuracies and Kappa coefficients achieved for the first and second crops were 89.6% and 0.79, and 83.2% and 0.66, respectively. The ANN applied to the filtered MODIS VI data to map complex rice cropping systems in LMC showed the overall accuracy and Kappa coefficient of 84.9% and 0.8, respectively. The comparison results between MODIS-derived rice area and rice area statistics at the provincial level also reaffirmed by the validity of ANN algorithm (R2 = 0.91). The accuracy level of the mapping results was lowered by some error sources including data preprocessing, mixed-pixel problems and resolution bias between the mapping results and the ground reference data constructed using high-resolution satellite data or aerial photos. The results, however, achieved from this study could provide quantitative information on rice cropping systems that may be useful for agronomic planners to devise strategies for rice crop management to enhance national food security and rice grain exports. Such approaches were thus proposed for monitoring rice cropping activities in the study regions and other places worldwide.
關鍵字(中)	★ 遙測 ★ 稻米 ★ 作物監測 ★ 物候資訊 ★ 影像融合	關鍵字(英)	★ Remote sensing ★ Rice ★ Crop monitoring ★ Phenology ★ Image fusion
論文目次	中文摘要 i ABSTRACT iv ACKNOWLEDGMENT vii TABLE OF CONTENTS viii LIST OF FIGURES xii LIST OF TABLES xvi LIST OF ABBREVIATIONS xvii CHAPTER 1. INTRODUCTION 1 1.1 General Background 1 1.2 Statement of Research Problems 6 1.3 Research Objectives 7 1.4 Dissertation Structure 7 Chapter 2. LITERATURE REVIEW 9 2.1 Time-series Satellite Data for Agriculture Monitoring 9 2.2 Data Fusion Techniques 10 2.3 Noise Filtering Techniques 15 2.3.1 Fourier Transform 15 2.3.2 Savitzky-Golay Filter 16 2.3.3 Wavelet Transform 17 2.3.4 Empirical Mode Decomposition 19 2.3.5 Comparisons between Noise Filtering Methods 20 2.4 Classification Algorithms on Remote Sensing for Crop Monitoring 22 CHAPTER 3. STUDY AREA AND DATA COLLECTION 29 3.1 Study Region in Mekong River Delta 29 3.2 Study Region in Taiwan 32 3.3 Study Region in Southeast Asia 34 3.3. Data Collection 38 3.3.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Data 38 3.3.2 Satellite Pour l’Observation de la Terre (SPOT) Data 40 3.3.3 Ground Reference and Ancillary Data 42 CHAPTER 4. METHODOLOGY 46 4.1 Conceptual Framework 46 4.2 Rice Crop Mapping in the Upper Mekong River Delta 48 4.2.1 Data Pre-Processing 48 4.2.2 Non-Rice Area Masking 50 4.2.3 Phenology-Based Rice Crop Classification 52 4.2.4 Accuracy Assessment 55 4.3 Rice Crop Mapping in Taiwan 56 4.3.1 Data Pre-processing 56 4.3.2 MODIS-SPOT Fusion data 57 4.3.3 Constructing Smooth Time-Series STARFM NDVI Data 59 4.3.4 Rice Crop Mapping Using Phenology-Based Algorithm 60 4.3.5 Accuracy Assessment 62 4.4 Rice Crop Mapping in Southeast Asia Region 63 4.4.1 Data Pre-Processing 64 4.4.2 ANN-Based Image Classification 67 4.4.3 Accuracy Assessment 71 CHAPTER 5: RESULTS AND DISCUSSION 72 5.1 Results of Rice Crop Mapping in the Upper Mekong Delta 72 5.1.1 EMD of NDVI Time-Series Data 72 5.1.2 Comparison between the Estimated Dates and Field Survey Data 75 5.1.3 Classification Accuracy Assessment 76 5.1.4 Comparison of Classified Results with Rice Statistics 78 5.1.5 Summary 79 5.2 Results of Rice Crop Mapping in Taiwan 80 5.2.1 Validation of STARFM Results 80 5.2.2 Comparison between the Estimated Dates and Field Survey Data 83 5.2.3 Accuracy of Mapping Results 86 5.2.3 Summary 91 5.3 Results of Rice Crop Mapping in Southeast Asia 92 5.3.1 Temporal characteristics of rice crop NDVI profiles 92 5.3.2 Spatial Distributions of Rice Cropping Systems in Southeast Asia Region 95 5.3.3 Comparisons between Classification Map and the Ground Reference Data 96 5.3.4 Comparisons between Classification Results and Rice Area Statistics 98 5.3.5 Summary 101 CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS 103 6.1 Conclusions 103 6.3 Recommendations 104 REFERENCES 107
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指導教授	陳繼藩(Chi-Farn Chen)	審核日期	2014-7-31
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