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.

It is known that plant height is a suitable parameter for estimating crop biomass. The aim of this study was to confirm the validity of spatial plant height data, which is derived from terrestrial laser scanning (TLS), as a non-destructive estimator for biomass of paddy rice on the field scale. Beyond that, the spatial and temporal transferability of established biomass regression models were investigated to prove the robustness of the method and evaluate the suitability of linear and exponential functions. In each growing season of two years, three campaigns were carried out on a field experiment and on a farmer’s conventionally managed field. Crop surface models (CSMs) were generated from the TLS-derived point clouds for calculating plant height with a very high spatial resolution of 1 cm. High coefficients of determination between CSM-derived and manually measured plant heights (R²: 0.72 to 0.91) confirm the applicability of the approach. Yearly averaged differences between the measurements were ~7% and ~9%. Biomass regression models were established from the field experiment data sets, based on strong coefficients of determination between plant height and dry biomass (R²: 0.66 to 0.86 and 0.65 to 0.84 for linear and exponential models, respectively). The spatial and temporal transferability of the models to the farmer’s conventionally managed fields is supported by strong coefficients of determination between estimated and measured values (R²: 0.60 to 0.90 and 0.56 to 0.85 for linear and exponential models, respectively). Hence, the suitability of TLS-derived spatial plant height as a non-destructive estimator for biomass of paddy rice on the field scale was verified and the transferability demonstrated.

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed geodata in the last years (Hardin & Jensen 2011). Various applications in numerous fields of research like archaeology (Hendrickx et al., 2011), forestry or geomorphology evolved (Martinsanz, 2012). This contribution deals with the generation of multi-temporal crop surface models (CSMs) with very high resolution by means of low-cost equipment. The concept of the generation of multi-temporal CSMs using Terrestrial Laserscanning (TLS) has already been introduced by Hoffmeister et al. (2010). For this study, data acquisition was performed with a low-cost and low-weight Mini-UAV (< 5 kg). UAVs in general and especially smaller ones, like the system presented here, close a gap in small scale remote sensing (Berni et al., 2009; Watts et al., 2012). In precision agriculture frequent remote sensing on such scales during the vegetation period provides important spatial information on the crop status. Crop growth variability can be detected by comparison of the CSMs in different phenological stages. Here, the focus is on the detection of this variability and its dependency on cultivar and plant treatment. The method has been tested for data acquired on a barley experiment field in Germany. In this contribution, it is applied to a different crop in a different environment. The study area is an experiment field for rice in Northeast China (Sanjiang Plain). Three replications of the cultivars Kongyu131 and Longjing21 were planted in plots that were treated with different amounts of N-fertilizer. In July 2012 three UAV-campaigns were carried out. Establishment of ground control points (GCPs) allowed for ground truth. Additionally, further destructive and non-destructive field data were collected. The UAV-system is an MK-Okto by Hisystems (http://www.mikrokopter.de) which was equipped with the high resolution Panasonic Lumix GF3 12 megapixel consumer camera. The self-built and self-maintained system has a payload of up to 1 kg and an average flight time of 15 minutes. The maximum speed is around 30 km/h and the system can be operated up to a wind speed of less than 19 km/h (Beaufort scale number 3 for wind speed). Using a suitable flight plan stereo images can be captured. For this study, a flying height of 50 m and a 44% side and 90% forward overlap was chosen. The images are processed into CSMs under the use of the Structure from Motion (SfM)-based software Agisoft Photoscan 0.9.0. The resulting models have a resolution of 0.02 m and an average number of about 12 million points. Further data processing in Esri ArcGIS allows for quantitative comparison of the plant heights. The multi-temporal datasets are analysed on a plot size basis. The results can be compared to and combined with the additional field data. Detecting plant height with non-invasive measurement techniques enables analysis of its correlation to biomass and other crop parameters (Hansen & Schjoerring, 2003; Thenkabail et al., 2000) measured in the field. The method presented here can therefore be a valuable addition for the recognition of such correlations.

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R² < 0.85) than on spectral data (R² < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R²: 0.40–0.81) than on spectral data (R²: 0.18–0.68). Overall, this first study shows the potential of crop-specific across‑season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research.

High-resolution digital elevation models (DEMs) are useful for the detailed mapping of geomorphological features. Nowadays various sensors and platforms are available to collect 3D data. The presented study compares terrestrial laser scanning (TLS) and low-cost unmanned aerial vehicles (UAV)-based imaging in terms of their usability for capturing small-scale surface structures. In October 2014 and June 2015 measurements with both systems were carried out in an episodically water-filled karst depression under pasture farming in the region of Hohenlohe (Southwest Germany). The overall aims were to establish high-resolution DEMs and monitor changes of the relief caused by dissolution and compare the advantages and drawbacks of both systems for such studies. Due to the short time between the campaigns the clear detection of temporal changes was hardly possible. However, the multi-temporal campaigns allowed an extensive investigation of the usability of both sensors under different environmental conditions. In addition to the remote sensing measurements, the coordinates of several positions in the study area were measured with a RTK-DGPS system as independent reference data sets in both campaigns. The TLS- and UAV-derived DEM heights at these positions were validated against the DGPS-derived heights. The accuracy of the TLS-derived values is supported by low mean differences between TLS and DGPS measurements while the UAV-derived models show a weaker performance. In the future years additional simultaneous measurements with both approaches under more similar vegetation conditions are necessary to detect surface movements. Moreover, by investigating the subsurface the interaction of above and below ground processes might be detected.

The rapidly growing world population and the resulting pressure on the efficiency of agriculture require a sustainable development of intensive field management with regard to natural resources. In this context, the use of non-destructive remote sensing technologies to monitor status and change detection of plant growth is in the focus of research and application. In this contribution, we evaluate the applicability of multitemporal terrestrial laser scanning (TLS) for rice growth monitoring. The test sites are located around Jiansanjiang in Heilongjiang Province in the far northeast of China. The focus of the field experiment was on different nitrogen fertilizer inputs during the growing period in 2011. To realize the monitoring approach, three campaigns were carried out during the vegetative stage of rice plants. For all campaigns the terrestrial laser scanner Riegl VZ-1000 was used. The achieved knowledge can be described in two parts. First, for each date the variability of plant height and biomass is detectable for the whole experiment field and – more important – between the plots. Furthermore, differences in height and biomass related to edge effects can be investigated for every single plot. The spatial distribution is visualized by Crop Surface Models (CSM), which are digital surface models with a high resolution and accuracy achieved by the interpolation of the 3D point clouds. Secondly, the multitemporal surveying approach enables the monitoring of the growth rate of the rice plants. Additionally, it is possible to detect and analyze as well the spatial distribution of the changes by comparing the CSMs. Our results show that TLS is a suitable and promising method for rice growth monitoring. Furthermore, the contemporaneous surveying with other sensors enables us to validate our measurements and bares opportunities for further enhancements.