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3. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

Author

Leite, Rodrigo Vieira, Silva, Carlos Alberto, Broadbent, Eben North, Amaral, Cibele Hummel do, Liesenberg, Veraldo, Almeida, Danilo Roberti Alves de, Mohan, Midhun, Godinho, Sérgio, Cardil, Adrian, Hamamura, Caio, Faria, Bruno Lopes de, Brancalion, Pedro H.S., Hirsch, André, Marcatti, Gustavo Eduardo, Dalla Corte, Ana Paula, Zambrano, Angelica Maria Almeyda, Costa, Máira Beatriz Teixeira da, Matricardi, Eraldo Aparecido Trondoli, Silva, Anne Laura da, Goya, Lucas Ruggeri Ré Y., Valbuena, Ruben, Mendonça, Bruno Araujo Furtado de, Silva Junior, Celso H.L., Aragão, Luiz E.O.C., García, Mariano, Liang, Jingjing, Merrick, Trina, Hudak, Andrew T., Xiao, Jingfeng, Hancock, Steven, Duncason, Laura, Ferreira, Matheus Pinheiro, Valle, Denis, Saatchi, Sassan, and Klauberg, Carine

Published
2022
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4. Multiple approaches for quantifying fuels, combustion dynamics, and regional fire emissions in the Amazon and Cerrado

Author

Forkel, Matthias, primary, Wessollek, Christine, additional, Andela, Niels, additional, de Laat, Jos, additional, Huijnen, Vincent, additional, Kinalczyk, Daniel, additional, Marrs, Christopher, additional, van Wees, Dave, additional, Bastos, Ana, additional, Ciais, Philippe, additional, Fawcett, Dominic, additional, Kaiser, Johannes W., additional, Kutchartt, Erico, additional, Klauberg, Carine, additional, Leite, Rodrigo Vieira, additional, Li, Wei, additional, Silva, Carlos, additional, Sitch, Stephen, additional, Goncalves De Souza, Jefferson, additional, and Plummer, Stephen, additional

Published
2024
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5. Individual tree detection using UAV-lidar and UAV-SfM data: A tutorial for beginners

Author

Mohan Midhun, Leite Rodrigo Vieira, Broadbent Eben North, Wan Mohd Jaafar Wan Shafrina, Srinivasan Shruthi, Bajaj Shaurya, Dalla Corte Ana Paula, do Amaral Cibele Hummel, Gopan Gopika, Saad Siti Nor Maizah, Muhmad Kamarulzaman Aisyah Marliza, Prata Gabriel Atticciati, Llewelyn Emma, Johnson Daniel J., Doaemo Willie, Bohlman Stephanie, Almeyda Zambrano Angelica Maria, and Cardil Adrián

Subjects
single tree detection, chm, lm, drones, uav tutorials, forestry data analysis, forest remote sensing, Geology, QE1-996.5
Abstract

Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpretation from high-resolution imagery and field-data-based accuracy assessment. As the intended audience are beginners in remote sensing, we have adopted a very simple methodology and chosen study plots that have relatively open canopies to demonstrate our proposed approach; the respective R codes and sample plot data are available as supplementary materials.

Published
2021
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7. Analyzing Canopy Height Patterns and Environmental Landscape Drivers in Tropical Forests Using NASA’s GEDI Spaceborne LiDAR

Author

Adrah, Esmaeel, primary, Wan Mohd Jaafar, Wan Shafrina, additional, Omar, Hamdan, additional, Bajaj, Shaurya, additional, Leite, Rodrigo Vieira, additional, Mazlan, Siti Munirah, additional, Silva, Carlos Alberto, additional, Chel Gee Ooi, Maggie, additional, Mohd Said, Mohd Nizam, additional, Abdul Maulud, Khairul Nizam, additional, Cardil, Adrián, additional, and Mohan, Midhun, additional

Published
2022
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8. Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System

Author

Dalla Corte, Ana Paula, primary, de Vasconcellos, Bruna Nascimento, additional, Rex, Franciel Eduardo, additional, Sanquetta, Carlos Roberto, additional, Mohan, Midhun, additional, Silva, Carlos Alberto, additional, Klauberg, Carine, additional, de Almeida, Danilo Roberti Alves, additional, Zambrano, Angelica Maria Almeyda, additional, Trautenmüller, Jonathan William, additional, Leite, Rodrigo Vieira, additional, do Amaral, Cibele Hummel, additional, Veras, Hudson Franklin Pessoa, additional, da Silva Rocha, Karla, additional, de Moraes, Anibal, additional, Karasinski, Mauro Alessandro, additional, Sanquetta, Matheus Niroh Inoue, additional, and Broadbent, Eben North, additional

Published
2022
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9. Improving Yield Projections from Early Ages in Eucalypt Plantations with the Clutter Model and Artificial Neural Networks

Author

Casas, Gianmarco Goycochea, primary, Fardin, Leonardo Pereira, additional, Silva, Simone, additional, de Oliveira Neto, Ricardo Rodrigues, additional, Breda Binoti, Daniel Henrique, additional, Leite, Rodrigo Vieira, additional, Ramos Domiciano, Carlos Alberto, additional, de Sousa Lopes, Lucas Sérgio, additional, da Cruz, Jovane Pereira, additional, dos Reis, Thaynara Lopes, additional, and Leite, Hélio Garcia, additional

Published
2022
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10. Leatherback turtle conservation and monitoring efforts at the crossroads: A remote sensing perspective

Author

Mohan, Midhun, primary, Doaemo, Willie, additional, Tapilatu, Ricardo F., additional, Mohd Jaafar, Wan Shafrina Wan, additional, Adrah, Esmaeel, additional, Gopan, Gopika, additional, Broadbent, Eben North, additional, Shapiro, Aurelie, additional, Sasmito, Sigit D, additional, Srinivasan, Shruthi, additional, Ranagalage, Manjula, additional, Varela, Miguel R., additional, Llewelyn, Emma, additional, Asok, Frank, additional, Huo, Lian-Zhi, additional, Silva, Carlos Alberto, additional, Paula Dalla Corte, Ana, additional, Cardil, Adrian, additional, and Leite, Rodrigo Vieira, additional

Published
2021
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12. UAV-Supported Forest Regeneration: Current Trends, Challenges and Implications

Author

Mohan, Midhun, primary, Richardson, Gabriella, additional, Gopan, Gopika, additional, Aghai, Matthew Mehdi, additional, Bajaj, Shaurya, additional, Galgamuwa, G. A. Pabodha, additional, Vastaranta, Mikko, additional, Arachchige, Pavithra S. Pitumpe, additional, Amorós, Lot, additional, Corte, Ana Paula Dalla, additional, de-Miguel, Sergio, additional, Leite, Rodrigo Vieira, additional, Kganyago, Mahlatse, additional, Broadbent, Eben North, additional, Doaemo, Willie, additional, Shorab, Mohammed Abdullah Bin, additional, and Cardil, Adrian, additional

Published
2021
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13. Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Author

da Costa, Máira Beatriz Teixeira, primary, Silva, Carlos Alberto, additional, Broadbent, Eben North, additional, Leite, Rodrigo Vieira, additional, Mohan, Midhun, additional, Liesenberg, Veraldo, additional, Stoddart, Jaz, additional, do Amaral, Cibele Hummel, additional, de Almeida, Danilo Roberti Alves, additional, da Silva, Anne Laura, additional, Ré Y. Goya, Lucas Ruggeri, additional, Cordeiro, Victor Almeida, additional, Rex, Franciel, additional, Hirsch, Andre, additional, Marcatti, Gustavo Eduardo, additional, Cardil, Adrian, additional, de Mendonça, Bruno Araujo Furtado, additional, Hamamura, Caio, additional, Corte, Ana Paula Dalla, additional, Matricardi, Eraldo Aparecido Trondoli, additional, Hudak, Andrew T., additional, Zambrano, Angelica Maria Almeyda, additional, Valbuena, Ruben, additional, de Faria, Bruno Lopes, additional, Silva Junior, Celso H.L., additional, Aragao, Luiz, additional, Ferreira, Manuel Eduardo, additional, Liang, Jingjing, additional, e Carvalho, Samuel de Pádua Chaves, additional, and Klauberg, Carine, additional

Published
2021
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14. UNDERSTANDING THE DYNAMICS OF THE BRAZILIAN MARKET FOR NATIVE FOREST WOOD: A CASE STUDY FOR MINAS GERAIS STATE, BRAZIL

Author

Martins, Thales Guilherme Vaz, primary, Reis, Geraldo Gonçalves dos, additional, Reis, Maria das Graças Ferreira, additional, Leite, Rodrigo Vieira, additional, Telles, Lucas Arthur de Almeida, additional, Mendes, Gleidson Guilherme Caldas, additional, Lage, Mayara Ribeiro, additional, Pinto, Dayane Lopes, additional, Carvalho, Thamires de Souza, additional, Cardoso, Hiohanes Moura, additional, Lorenzon, Alexandre Simões, additional, and Martins, Douglas Vinícius Vaz, additional

Published
2021
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15. Estimating Stem Volume in Eucalyptus Plantations Using Airborne LiDAR: A Comparison of Area- and Individual Tree-Based Approaches

Author

Leite, Rodrigo Vieira, primary, Amaral, Cibele Hummel do, additional, Pires, Raul de Paula, additional, Silva, Carlos Alberto, additional, Soares, Carlos Pedro Boechat, additional, Macedo, Renata Paulo, additional, Silva, Antonilmar Araújo Lopes da, additional, Broadbent, Eben North, additional, Mohan, Midhun, additional, and Leite, Hélio Garcia, additional

Published
2020
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16. Estimativa de volume em plantios de eucalipto utilizando LiDAR aerotransportado: uma comparação entre as abordagens a nível de área e árvore

Author

Leite, Rodrigo Vieira, Leite, Hélio Garcia, and Amaral, Cibele Hummel do

Subjects
Eucalipto, Aprendizado do computador, Sensoriamento remoto, Fotointerpretação Florestal
Abstract

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) A estimativa de parâmetros florestais usando nuvem de pontos de LiDAR é um aprimoramento dos métodos convencionais, pois fornece informações precisas e espacializadas. Neste estudo comparamos o desempenho das abordagens a nível de área (ABA) e árvore (ITD) para esimar o volume em povoamentos de eucalipto. Foram testados cinco métodos na abordagem ABA usando Aritificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), regressão linear e Gompertz, selecionando variáveis de entrada nos modelos usando Recursive Feature Elimination e correlação de Spearman para métodos não-paramétricos e paramétricos, respectivamente. Para o ITD, foram testados três métodos para detectar árvores (dois filtros máximos locais e o método da bacia hidrográfica), adicionando um novo procedimento gerando um buffer de exclusão de árvores para melhorar a precisão da detecção de arvores. Também foram testados quatro métodos para estimar o dap usando ANN e as equações de Gompertz, logística e exponencial. O volume de árvores nessa abordagem foi calculado utilizando o modelo de Schumacher e Hall. Além disso, foram avaliados os efeitos do ângulo de escaneamento, inclinação do terreno e idade do talhão na contagem de árvores, medição de altura e estimativa de volume. No geral, a abordagem ABA teve um melhor desempenho na estimativa de volume para o talhão (r = 0,95 e RMSE = 14,41%) não diferindo estatisticamente (p > 0,05) dos volumes medidos em campo para o teste F de Graybill e teste-t para o erro médio. As estimativas ITD, embora com desempenho semelhante (r = 0,97 e RMSE = 16,4%), tenderam a subestimar o volume em povoamentos mais jovens e em terrenos com inclinação até 25%, além disso diferiram estatisticamente (p < 0,05) dos volumes medidos em campo no teste F de Graybill e teste t para o erro médio. Os mapas de volume também foram diferentes para as abordagens, em que ITD teve valores distribuídos em mais classes que ABA. É discutido neste trabalho a importância das métricas LiDAR como variáveis de entrada nos métodos de estimativa e os possíveis problemas relacionados ao desempenho do ITD. Palavras-chave: Eucalyptus. Detecção de árvores. Aprendizado de máquina. Sensoriamento remoto Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation (r = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision (r = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (< 25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance. Keywords: Eucalyptus. Tree detection. Machine learning. Remote sensing

Published
2019

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

Author

Leite, Rodrigo Vieira, Silva, Carlos Alberto, Mohan, Midhun, Cardil, Adrián, Almeida, Danilo Roberti Alves de, Carvalho, Samuel de Pádua Chaves e, Jaafar, Wan Shafrina Wan Mohd, Guerra-Hernández, Juan, Weiskittel, Aaron, Hudak, Andrew T., Broadbent, Eben N., Prata, Gabriel, Valbuena, Ruben, Leite, Hélio Garcia, Taquetti, Mariana Futia, Soares, Alvaro Augusto Vieira, Scolforo, Henrique Ferraço, Amaral, Cibele Hummel do, Dalla Corte, Ana Paula, and Klauberg, Carine

Subjects
*EUCALYPTUS, *TREE farms, *LIDAR, *FOREST dynamics, *UNIFORMITY, *ALLOMETRIC equations
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. [ABSTRACT FROM AUTHOR]

Published
2020
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