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

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题名:	RICE CROP MAPPING AND YIELD ESTIMATION USING MULTI-TEMPORAL MODIS IMAGERY IN WEST JAVA PROVINCE, INDONESIA
作者:	林陽昇;Stevanus,Indra
贡献者:	遙測科技碩士學位學程
关键词:	中尺度影像光譜輻射儀;多時序;稻作;產量估算模式;MODIS;time-series;rice crop;yield estimation model
日期:	2014-08-19
上传时间:	2014-10-15 17:16:16 (UTC+8)
出版者:	國立中央大學
摘要:	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。 ;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.
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