Multi-Temporal Crop Surface Models Combined With the RGB Vegetation Index From Uav-Based Images for Forage Monitoring in Grassland

Abstract

Commission I, ICWG I/VbKEY WORDS: Precision Agriculture, Grassland, UAV, Crop Surface Model, Structure from Motion, RGBVI ABSTRACT:Remote sensing of crop biomass is important in regard to precision agriculture, which aims to improve nutrient use efficiency and to develop better stress and disease management. In this study, multi-temporal crop surface models (CSMs) were generated from UAVbased dense imaging in order to derive plant height distribution and to determine forage mass. The low-cost UAV-based RGB imaging was carried out in a grassland experiment at the University of Bonn, Germany, in summer 2015. The test site comprised three consecutive growths including six different nitrogen fertilizer levels and three replicates, in sum 324 plots with a size of 1.5×1.5 m. Each growth consisted of six harvesting dates. RGB-images and biomass samples were taken at twelve dates nearly biweekly within two growths between June and September 2015. Images were taken with a DJI Phantom 2 in combination of a 2D Zenmuse gimbal and a GoPro Hero 3 (black edition). Overlapping images were captured in 13 to 16 m and overview images in approximately 60 m height at 2 frames per second. The RGB vegetation index (RGBVI) was calculated as the normalized difference of the squared green reflectance and the product of blue and red reflectance from the non-calibrated images. The post processing was done with Agisoft PhotoScan Professional (SfM-based) and Esri ArcGIS. 14 ground control points (GCPs) were located in the field, distinguished by 30 cm × 30 cm markers and measured with a RTK-GPS (HiPer Pro Topcon) with 0.01 m horizontal and vertical precision. The errors of the spatial resolution in x-, y-, z-direction were in a scale of 3-4 cm. From each survey, also one distortion corrected image was georeferenced by the same GCPs and used for the RGBVI calculation. The results have been used to analyse and evaluate the relationship between estimated plant height derived with this low-cost UAV-system and forage mass. Results indicate that the plant height seems to be a suitable indicator for forage mass. There is a robust correlation of crop height related with dry matter (R² = 0.6). The RGBVI seems not to be a suitable indicator for forage mass in grassland, although the results provided a medium correlation by combining plant height and RGBVI to dry matter (R² = 0.5).

Canopy height measurements and non‐destructive biomass estimation of Lolium perenne swards using UAV imagery

“…The correlation obtained between the canopy height measured with the RPM, and the variables derived from the CHM for the Lp‐CPs was 0.92 (Figure b). The correlation is similar to the values reported by Viljanen et al () for a timothy and meadow fescue mixture ( r = .91–.94), similar to the values reported in a 3‐year study by Bareth and Schellberg () ( r = .83–.91) and higher than the value of .55 reported by Possoch et al (). It was observed that the CHM‐CH was underestimated for the earliest measurement dates of a growth period and during a growth period with reduced growth due to abiotic stress (i.e.…”

“…RGB sensors mounted on UAV (drones) have been used to estimate DMY based on DEM in maize (Li et al, ), winter wheat (Yue et al, ), barley (Näsi et al, ), corn (Geipel, Link, & Claupein, ), soybean (Raymond, Cavigelli, Daughtry, Mcmurtrey, & Walthall, ) and grasslands, among others. Specifically for grasslands, Possoch et al (), Näsi et al (), Viljanen et al () and Rueda‐Ayala, Peña, Höglind, Bengochea‐Guevara, and Andújar () have demonstrated the use of regression models involving plant height and/or vegetation indices derived from data obtained using RGB or multispectral sensors in combination with UAV to estimate DMY. Possoch et al () used a simple linear regression to relate canopy height and biomass yield of 18 plots on 11 dates (in total 196 samples) obtaining an r 2 of .63.…”

Evaluation of RGB-Based Vegetation Indices From Uav Imagery to Estimate Forage Yield in Grassland

“…This is especially important for multitemporal studies of vegetation parameters (Smith & Milton 1999, Wang et al 2015. Surprisingly, the RGBVI, which performs moderate to well in other studies (Bendig et al, 2015;Bareth et al, 2016, Possoch et al 2016 shows no correlation to the observed dry matter yield. The GLI performs also weak as an estimator of biomass.…”

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

“…Field-scale biomass is typically collected by methods of destructive sampling, calibrated visual estimation, and proximal sensing measurements with spectrometers [27][28][29][30][31]. Common shortcomings exist among these methods which are time-consuming and labor intensive, thus, it is difficult to acquire vegetation biomass at large scales.…”

“…When the quality of the generated point cloud data is insufficient for distinguishing the ground and vegetation point clouds, the algorithm cannot accurately invert the vegetation height information. As a result, few studies have been conducted over grassland [29,54] and wetland [55], since the height of herbage is much smaller than that of forest and shrub. In this study, we focused on the development of a novel method for estimating the quadrat-scale aboveground biomass of low-statute vegetation.…”