RICE CROP MAPPING AND YIELD ESTIMATION USING MULTI-TEMPORAL MODIS IMAGERY IN WEST JAVA PROVINCE, INDONESIA

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林陽昇(Indra Stevanus) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

(RICE CROP MAPPING AND YIELD ESTIMATION USING MULTI-TEMPORAL MODIS IMAGERY IN WEST JAVA PROVINCE, INDONESIA)

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摘要(中)

稻米是印尼主要的糧食作物。稻米生長區域的資訊有助於相關的單位對國家糧食的供銷與安全發展出更好的策略。本研究主要的目的即是以中尺度影像光譜輻射儀(MODIS)資料推導的常態化差異植生指數(NDVI)來進行稻區分布的製圖並估算稻米的產量。研究區位於印尼西爪哇省的西北部，是印尼主要稻米生產的區域之一，範圍包含了九個行政區，涵蓋面積約為13,155平方公里。
在本研究中2008年與2011年的資料被個別處理，研究方法包括兩個部分:稻作分區製圖與稻米產量模型建置。稻作分區製圖需進行四個步驟: (1)資料預處理:利用小波分析建立MODIS植生指數的平滑時間序列，包括NDVI、地表水指數(LSWI)、常態化建成區指數(NDBI);(2)進行影像遮罩移除非稻作區域; (3)以支援向量機進行稻作分類; (4)利用地面參考資料、稻作區統計資料進行分類的精度評估;而建立稻米產量模型包含了三個步驟:(1)像元NDVI剖線最大分蘗日的萃取;(6)建立稻米產量模式;(7) 稻米產能模式的誤差分析。
分類的結果顯示:2008年MODIS推估稻作分區的總體分類精度為86.5%、kappa值為 0.73；而2011年的總體分類精度為86.4%、kappa值為0.728。在稻米產量估算上，本研究建立了七個稻米測試模式(在NDVI時序剖線中稻作分蘗盛期的:最大分蘗日點，最大分蘗日點+鄰近2個點、最大分蘗日點+鄰近4個點、最大分蘗日點+鄰近6個點)。其中最大分蘗日點+鄰近2個點的預測模式有最佳的預測結果，原因為研究區中農民在稻作種植時間上的差異。推估的稻米產量顯示了R2為0.77的高相關與均方根誤差為0.414噸/公頃。本研究顯示使用MODIS時序影像進行稻作分區與稻米產量估測的可靠性與合理性。

摘要(英)

Rice is the staple food in Indonesia. Information on rice crop growing areas is useful for relevant agencies to devise better strategies to ensure security and stability of national food. The main objectives of this study are to map rice cropping systems and estimate the rice yield using normalized difference vegetation index (NDVI) generated from moderate resolution imaging spectroradiometer (MODIS) data in the northern part of West Java Province, Indonesia. This study area is one of the key rice producing regions in Indonesia. It consists of nine regencies, covering approximately 13,155 km2.
The data were processed for 2008 and 2011. The methodology of this study comprises two main parts: rice crop mapping and rice yield model establishment. To map rice area, four steps need to be performed: (1) data preprocessing to construct smoothed time-series MODIS vegetation indices, including: NDVI, land surface water index (LSWI), normalized difference built-up index (NDBI); using wavelet transform, (2) image masking to eliminate non-crop areas, (3) rice crop classification using the supervised Support Vector Machine (SVM) classifier, and (4) classification accuracy assessment using the ground reference data and rice area statistics. To establish a rice yield model, three steps were needed to be performed: (1) heading date NDVI extraction, (2) rice yield modeling, and (3) error analysis of rice yield model.
The classification results showed that the overall accuracy and Kappa coefficient of MODIS-derived rice area in 2008 are 86.5% and 0.73 respectively, while in 2011 are 86.4% and 0.728 respectively. For rice yield estimation model established from this study, several models (one, three, five, and seven points of NDVI value in heading period) were generated and the three points rice yield model was chosen as the best model since it represent the shifted period of planting date for the farmers in the study area. The rice yield model showed high correlation coefficient (R2) of 0.72 and RMSE of 0.418 ton/ha. The study results indicated that the rice crop mapping and yield estimation model using MODIS imagery was reliable and reasonable.

關鍵字(中)

★ 中尺度影像光譜輻射儀
★ 多時序
★ 稻作
★ 產量估算模式

關鍵字(英)

★ MODIS
★ time-series
★ rice crop
★ yield estimation model

論文目次

摘要 i
ABSTRACT ii
ACKNOWLEDGMENT iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
LIST OF ABBREVIATIONS x
CHAPTER 1. INTRODUCTION 1
1.1. Background 1
1.2. Research Problems 5
1.3. Objectives 5
1.4. Research Questions 6
1.5. Scope and Limitation 6
1.5.1. Scope 6
1.5.2. Limitation 6
CHAPTER 2. LITERATURE REVIEW 7
2.1. Remote Sensing Application for Crop Monitoring 7
2.2. MODIS 8
2.3. Noise Reduction Technique 11
2.3.1. Discrete Wavelet Transform 11
2.3.2. Mother Wavelet 12
2.4. Rice Crop Mapping 13
2.4.1. Support Vector Machines 15
2.5. Rice Yield Estimation 18
2.5.1. Advantages of Remote Sensing over Yield Monitors 19
2.5.2. Yield Models 20
CHAPTER 3. STUDY AREA AND DATA COLLECTION 21
3.1. Information of Study Area 21
3.1.1. Geographic Location 21
3.1.2. Land-Use Land Cover (LULC) Types 22
3.1.3. Topography 24
3.1.4. Climatology 25
3.1.5. Demography 27
3.1.6. Rice Cropping Systems 27
3.2. Data Collection 28
3.2.1. Satellite Data 28
3.2.2. Ancillary Data 29
CHAPTER 4. METHODOLOGY 31
4.1. Mapping Rice Crop 32
4.1.1. Data Pre-processing 32
4.1.2. Calculate Indices 32
4.1.3. Filtering the Noise 33
4.1.4. Image Masking 35
4.1.5. Image Classification 40
4.1.6. Classification Accuracy Assessment 41
4.2. Establishment of Rice Yield Model 43
4.2.1. NDVI Values Extraction 43
4.2.2. Rice Yield Modeling 48
4.2.3. Error Analysis of Rice Yield Model 48
CHAPTER 5. RESULTS AND DISCUSSION 49
5.1. Results of Rice Crop Classification 49
5.1.1. Image Masking Result 49
5.1.1. Spatial Distribution of Rice Cropping Systems 51
5.1.2. Comparison between Classification Results and Ground Reference Data 53
5.1.3. Comparison between MODIS-Derived Rice Area and Government Rice Area Statistics 56
5.1.4. Changes in Rice Cropping Practices 58
5.2. Rice Crop Yield models 59
5.2.1. Analysis and Accuracy Assessment for Rice Yield Model Result 59
CHAPTER 6. CONCLUSION AND RECOMMENDATION 63
6.1. Conclusion 63
6.2. Recommendation 64
REFERENCES 66

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指導教授

陳繼藩(Prof. Chi Farn Chen)

審核日期

2014-8-19

推文