Estimation of chlorophyll content with multispectral high-resolution imagery from an unmanned aerial vehicle (UAV) for paddy rice fields under alternate wetting and drying irrigation and system of rice intensification

• Main Authors: Traore Adama, 茶奥
• Other Authors: Wang, Yu-Min
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/bv24tz

碩士 === 國立屏東科技大學 === 土壤與水工程國際碩士學位學程 === 105 === Chlorophyll content, a good indicator for plant healthy state and important biophysical parameters, is important significance for precision agriculture. To estimate the spatial variability of chlorophyll content over fields, traditional method using chlorophyll meter requires many point samples. Because of relationship between chlorophyll content and spectral reflectance of certain bands, remote sensing techniques have the potential to predict the chlorophyll content over large fields. In this study, the use of multispectral resolution imagery using unmanned aerial vehicle called UAV is to select the vegetation indices sensitive to chlorophyll content using regression model. The goal of our study is to investigate the performance of multispectral camera for estimation of chlorophyll content. The application of remote sensing techniques on paddy rice were conducted on six dates from January to May during all stage growth with four narrow band sensors (Green, Red, Red Edge and Near Infrared) in order to estimate the chlorophyll content. Nine physiological indices were determined to estimate the chlorophyll content. Normalized Vegetation index (NDVI), Modified Triangular vegetation index (MTVI), Normalized Green-Red Difference Index (NGRDI), Red Edge NDVI (REGNDVI), Chlorophyll Vegetation Index (CVI) were found to be accurate and linear estimators of chlorophyll content measured in the fields.