Individual Tree Attribute Estimation and Uniformity Assessment in Fast-Growing Eucalyptus spp. Forest Plantations Using Lidar and Linear Mixed-Effects Models

Author(s):Show less -

Leite, Rodrigo Vieira ^[1] ; Silva, Carlos Alberto ^{[2, 3]} ; Mohan, Midhun ^[4] ; Cardil, Adrian ^[5] ; Alves de Almeida, Danilo Roberti ^[6] ; Chaves e Carvalho, Samuel de Padua ^[7] ; Jaafar, Wan Shafrina Wan Mohd ^[8] ; Guerra-Hernandez, Juan ^{[9, 10]} ; Weiskittel, Aaron ^[11] ; Hudak, Andrew T. ^[12] ; Broadbent, Eben N. ^[13] ; Prata, Gabriel ^[13] ; Valbuena, Ruben ^[14] ; Leite, Helio Garcia ^[1] ; Taquetti, Mariana Futia ^[1] ; Vieira Soares, Alvaro Augusto ^[15] ; Scolforo, Henrique Ferraco ^[16] ; do Amaral, Cibele Hummel ^[1] ; Dalla Corte, Ana Paula ^[17] ; Klauberg, Carine ^[18]

Affiliation:Show less -

^[1] Univ Fed Vicosa, Dept Forest Engn, BR-36570900 Vicosa, MG - Brazil

^[2] Univ Florida, Sch Forest Resources & Conservat, Gainesville, FL 32611 - USA

^[3] Univ Maryland, Dept Geog Sci, College Pk, MD 20740 - USA

^[4] Univ Calif Berkeley, Dept Geog, Berkeley, CA 94709 - USA

^[5] Tecnosylva, Parque Tecnol Leon, Leon 24009 - Spain

^[6] Univ Sao Paulo, Luiz de Queiroz Coll Agr USP ESALQ, Dept Forest Sci, Av Padua Dias 11, BR-13418900 Piracicaba, SP - Brazil

^[7] Univ Fed Mato Grosso, Coll Forestry, Av Fernando Correa da Costa 2367, BR-78060900 Cuiaba, MT - Brazil

^[8] Natl Univ Malaysia UKM, Earth Observat Ctr, Inst Climate Change, Bangi 43600 - Malaysia

^[9] Fdn CEL, Ctr Iniciat Empresariais, O Palomar S-N, Lugo 27004 - Spain

^[10] Univ Lisbon, Forest Res Ctr, Sch Agr, Inst Super Agron, P-1649004 Lisbon - Portugal

^[11] Univ Maine, Ctr Res Sustainable Forests, 5755 Nutting Hall, Orono, ME 04469 - USA

^[12] US Forest Serv, USDA, Rocky Mt Res Stn, 1221 South Main St, Ft Collins, CO 80526 - USA

^[13] Univ Florida, Sch Forest Resources & Conservat, Spatial Ecol & Conservat Lab, Gainesville, FL 32611 - USA

^[14] Bangor Univ, Sch Nat Sci, Bangor LL57 2W, Gwynedd - Wales

^[15] Univ Fed Uberlandia, Inst Agr Sci, BR-38500000 Monte Carmelo, MG - Brazil

^[16] North Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 - USA

^[17] Univ Fed Parana, Dept Forest Engn, BR-80210170 Curitiba, PR - Brazil

^[18] Univ Fed Sao Joao del Rei, Dept Forest Engn, BR-35701970 Sete Lagoas, MG - Brazil

• Document type: Journal article
• Source: REMOTE SENSING; v. 12, n. 21 NOV 2020.
• Web of Science Citations: 4

Abstract

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus-and similar forest plantations for carbon dynamics and forest product planning. (AU)

• FAPESP's process: 18/21338-3 - Monitoring forest landscape restoration from unmanned aerial vehicles using Lidar and hyperspectral remote sensing
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 19/14697-0 - Monitoring the demography and diversity of forests undergoing restoration using a drone-lidar-hyperspectral system
• Grantee: Danilo Roberti Alves de Almeida
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor