Estimating corn (Zea mays L.) LAI [leaf area index] using UAV-derived vegetation indices
2018
Fumera, J.O.
A more time-saving and accurate way of crop monitoring is what remote sensing can offer, spatial and temporal monitoring alike. This study focuses on using unmanned aerial vehicle equipped with specialized camera for monitoring leaf area index of com (LAI) (Zea mays L.). The study was conducted during the wet season (August 2017 to November 2017) in a 2160 m? experimental plot. Four different fertilizer treatments: FO No Fertilizer Treatment), FI (100% Conventional Fertilizer), F2 (100% Organic Fertilizer) and F3 (50% Conventional 50% Organic Fertilizer), were applied to four major plots to ensure variation in the vegetation cover, thus difference on the LAI. Aerial photographs were captured at 47 DAS (vegetative stage) to ensure that all the leaves are already emerged. These geo-referenced images were stitched and orthorectified to produce a single orthomosaic image. Using QGIS, the orthomosaic map was processed into blue NDVI (BNDVI), enhanced (ENDVI) and green NDVI (GNDVI) maps. Ground measurements of the LAI were also performed on the same stage of the crop growth. In the vegetative stage, the average corn LAI for treatments FO, FI, F2 and F3 are 0.9452, 1.9847, 0.9893 and 1.4158 respectively. This suggests that the different fertilizer treatments produce LAI variations. The correlation coefficients of BNDI, ENDVI and GNDVI with LAI are 0.7189, 0.0526 and 0.8192 respectively. Compared to other vegetation indices, GNDVI is a good predictor of LAI of corn. This relationship is useful enough in developing remote sensing protocols for a site-specific monitoring of LAI.
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Este registro bibliográfico ha sido proporcionado por University of the Philippines at Los Baños