The increase of vegetation greenness in the Northern latitudes suggests a rise in the fixation of CO2 by photosynthesis, but the observed upward trends in respiration could compensate for elevated uptake by photosynthesis, necessitating the monitoring of variation in vegetation structure and carbon (C) storage at very high spatio-temporal resolution. Compared to passive optical remote sensing, Light Detection and Ranging (Lidar) scanners may improve the quantification of C sink by providing 3D information of plant structures without apparent sign of saturation of spectral response over dense canopies. We evaluate a novel approach to precisely map C sequestration and key metrics describing the 3D canopy structure of a temperate agricultural expanse by implementing drone-borne Lidar scanner technology and deep learning (DL) architectures potentially capable of detecting individual plants and associated geometrical properties while deriving their above ground biomass (AGB) from point cloud datasets originating from the scanner. An intensive aerial and field campaign was carried out over an Integrated Carbon Observation System (ICOS) class 1 station site (60 ha) in Denmark to remotely measure the horizontal and vertical canopy structure at 15-day intervals during the vegetation growing period, and to collect ground truth data of crop growth in terms of height, density, AGB and green area index of more than 1200 plants. The point cloud data are processed using pattern recognition tools to remove noise and classify them to ground and non-ground points. Two DL models specifically designed to handle the irregular structure of raw point clouds are trained to extract features of vegetation by labeling the processed point cloud data; DL’s suitability for assigning semantic information on 3D data representing cropland is assessed by validating them with the field-based observations. In combination with tower-based flux data, the application of Lidar and DL technologies appear to offer a characterization of the dynamic interaction between climatic conditions, vegetation growth, C sink, water and CO2 fluxes suitable to the challenge of assessing the rapidly changing northern landscapes.

Advances in remote sensing technology, notably in UAV Light Detection and Ranging (Lidar), may yield to better predictions of the implications of land cover alterations on greenhouse gas (GHG) exchanges by facilitating the acquisition of high-resolution information on surface topography and vegetation structure. Although surface morphology is fundamentally related to the magnitude of aerodynamic roughness length (zo) and zero place displacement height (d), assigning appropriate values to estimate energy and GHG fluxes remains challenging. In this study, we evaluate the effectiveness of a workflow for processing small-footprint point clouds from a UAV-Lidar system, multispectral and thermal infrared data in order to obtain necessitated parameters for calculation of water vapor fluxes over a mixed canopy, populated by agricultural vegetation and evergreen trees in Denmark. Point cloud data are classified into ground and vegetation using the progressive triangulated irregular network densification algorithm, and are interpolated with the kriging method to generate canopy height models (CHMs) with 0.5 m pixel resolution. CHMs are then delineated using the watershed algorithm to extract geometrical characteristics, orientation and spacing of the low and high vegetation and assign them to four morphometric roughness models that calculate zo and d. The rasterized aerodynamic resistance maps are rectified with thermal and multispectral orthomosaics to obtain the spatial distribution of available energy, sensible heat and water vapor fluxes by incorporating these terms into a surface energy balance model. The Lidar-derived geometric attributes are validated with selected ground truth data and the modeled water vapor fluxes are compared with eddy covariance measurements. Derivation of more precise high-resolution aerodynamic parameters and reflectance characteristics from UAV-based instrumentation can increase the accuracy of water flux estimates of a canopy under surface heterogeneity conditions and may confine the uncertainty in describing the propagation of their long-term effects on ecosystem’s resilience.