Abstract
Abstract. In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is performed by using a range-correction function f(r), which is derived from lab experiments with a reference target of known reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for natural surface classification, particularly in combination with geometric features.
Highlights
Mapping and characterization of the three-dimensional nature of vegetation is increasingly gaining in importance in many fields of applications
The exploration of the features shows a good separability between harvest residues and bare soil
The study at hand shows the potential of LiDAR data for harvest residues detection and coverage mapping if using both radiometric information and geometric description
Summary
Mapping and characterization of the three-dimensional nature of vegetation is increasingly gaining in importance in many fields of applications. Several studies indicate a high value for 3D vegetation description, such as in agricultural monitoring of trees (Rosell and Sanz, 2012; Seidel et al, 2011), field crops (Höfle, 2013; Saeys et al, 2009; Lumme et al, 2008) or harvest residues (Lenaerts et al, 2012). Knowledge of the quantity and the spatial distribution of harvest residues are important for the determination of surface properties. Hyperspectral (HS) remote sensing is used in agriculture, mapping crop characteristics (Jarmer, 2013; Thenkabail, 2000), the assessment of crop residues (Daughtry, 2004) and the assessment of soil properties (Jarmer et al, 2008; Barnes and Baker, 2000). The captured mixed signal in case of existing harvest residues can be interpreted and considered in further analysis
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