Abstract
Abstract. Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.
Highlights
A major topic in the field of precision agriculture (PA) is the enhancement of crop management due to the constant or even decreasing cultivation area but concurrently growing world population (Oliver, 2013)
The terrestrial laser scanning (TLS)-derived point clouds were interpolated to generate a Crop Surface Models (CSM) of the whole maize field for each campaign
In the study presented in this paper, the laser scanner was mounted on a cherry picker
Summary
A major topic in the field of precision agriculture (PA) is the enhancement of crop management due to the constant or even decreasing cultivation area but concurrently growing world population (Oliver, 2013). Depending on the investigated parameters and desired resolution various sensors and methods are applied. Studies focusing on maize plants have a particular challenge in common. In contrast to other crops, tall maize plants with heights of about 3 m complicate ground based nadir measurements. As demonstrated by Claverie et al (2012), spectral satellite data has promising potential for large-scale crop monitoring and biomass estimation. Ground based observations are conducted to achieve a high resolution and enable the detection of infield variability. Studies show the potential of passive hyperspectral hand-held sensors for biomass estimations (Teal et al, 2006; Osborne et al, 2002). Perbandt et al (2011) compared nadir and off-nadir hyperspectral measurements and detected a significant influence of sensor height and measuring angle
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