Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection

Abstract

Aim of study: In this study we applied 3D point clouds generated by images obtained from an Unmanned Aerial Vehicle (UAV) to evaluate the uniformity of young forest stands.Area of study: Two commercial forest stands were selected, with two plots each. The forest species studied were Eucalyptus spp. and Pinus taeda L. and the trees had an age of 1.5 years.Material and methods: The individual trees were detected based on watershed segmentation and local maxima, using the spectral values stored in the point cloud. After the tree detection, the heights were calculated using two approaches, in the first one using the Digital Surface Model (DSM) and a Digital Terrain Model, and in the second using only the DSM. We used the UAV-derived heights to estimate an uniformity index.Main results: The trees were detected with a maximum 6% of error. However, the height was underestimated in all cases, in an average of 1 and 0.7 m for Pinus and Eucalyptus stands. We proposed to use the models built herein to estimate tree height, but the regression models did not explain the variably within the data satisfactorily. Therefore, the uniformity index calculated using the direct UAV-height values presented results close to the field inventory, reaching better results when using the second height approach (error ranging 2.8-7.8%).Research highlights: The uniformity index using the UAV-derived height from the proposed methods was close to the values obtained in field. We noted the potential for using UAV imagery in forest monitoring.

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

“…The tendency of ALS and UAV-based DAP technologies to underestimate h may be the main reason for the underestimation of v with 1st approach. It should be also borne in mind that the ALS and UAV-based DAP, as a tree height estimation technique, tends to underestimate tree height (e.g., DAP [39,90,91], UAV-based DAP [47,74,76,92] and ALS [73,77,[93][94][95] point cloud data). However, our volume modelling results suggest that this bias may not influence in volume estimations using the 2nd approach, leaving open the question as to when and where specific models should be developed for correcting the bias at tree level depending on particular species or forest structure [77].…”

“…Finally, ALS and SfM approaches to tree height estimation tend to underestimate tree height [73,74,76,77]. Recent studies [77] have presented a model that explains the observed bias using probability theory, developing methods for correcting several ALS metrics used for ABA prediction of stand structure.…”

Desenvolvimento De Software Para Dimensionamento De Silos Multicelulares De Concreto Armado

“…Não diferente desse cenário, a silvicultura tem incorporado as evoluções da indústria 4.0, a fi m de otimizar a cadeia de produção fl orestal, trazer informações mais precisas, incrementar a produtividade e reduzir custos, desde a produção de mudas até ao consumidor fi nal, trazendo consigo diversos avanços, o que ocasionou a evolução para a fronteira da silvicultura 4.0. -GRANADOS et al, 2016a;LÓPEZ-GRANADOS et al, 2016b;DASH et al, 2017;RUZA et al, 2017;FEDUCK;MCDERMID;CASTILLA, 2018;HENTZ et al, 2018 Net, 2017;BONNEAU et al, 2017).…”

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

“…Conforme resultados da revisão sistemática da literatura é possível observar que as ARP foram utilizadas em diversos estudos florestais voltados a individualização de árvores. Hentz et al (2018) utilizaram ARP na coleta de dados com sensor passivo com a finalidade de gerar nuvens de pontos tridimensionais, por correlação fotogramétrica em florestas de Eucalyptus spp. e Pinus taeda L. com idade de 1,5 anos, para posterior individualização baseada em segmentação de bacias hidrográficas e máximos locais atingindo um erro máximo de 6%.…”