Nora Tilly

Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice

Abstract Appropriate field management requires methods of measuring plant height with high precision, accuracy, and resolution. Studies show that terrestrial laser scanning (TLS) is suitable for capturing small objects like crops. In this contribution, the results of multitemporal TLS surveys for monitoring plant height on paddy rice fields in China are presented. Three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. The high density of measurement points allows us to establish crop surface models with a resolution of 1 cm, which can be used for deriving plant heights. For both sites, strong correlations (each R 2 = 0.91 between TLS-derived and manually measured plant heights confirm the accuracy of the scan data. A biomass regression model was established based on the correlation between plant height and biomass samples from the field experiment ( R 2 = 0.86 ). The transferability to the farmer’s field was supported with a strong correlation between simulated and measured values ( R 2 = 0.90 ). Independent biomass measurements were used for validating the temporal transferability. The study demonstrates the advantages of TLS for deriving plant height, which can be used for modeling biomass. Consequently, laser scanning methods are a promising tool for precision agriculture.

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R2: 0.07–0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations.

Correction: Tilly, N. et al. Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass. Remote Sens. 2015, 7, 11449-11480

. Unfortunately, this step was missed out.All analyses were re-executed based on the correct values, and the corresponding tables andfigures are presented in the same order as in the paper in the following Tables1–3, Figure1–3. Thus,the stated sensitivity thresholds for the saturation of the NDVI and RGBVI must also be correctedto be about 185 g/m