A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment.

Unmanned Aerial Systems (UAS) have emerged as a capable platform for measuring vegetation health, structure and productivity. Products derived from UAS imagery typically have much finer spatial resolutions than traditional satellite or aircraft imagery, allowing the spectral and structural heterogeneity of vegetation to be mapped and monitored with more detail. This study uses UAS-captured imagery from the Chobe Enclave of northern Botswana. Flights were conducted across a gradient of savanna sites classified as grass-, shrub-, or tree-dominated. We compare multiple approaches for extracting woody vegetation structure from UAS imagery and assess correlations between in situ field measurements and UAS estimates. Sensor types were also compared, to determine whether multispectral data improves estimates of vegetation structure at the expense of spatial resolution. We found that leveraging multispectral reflectance information aids in crown delineation, areal estimates, and fractional cover of woody and non-woody vegetation within the study area. Comparisons are made between two crown delineation techniques, and the efficacy of each technique within savanna environments is discussed. The methods presented hold potential to inform field sampling protocols and UAS-based techniques for autonomous crown delineation in future dryland systems research. These findings advance research for field and remote sensing analyses assessing degradation in heterogeneous landscapes where varying vegetation structure has implications on land use and land functions

Tree Species Traits Determine the Success of LiDAR-based Crown Mapping in a Mixed Temperate Forest

Automated individual tree crown delineation (ITCD) via remote sensing platforms offers a path forward to obtain wall-to-wall detailed tree inventory/information over large areas. While LiDAR-based ITCD methods have proven successful in conifer dominated forests, it remains unclear how well these methods can be applied broadly in deciduous broadleaf (hardwood) dominated forests. In this study, I applied five common automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to hardwood- dominated at the Harvard Forest in Petersham, MA, USA, and assess accuracy against manually delineation crowns. I then identified basic tree- and plot-level factors influencing the success of delineation techniques. My results showed that automated crown delineation shows promise in closed canopy mixed-species forests. There was relatively little difference between crown delineation methods (51-59% aggregated plot accuracy), and despite parameter tuning, none of the methods produce high accuracy across all plots (27 – 90% range in plot-level accuracy). I found that all methods delineate conifer species (mean 64%) better than hardwood species (mean 42%), and that accuracy of each method varied similarly across plots and was significantly related to plot-level conifer fraction. Further, while tree-level factors related to tree size (DBH, height and crown area) all strongly influenced the success of crown delineations, the influence of plot-level factors varied. Species evenness (relative species abundance) was the most important plot-level variable controlling crown delineation success, and as species evenness decreased, the probability of successful delineation increased. Evenness was likely important due to 1) its negative relationship to conifer fraction and 2) a relationship between evenness and increased canopy space filling efficiency. Overall, my work suggests that the ability to delineate crowns is not strongly driven by methodological differences, but instead driven by differences in functional group (conifer vs. hardwood) tree size and diversity and how crowns are displayed in relation to each other. While LiDAR-based ITCD methods are well suited for conifer dominated plots with distinct canopy structure, they remain less reliable in hardwood dominated plots. I suggest that future work focus on integrating phenology and spectral characteristics with existing LiDAR approaches to better delineate hardwood dominated stands

A fast and reliable method for the delineation of tree crown outlines for the computation of crown openness values and other crown parameters

Numerous crown parameters (e.g., leaf area index, diameter, height, volume) can be obtained via the analysis of tree crown photographs. In all cases, parameter values are functions of the position of the crown outline. However, no standardized method to delineate crowns exists. To explore the effect of different outlines on tree crown descriptors, in this case crown openness (CO), and facilitate the adoption of a standard method free of user bias, we developed the program Crown Delineator that automatically delineates any outline around tree crowns following predetermined sensibility settings. We used different outlines to analyze tree CO in contrasting settings: using saplings from four species in young boreal mixedwood forests and medium-sized hybrid poplar trees from a low-density plantation. In both cases, the estimated CO increases when calculated from a looser outline, which had a strong influence on understory available light simulations using a forest simulator. These results demonstrate that the method used to trace crown outlines is an important step in the determination of CO values. We provide a much-needed computer-assisted solution to help standardize this procedure, which can also be used in many other situations in which the delineation of tree crowns is needed (e.g., competition and crown shyness)

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

Single-tree detection in high-density LiDAR data from UAV-based survey

UAV-based LiDAR survey provides very-high-density point clouds, which involve very rich information about forest detailed structure, allowing for detection of individual trees, as well as demanding high computational load. Single-tree detection is of great interest for forest management and ecology purposes, and the task is relatively well solved for forests made of single or largely dominant species, and trees having a very evident pointed shape in the upper part of the canopy (in particular conifers). Most authors proposed methods based totally or partially on search of local maxima in the canopy, which has poor performance for species that have flat or irregular upper canopy, and for mixed forests, especially where taller trees hide smaller ones. Such considerations apply in particular to Mediterranean hardwood forests. In such context, it is imperative to use the whole volume of the point cloud, however keeping computational load tractable. The authors propose the use of a methodology based on modelling the 3D-shape of the tree, which improves performance w.r.t to maxima-based models. A case study, performed on a hazel grove, is provided to document performance improvement on a relatively simple, but significant, case

Forest Remote Sensing in Canada and the Individual Tree Crown (ITC) Approach to Forest Inventories

After a brief description of Canada’s forest situation and the role of the federal government in forestry, some Natural Resources Canada’country-wide project will be introduced. These include the National Forest Inventories (past and present), the National Forest Information System, the EOSD programs to map land cover, monitor change and evaluate biomass, mostly from Canada-wide coverages with Landsat images. The accounting of carbon and the monitoring of deforestation at a map scale level will also be introduced. The second and most significant part of this paper will describe our Individual Tree Crown (ITC) approach to forest inventories used with high spatial resolution images (better than 1m/pixel). Techniques for individual crown delineation, species classification and regrouping into forest stands that are leading to a semi-automatic production of forest inventories will be described.A locally adaptive technique for tree counts, mostly reserved for young regenerating areas, will also be presented. The synergy of multispectral and LIDAR data (atmany levels) will be examined and, the normalization of spectral values within and among aerial images will be considered.Article信州大学農学部紀要. 46(1-2): 85-92 (2010)departmental bulletin pape

Using multi-spectral UAV imagery to extract tree crop structural properties and assess pruning effects

Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e., tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m(2), and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV-based approach for larger orchards. The different flying heights (i.e., 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements

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