Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

Cosenza, Diogo Nepomuceno; Soares, Vicente Paulo; Leite, Hélio Garcia; Gleriani, José Marinaldo; Amaral, Cibele Hummel do; Júnior, Joel Gripp; Silva, Antonilmar Araújo Lopes da; Soares, Paula; Tomé, Margarida

doi:10.1590/s0100-204x2018001200010

Cited by 7 publications

(11 citation statements)

References 22 publications

(32 reference statements)

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“…In the case of ALS-based diameter models, our results were similar in terms of R 2 to those reported by Chisholm et al [85], who extracted forest below-canopy information using UAV-based LiDAR and developed post-processing software to detect trees and to estimate their diameters (R 2 = 0.45, rRMSE = 25.1%). Finally, the results were also similar in terms of rRMSE to those reported by Cosenza et al [86] in a eucalyptus plantation in Brazil (rRMSE = 9%), for an exponential equation with h as explanatory variable. Cosenza et al [86] also observed a slight tendency to overestimate the initial diameter values (bias = 0.12 cm).…”

Section: Discussionsupporting

confidence: 88%

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Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Guerra-Hernández

Cosenza

Cardíl³

et al. 2019

Forests

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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 from Unmanned Aerial Vehicles (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 Efficiency (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 difference 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. plantations.

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Section: Discussionsupporting

confidence: 88%

“…Finally, the results were also similar in terms of rRMSE to those reported by Cosenza et al [86] in a eucalyptus plantation in Brazil (rRMSE = 9%), for an exponential equation with h as explanatory variable. Cosenza et al [86] also observed a slight tendency to overestimate the initial diameter values (bias = 0.12 cm).…”

Section: Discussionsupporting

confidence: 88%

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Guerra-Hernández

Cosenza

Cardíl³

et al. 2019

Forests

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“…Method 1 and Method 2 provide accurate estimations of individual tree detection on Eucalyptus stands, independently of defoliation-related problems. The resulting DR with these methods is similar to the DR obtained in other studies that are focused on tree detection in Eucalyptus healthy stands: 92% [13], 97% [42], and 99% [43]. In the case of Wallace et al [45], the DR resulted in an overestimation of 100.6% using a LM-based on a seeded k-means clustering approach.…”

Section: Discussionsupporting

confidence: 80%

“…This approach is mostly suited to the estimation of a number of averaged stand attributes over large areas such as biomass, volume, basal area, mean diameter, or average height. The other approach is the individual tree detection (ITD), which refers to partitioning the point cloud into objects representing single trees or groups of trees by using certain segmentation algorithms [13]. It is the basic unit for analysis in forestry applications such as monitoring forest regeneration [14], sustainable forest management [15], biomass and carbon stock estimation [16], and wildland fire risk assessment [17,18].…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

et al. 2020

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The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

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“…A densidade de pontos com diferenças mínimas quando comparadas as duas maiores densidades, os autores consideraram que nuvens mais densas que 3 p/m² não trazem muitos benefícios, visto que as estimativas atingidas não ultrapassaram 82% de acurácia. Cosenza et al (2018) realizaram a contagem e estimativa de volume para povoamentos florestais de Eucalyptus ssp. de 4 a 7 anos de idade, aplicando o algoritmo de máximos locais atingindo níveis satisfatórios na contagem de árvores, desde que os resultados sejam calibrados com inventários em campo.…”

Section: Artigo De Conferências 25unclassified

Revisão Sistemática Da Literatura Sobre Detecção De Árvores Utilizando Dados De Sensoriamento Remoto

Mlenek

Corte

Santos³

et al. 2019

BIOFIX

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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.

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Airborne laser scanning applied to eucalyptus stand inventory at individual tree level

Cited by 7 publications

References 22 publications

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR

Revisão Sistemática Da Literatura Sobre Detecção De Árvores Utilizando Dados De Sensoriamento Remoto

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