Helge Aasen

Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis highlights the necessity to consider angular effects in optical sensors when observing vegetation. We compare the measurements of the UAV goniometer to the angular modules of the SCOPE radiative transfer model. Model and measurements are in high accordance (r2 = 0.88) in the infrared region at angles close to nadir; in contrast the comparison show discrepancies at low tilt angles (r2 = 0.25). This study demonstrates that the UAV goniometer is a promising approach for the fast and flexible assessment of angular effects.

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.

Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor

(Accepted: photogrammetric engineering & remote sensing, forthcoming August 2014) Helge Aasen; Martin Leon Gnyp; Yuxin Miao; Georg Bareth 1 Institute of Geography, University of Cologne, Albertus.Magnus-Platz, 50923 Cologne, Germany (helge.aasen, mgnyp1, g.bareth @uni-koeln.de). 2 College of Resources and Environmental Science, China Agricultural University, 100193 Beijing, China (ymiao2007@gmail.com). 3 International Center for Agro-Informatics and Sustainable Development (www.icasd.org). * Corresponding author

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

Towards robust vegetation indices: The multi-correlation matrix strategy

Hyperspectral vegetation indices (HVIs) have shown great potential for characterizing and monitoring vegetation and agricultural crops. Additionally, hyperspectral data becomes more commonly available. Latter may be used to address varying annual crop growth. In this paper we describe the multi-correlation matrix strategy as a new approach to derive robust HVIs from multiple hyperspectral field spectrometers datasets. The approach combines the information from multiple correlation matrices (CMs). The software HyperCor is used to automate the data pre-processing and CMs computation. In this study we use data from three growth stages (tillering, stem elongation, heading) in five years (2007–2009, 2011 and 2012) to estimate rice biomass. The new approach is validated through leave-one-out cross-validation and compared to results from a direct approach. On average the multi-correlation matrix approach showed 15% better performance and could reduce the RMSE compared to the direct approach.