In this study, we present the first findings of the potential utility of miniaturized light and detection ranging (LiDAR) scanners mounted on unmanned aerial vehicles (UAVs) for improving urban flood modelling and assessments at the local scale. This is done by generating ultra-high spatial resolution digital terrain models (DTMs) featuring buildings and urban microtopographic structures that may affect floodwater pathways (DTMbs). The accuracy and level of detail of the flooded areas, simulated by a hydrologic screening model (Arc-Malstrøm), were vastly improved when DTMbs of 0.3 m resolution representing three urban sites surveyed by a UAV-LiDAR in Accra, Ghana, were used to supplement a 10 m resolution DTM covering the region’s entire catchment area. The generation of DTMbs necessitated the effective classification of UAV-LiDAR point clouds using a morphological and a triangulated irregular network method for hilly and flat landscapes, respectively. The UAV-LiDAR data enabled the identification of archways, boundary walls and bridges that were critical when predicting precise run-off courses that could not be projected using the coarser DTM only. Variations in a stream’s geometry due to a one-year time gap between the satellite-based and UAV-LiDAR data sets were also observed. The application of the coarser DTM produced an overestimate of water flows equal to 15% for sloping terrain and up to 62.5% for flat areas when compared to the respective run-offs simulated from the DTMbs. The application of UAV-LiDAR may enhance the effectiveness of urban planning by projecting precisely the locations, extents and run-offs of flooded areas in dynamic urban settings.

Current endeavors to enhance the accuracy of in situ above-ground biomass (AGB) prediction for croplands rely on close-range monitoring surveys that use unstaffed aerial vehicles (UAVs) and mounted sensors. In precision agriculture, light detection and ranging (LiDAR) technologies are currently used to monitor crop growth, plant phenotyping, and biomass dynamics at the ecosystem scale. In this study, we utilized a UAV–LiDAR sensor to monitor two crop fields and a set of machine learning (ML) methods to predict real-time AGB over two consecutive years in the region of Mid-Jutland, Denmark. During each crop growing period, UAV surveys were conducted in parallel with AGB destructive sampling every 7–15 days, the AGB samples from which were used as the ground truth data. We evaluated the ability of the ML models to estimate the real-time values of AGB at a sub-meter resolution (0.17–0.52 m²). An extremely randomized trees (ERT) regressor was selected for the regression analysis, based on its predictive performance for the first year’s growing season. The model was retrained using previously identified hyperparameters to predict the AGB of the crops in the second year. The ERT performed AGB estimation using height and reflectance metrics from LiDAR-derived point cloud data and achieved a prediction performance of $R^2$ = 0.48 at a spatial resolution of 0.35 m². The prediction performance could be improved significantly by aggregating adjacent predictions ($R^2$ = 0.71 and $R^2$ = 0.93 at spatial resolutions of 1 m² and 2 m², respectively) as they ultimately converged to the reference biomass values because any individual errors averaged out. The AGB prediction results were examined as function of predictor type, training set size, sampling resolution, phenology, and canopy density. The results demonstrated that when combined with ML regression methods, the UAV–LiDAR method could be used to provide accurate real-time AGB prediction for crop fields at a high resolution, thereby providing a way to map their biochemical constituents.

Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation structure at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently above-ground carbon stocks directly from airborne LiDAR point clouds. Specifically, we devise different neural network architectures for point cloud regression and evaluate them on remote sensing data of areas for which AGB estimates have been obtained from field measurements in a national forest inventory. Our adaptation of Minkowski convolutional neural networks for regression gave the best results. The deep neural networks produced significantly more accurate wood volume, AGB, and carbon estimates compared to state-of-the-art approaches operating on basic statistics of the point clouds. In contrast to other methods, no digital terrain model is required. We expect this finding to have a strong impact on LiDAR-based analyses of terrestrial ecosystem dynamics.

The aerodynamic roughness length (Z₀) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z₀ maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z₀ maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z₀ values from a nearby eddy covariance tower (Z₀_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z₀ from UAV-LiDAR-driven models converged with Z₀_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z₀ conditioned by vertical and horizontal vegetation density. The Z₀ calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z₀_EC. Deriving Z₀ in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites.

With rising demand for increasing the yield potential of agricultural products and for reducing greenhouse gas emissions during food production, strengthening our scientific and technological capacity to monitor crop growth and above ground biomass (AGB) is indispensable to move towards more sustainable management of our agricultural resources. Pivotal to meet this goal is the application of high-throughput field-phenotyping tools such as drone borne Light Detection and Ranging (Lidar) systems for accurate, fine-grained, rapid and labor-saving measurements of vegetation growth parameters. Our objective is to develop and assess a workflow to estimate AGB, leaf area index (LAI), plant height (PH) and volume of a homogeneous and highly dense agricultural field using the capabilities of UAV-Lidar technology. The experimental site is located in Denmark and populated by potato plants. Aerial campaigns and field experiments, including destructive biomass sampling and measurements of LAI and plants’ geometrical characteristics at 1m2 square plots, were performed once per month during the vegetation growth period (May–September 2019). The high resolution (3.6 cm) Canopy Height model (CHM) is generated by first evaluating the performance of different filtering algorithms that separate the ground points from the Lidar-derived point cloud datasets. To extract the geometrical parameters of individual crop plants, we delineate the CHM by applying segmentation directly to the Lidar point cloud rather than segmenting the CHM as an interpolated raster surface. The PH obtained by the Lidar scanner is highly correlated with the field-measured PH (R²=0.89 and RMSE=0.028 m) implying that the point cloud data processing evaluated here is efficient and able to generate serviceably accurate CHMs for agricultural sites with similar vegetation structures. Throughout the observed vegetation growth period, the AGB can be quantified with high accuracy when it is considered to be a function of plant volume (R²=0.81 and RMSE=31.65 %) rather than a function of PH, as the latter approximating an exponential relationship with AGB. Height and density Lidar metrics were more effective in predicting in situ LAI measurements in comparison with remotely sensed LAI calculated directly from Lidar vegetation points following the Beer Lambert law. The predictive frameworks emerging from this approach indicate the applicability of drone borne Lidar systems for obtaining agricultural crop growth parameters in both high spatial and temporal resolution.

Rapidly expanding cities are exposed to higher damage potential from floods, necessitating effective proactive management using technological developments in remote sensing observations and hydrological modelling.  In this study we tested whether high resolution topographic data derived by Light and Detection Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) systems can facilitate rapid and precise identification of high-risk urban areas, at the local scale. Three flood prone areas located within the Greater Accra Metropolitan Area in Ghana were surveyed by a UAV-LiDAR system. In order to simulate a realistic flow of precipitation runoff on terrains, Digital Terrain Models (DTM) including buildings and urban features that may have a substantial effect on water flow pathways (DTMb) were generated from the UAV-LiDAR datasets. The resulting DTMbs, which had a spatial resolution of 0.3 m supplemented a satellite-based DTM of 10 m resolution covering the full catchment area of Accra, and applied to a hydrologic screening model (Arc-Malstrøm) to compare the flood simulations. The precision of the location, extent and capacity of landscape sinks were substantially improved when the DTMbs were utilized for mapping the flood propagation. The semi-low resolution DTM projected unrealistically shallower sinks, with larger extents but smaller capacities that consequently led to an overestimation of the runoff volume by 15% for a sloping site, and up to 65 % for 1st order sinks in flat terrains. The observed differences were attributed to the potential of high resolution DTMbs to detect urban manmade features like archways, boundary walls and bridges which were found to be critical in predictions of runoff’s courses, but could not be captured by the coarser DTM. Discrepancies in the derived water volumes using the satellite-based DTM vs. the UAV-LiDAR DTMbs were also traced to dynamic alterations in the geometry of streams and rivers, due to construction activities occurring in the interval between the aerial campaign and the date of acquisition of the commercially available DTM. Precise identification of urban flood prone areas can be enhanced using UAV-LiDAR systems, facilitating the design of comprehensive early flood-control measures, especially in urban settlements exposed to the adverse effects of perennial flooding. This research is funded by a grant awarded by the Danish Ministry of Foreign Affairs (Danida).