High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders

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

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

DFG, 357874777, FOR 2694: Large-Scale and High-Resolution Mapping of Soil Moisture on Field and Catchment Scales - Boosted by Cosmic-Ray NeutronsDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation

This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.The vast size of oil palm (Elaeis guineensis) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 31 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (R2= 0.63) for 7 year old and 0.45 m (R2=0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.European CommissionMarie Skłodowska-Curi

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Image Processing in Dense Forest Areas using Unmanned Aerial System (UAS)

Description: A detailed workflow using Structure from Motion (SfM) techniques for processing high-resolution Unmanned Aerial System (UAS) NIR and RGB imagery in a dense forest environment where obtaining control points is difficult due to limited access and safety issues. Abstract: Imagery collected via Unmanned Aerial System (UAS) platforms has become popular in recent years due to improvements in a Digital Single-Lens Reflex (DSLR) camera (centimeter and sub-centimeter), lower operation costs as compared to human piloted aircraft, and the ability to collect data over areas with limited ground access. Many different application (e.g., forestry, agriculture, geology, archaeology) are already using and utilizing the advantages of UAS data. Although, there are numerous UAS image processing workflows, for each application the approach can be different. In this study, we developed a processing workflow of UAS imagery collected in a dense forest (e.g., coniferous/deciduous forest and contiguous wetlands) area allowing users to process large datasets with acceptable mosaicking and georeferencing errors. Imagery was acquired with near-infrared (NIR) and red, green, blue (RGB) cameras with no ground control points. Image quality of two different UAS collection platforms were observed. Agisoft Metashape, a photogrammetric suite, which uses SfM (Structure from Motion) techniques, was used to process the imagery. The results showed that an UAS having a consumer grade Global Navigation Satellite System (GNSS) onboard had better image alignment than an UAS with lower quality GNSS