A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest

Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way

Al-Robotics team: A cooperative multi-unmanned aerial vehicle approach for the Mohamed Bin Zayed International Robotic Challenge

The Al-Robotics team was selected as one of the 25 finalist teams out of 143 applications received to participate in the first edition of the Mohamed Bin Zayed International Robotic Challenge (MBZIRC), held in 2017. In particular, one of the competition Challenges offered us the opportunity to develop a cooperative approach with multiple unmanned aerial vehicles (UAVs) searching, picking up, and dropping static and moving objects. This paper presents the approach that our team Al-Robotics followed to address that Challenge 3 of the MBZIRC. First, we overview the overall architecture of the system, with the different modules involved. Second, we describe the procedure that we followed to design the aerial platforms, as well as all their onboard components. Then, we explain the techniques that we used to develop the software functionalities of the system. Finally, we discuss our experimental results and the lessons that we learned before and during the competition. The cooperative approach was validated with fully autonomous missions in experiments previous to the actual competition. We also analyze the results that we obtained during the competition trials.Unión Europea H2020 73166

Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with AT

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

LIDAR obstacle warning and avoidance system for unmanned aerial vehicle sense-and-avoid

The demand for reliable obstacle warning and avoidance capabilities to ensure safe low-level flight operations has led to the development of various practical systems suitable for fixed and rotary wing aircraft. State-of-the-art Light Detection and Ranging (LIDAR) technology employing eye-safe laser sources, advanced electro-optics and mechanical beam-steering components delivers the highest angular resolution and accuracy performances in a wide range of operational conditions. LIDAR Obstacle Warning and Avoidance System (LOWAS) is thus becoming a mature technology with several potential applications to manned and unmanned aircraft. This paper addresses specifically its employment in Unmanned Aircraft Systems (UAS) Sense-and-Avoid (SAA). Small-to-medium size Unmanned Aerial Vehicles (UAVs) are particularly targeted since they are very frequently operated in proximity of the ground and the possibility of a collision is further aggravated by the very limited see-and-avoid capabilities of the remote pilot. After a brief description of the system architecture, mathematical models and algorithms for avoidance trajectory generation are provided. Key aspects of the Human Machine Interface and Interaction (HMI2) design for the UAS obstacle avoidance system are also addressed. Additionally, a comprehensive simulation case study of the avoidance trajectory generation algorithms is presented. It is concluded that LOWAS obstacle detection and trajectory optimisation algorithms can ensure a safe avoidance of all classes of obstacles (i.e., wire, extended and point objects) in a wide range of weather and geometric conditions, providing a pathway for possible integration of this technology into future UAS SAA architectures