Abstract
Estimating above-ground biomass in the context of fertilization management requires the monitoring of crops at early stages. Conventional remote sensing techniques make use of vegetation indices such as the normalized difference vegetation index (NDVI), but they do not exploit the high spatial resolution (ground sampling distance < 5 mm) now achievable with the introduction of unmanned aerial vehicles (UAVs) in agriculture. The aim of this study was to compare image mosaics to single images for the estimation of corn biomass and the influence of viewing angles in this estimation. Nadir imagery was captured by a high spatial resolution camera mounted on a UAV to generate orthomosaics of corn plots at different growth stages (from V2 to V7). Nadir and oblique images (30° and 45° with respect to the vertical) were also acquired from a zip line platform and processed as single images. Image segmentation was performed using the difference color index Excess Green-Excess Red, allowing for the discrimination between vegetation and background pixels. The apparent surface area of plants was then extracted and compared to biomass measured in situ. An asymptotic total least squares regression was performed and showed a strong relationship between the apparent surface area of plants and both dry and fresh biomass. Mosaics tended to underestimate the apparent surface area in comparison to single images because of radiometric degradation. It is therefore conceivable to process only single images instead of investing time and effort in acquiring and processing data for orthomosaic generation. When comparing oblique photography, an angle of 30° yielded the best results in estimating corn biomass, with a low residual standard error of orthogonal distance (RSEOD = 0.031 for fresh biomass, RSEOD = 0.034 for dry biomass). Since oblique imagery provides more flexibility in data acquisition with fewer constraints on logistics, this approach might be an efficient way to monitor crop biomass at early stages.
Highlights
This study explored an alternative to vegetation indices for predicting corn biomass from images with very high spatial resolution
Segmenthe Sap tation was less accurate with orthomosaics than single images, both platforms (UAV and zip line) yielded good estimations of corn biomass at nadir view
Our study demonstrated that using oblique imagery at 30◦ could improve biomass estimation, differences between the nadir and oblique viewing angles up to 45◦ are not that significant
Summary
Corn (Zea Mays L.) represented 21% of cropland in Quebec in 2016, the most recent numbers according to Statistics Canada [1,2]. This intensified agriculture results in soil degradation and excessive nitrogen use, leading to environmental contamination [3,4] and to economic losses [5,6]. To answer those challenges, precision agriculture has emerged as a management strategy that takes into account temporal and spatial variability in order to improve the Remote Sens.
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