Remote sensing data fusion as a tool for biomass prediction in extensive grasslands invaded by L. polyphyllus

Remote sensing data fusion is a powerful tool to gain information of quantitative and qualitative vegetation properties on field level. The aim of this study was to develop prediction models from sensor data fusion for fresh and dry matter yield (FMY/DMY) in extensively managed grasslands with variable degree of invasion byL. polyphyllus. Therefore, a terrestrial 3d laser scanner (TLS) and a drone-based hyperspectral camera was used to collect high resolution 3d point clouds and hyperspectral aerial orthomosaics of four extremely heterogenous grasslands. From 3d point clouds multiple features (vegetation height, sum of voxel, point density and surface structure) were extracted and combined with hyperspectral data to develop an optimized biomass model from random forest regression algorithm to predict FMY and DMY (n(train) = 130, n(test) = 33). Models from hyperspectral data solitarily had the lowest prediction performance (FMY:R-2 = 0.61, nRMSE(r) = 17.14; DMY:R-2 = 0.59, nRMSE(r) = 19.37). Higher performance was gained by models derived from 3d laser data (FMY:R-2 = 0. 76, nRMSE(r) = 13.3; DMY:R-2 = 0. 74, nRMSE(r) = 15.1). A fusion of both sensor systems increased the FMY prediction performance up toR(2) = 0.8; nRMSE(r) = 12.02 and the DMY prediction performance toR(2) = 0.81 and nRMSE(r) = 12.06. The fusion of complementary sensor systems can increase the power to predict biomass yields of heterogenous and extensively managed grasslands. It is a novel alternative to labour-intensive, traditional biomass prediction methods and to remote sensing methods using only single sensor data.

Automated summary

Remote sensing data fusion can be a powerful tool for predicting biomass yields in extensively managed grasslands, with higher prediction performance achieved by fusion of multiple sensor systems. The combination of complementary sensor data provides a novel alternative to traditional labor-intensive methods and single sensor data remote sensing techniques.