International Benchmarking of the Individual Tree Detection Methods for Modeling 3-D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning

Canopy structure plays an essential role in biophysical activities in forest environments. However, quantitative descriptions of a 3-D canopy structure are extremely difficult because of the complexity and heterogeneity of forest systems. Airborne laser scanning (ALS) provides an opportunity to automatically measure a 3-D canopy structure in large areas. Compared with other point cloud technologies such as the image-based Structure from Motion, the power of ALS lies in its ability to penetrate canopies and depict subordinate trees. However, such capabilities have been poorly explored so far. In this paper, the potential of ALS-based approaches in depicting a 3-D canopy structure is explored in detail through an international benchmarking of five recently developed ALS-based individual tree detection (ITD) methods. For the first time, the results of the ITD methods are evaluated for each of four crown classes, i.e., dominant, codominant, intermediate, and suppressed trees, which provides insight toward understanding the current status of depicting a 3-D canopy structure using ITD methods, particularly with respect to their performances, potential, and challenges. This benchmarking study revealed that the canopy structure plays a considerable role in the detection accuracy of ITD methods, and its influence is even greater than that of the tree species as well as the species composition in a stand. The study also reveals the importance of utilizing the point cloud data for the detection of intermediate and suppressed trees. Different from what has been reported in previous studies, point density was found to be a highly influential factor in the performance of the methods that use point cloud data. Greater efforts should be invested in the point-based or hybrid ITD approaches to model the 3-D canopy structure and to further explore the potential of high-density and multiwavelengths ALS data

DEVELOPMENT OF SMART PRECISION FOREST IN CONIFER PLANTATION IN JAPAN USING LASER SCANNING DATA

Currently, the authors are planning to launch a consortium effort toward Japan’s first smart precision forestry project using laser data and to develop this technology throughout the country. Smart precision forestry information gathered using the Nagano model (laser scanning from aircraft, drone, and backpack) is being developed to improve the sophistication of forest information, reduce labor-intensive work, maintain sustainable timber productivity, and facilitate supply chain management by laser sensing information in collaboration with industry, academia, and government. In this paper, we outline the research project and the technical development situation of unmanned aerial vehicle laser scanning

REVISÃO SISTEMÁTICA DA LITERATURA SOBRE DETECÇÃO DE ÁRVORES UTILIZANDO DADOS DE SENSORIAMENTO REMOTO

O conhecimento do cenário atual referente formas de detecção de árvores por meio de sensoriamento remoto em plantios florestais é essencial para orientar novos estudos relacionados ao tema. Com as publicações científicas dos principais autores e periódicos do assunto dos últimos 20 anos, espera-se neste estudo delinear os principais tipos de dados, espécies e algoritmos estudados recentemente. Com o objetivo de apresentar o desenvolvimento de pesquisas em situações especificas com determinadas espécies, idades, espaçamento, tipos de dados e algoritmos, visando a contagem de árvores. Após reunir a literatura disponível nas bases selecionadas, Web of Science e a Scopus, foi realizada uma análise bibliométrica. Para a mesma utilizou-se o pacote bibliometrix desenvolvido para a linguagem R, para a filtragem de documentos e representação gráfica dos resultados. Foram encontrados resultados classificados em aceitáveis ou não aceitáveis para estimativas florestais, apresentando uma variada gama de alternativas para obtenção do número de árvores de um povoamento florestal, devido as diferenças entre os tipos de dados e algoritmos utilizados. Conclui-se que atualmente não existe uma metodologia padrão para a detecção de árvores e dificilmente será aplicável um método genérico para a contagem, visto a heterogeneidade das florestas e diferentes tratamentos de dados. Foi ainda observadas limitações quanto ao desenvolvimento de metodologias satisfatórias para plantios de Pinus taeda, regularmente espaçados e com idades próximas ao corte comercial

Benchmarking airborne laser scanning tree segmentation algorithms in broadleaf forests shows high accuracy only for canopy trees

Individual tree segmentation from airborne laser scanning data is a longstanding and important challenge in forest remote sensing. Tree segmentation algorithms are widely available, but robust intercomparison studies are rare due to the difficulty of obtaining reliable reference data. Here we provide a benchmark data set for temperate and tropical broadleaf forests generated from labelled terrestrial laser scanning data. We compared the performance of four widely used tree segmentation algorithms against this benchmark data set. All algorithms performed reasonably well on the canopy trees. The point cloud-based algorithm AMS3D (Adaptive Mean Shift 3D) had the highest overall accuracy, closely followed by the 2D raster based region growing algorithm Dalponte2016 +. However, all algorithms failed to accurately segment the understory trees. This result was consistent across both forest types. This study emphasises the need to assess tree segmentation algorithms directly using benchmark data, rather than comparing with forest indices such as biomass or the number and size distribution of trees. We provide the first openly available benchmark data set for tropical forests and we hope future studies will extend this work to other regions

Individual tree-based vs pixel-based approaches to mapping forest functional traits and diversity by remote sensing

Plant ecology and biodiversity research have increasingly incorporated trait-based approaches and remote sensing. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on airborne LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the ITC- and pixel-based approaches at changing pixel size and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by airborne Pushbroom Hyperspectral Imager-3 (PHI-3) hyperspectral data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC-based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness also decreased with increasing pixel size, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, high-resolution pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage

Tree crown segmentation in three dimensions using density models derived from airborne laser scanning

This article describes algorithms to extract tree crowns using two-dimensional (2D) and three-dimensional (3D) segmentation. As a first step, a 2D-search detected the tallest trees but was unable to detect trees located below other trees. However, a 3D-search for local maxima of model fits could be used in a second step to detect trees also in lower canopy layers. We compared tree detection results from ALS carried out at 1450 m above ground level (high altitude) and tree detection results from ALS carried out at 150 m above ground level (low altitude). For validation, we used manual measurements of trees in ten large field plots, each with an 80 m diameter, in a hemiboreal forest in Sweden (lat. 58 degrees 28' N, long. 13 degrees 38' E). In order to measure the effect of using algorithms with different computational costs, we validated the tree detection from the 2D segmentation step and compared the results with the 2D segmentation followed by 3D segmentation of the ALS point cloud. When applying 2D segmentation only, the algorithm detected 87% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 91% using low-altitude ALS data. However, when applying 3D segmentation as well, the algorithm detected 92% of the trees measured in the field using high-altitude ALS data; the detection rate increased to 99% using low-altitude ALS data. For all combinations of algorithms and data resolutions, undetected trees accounted for, on average, 0-5% of the total stem volume in the field plots. The 3D tree crown segmentation, which was using crown density models, made it possible to detect a large percentage of trees in multi-layered forests, compared with using only a 2D segmentation method

Genetic constraints on temporal variation of airborne reflectance spectra and their uncertainties over a temperate forest

Remote sensing enhances large-scale biodiversity monitoring by overcoming temporal and spatial limitations of ground-based measurements and allows assessment of multiple plant traits simultaneously. The total set of traits and their variation over time is specific for each individual and can reveal information about the genetic composition of forest communities. Measuring trait variation among individuals of one species continuously across space and time is a key component in monitoring genetic diversity but difficult to achieve with ground-based methods. Remote sensing approaches using imaging spectroscopy can provide high spectral, spatial, and temporal coverage to advance the monitoring of genetic diversity, if sufficient relation between spectral and genetic information can be established. We assessed reflectance spectra from individual Fagus sylvatica L. (European beech) trees acquired across eleven years from 69 flights of the Airborne Prism Experiment (APEX) above the same temperate forest in Switzerland. We derived reflectance spectra of 68 canopy trees and correlated differences in these spectra with genetic differences derived from microsatellite markers among the 68 individuals. We calculated these correlations for different points in time, wavelength regions and relative differences between wavelength regions. High correlations indicate high spectral-genetic similarities. We then tested the influence of environmental variables obtained at temporal scales from days to years on spectral-genetic similarities. We performed an uncertainty propagation of radiance measurements to provide a quality indicator for these correlations. We observed that genetically similar individuals had more similar reflectance spectra, but this varied between wavelength regions and across environmental variables. The short-wave infrared regions of the spectrum, influenced by water absorption, seemed to provide information on the population genetic structure at high temperatures, whereas the visible part of the spectrum, and the near-infrared region affected by scattering properties of tree canopies, showed more consistent patterns with genetic structure across longer time scales. Correlations of genetic similarity with reflectance spectra similarity were easier to detect when investigating relative differences between spectral bands (maximum correlation: 0.40) than reflectance data (maximum correlation: 0.33). Incorporating uncertainties of spectral measurements yielded improvements of spectral-genetic similarities of 36% and 20% for analyses based on single spectral bands, and relative differences between spectral bands, respectively. This study highlights the potential of dense multi-temporal airborne imaging spectroscopy data to detect the genetic structure of forest communities. We suggest that the observed temporal trajectories of reflectance spectra indicate physiological and possibly genetic constraints on plant responses to environmental change

Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

Obtaining low vegetation data is important in order to quantify the structural characteristics of a forest. Dense three-dimensional (3D) laser scanning data can provide information on the vertical profile of a forest. However, most studies have focused on the dominant and subdominant layers of the forest, while few studies have tried to delineate the low vegetation. To address this issue, we propose a framework for individual tree crown (ITC) segmentation from laser data that focuses on both overstory and understory trees. The framework includes 1) a new algorithm (SSD) for 3D ITC segmentation of dominant trees, by detecting the symmetrical structure of the trees, and 2) removing points of dominant trees and mean shift clustering of the low vegetation. The framework was tested on a boreal forest in Sweden and the performance was compared 1) between plots with different stem density levels, vertical complexities, and tree species composition, and 2) using airborne laser scanning (ALS) data, terrestrial laser scanning (TLS) data, and merged ALS and TLS data (ALS + TLS data). The proposed framework achieved detection rates of 0.87 (ALS + TLS), 0.86 (TLS), and 0.76 (ALS) when validated with field inventory data (of trees with a diameter at breast height >= 4 cm). When validating the estimated number of understory trees by visual interpretation, the framework achieved 19%, 21%, and 39% root-mean-square error values with ALS + TLS, TLS, and ALS data, respectively. These results show that the SSD algorithm can successfully separate laser points of overstory and understory trees, ensuring the detection and segmentation of low vegetation in forest. The proposed framework can be used with both ALS and TLS data, and achieve ITC segmentation for forests with various structural attributes. The results also illustrate the potential of using ALS data to delineate low vegetation

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

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline