Estimating canopy surface height of wheat and corn crops in reclaimed cropland using multispectral images from a small unmanned aircraft system

Abstract

Crop height is useful to monitor the effects of cropland reclamation. Unmanned aircraft system (UAS)-based observation through digital cameras has exhibited significant potential as a cost-effective way to predict crop height. However, the performance of UAS-based multispectral images has not been adequately analyzed in the context of estimating crop height. Therefore, our study investigates the performance of UAS-based multispectral images in terms of modeling and extracting crop height by considering a region of reclaimed cropland as an example. A framework is developed to model and extract the height of wheat and corn plants, the point cloud of varying densities is generated using the images, and geometric and physical outliers are removed from the point cloud. The flight altitude, threshold of extraction, and format of the data are examined to accurately describe and verify the canopy structure. The results indicate that UAS-based multispectral images can be used to estimate crop height. The relative accuracy of the estimated height of corn (<6 % ) was higher than that of wheat (>9 % ), but the opposite result was obtained in terms of absolute accuracy. The estimated crop height was significantly correlated with the measured above-ground biomass and can be used to monitor typical reclaimed cropland.