Jose Augusto Spiazzi Favarin

Verified @gmail.com

Postgraduate Program of Forest Engeneering/Federal University of Parana

Jose Augusto Spiazzi Favarin


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https://researchid.co/jaspiazzi

RESEARCH INTERESTS

Remote Sensing; GIS; Forest Management; Biomass and Carbon
12

Scopus Publications

91

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Aboveground biomass and carbon stocks in subtropical forests
    Hiago Adamosky Machado, Adriane Avelhaneda Mallmann, Kauana Engel, José Augusto Spiazzi Favarin, Jordan Luis Campos Modesto, et al.
    Ecological Indicators, 2025
  • Uniformity measures for young Eucalyptus sp. plantations using attributes extracted from UAV flights
    Adriane Avelhaneda Mallmann, Ana Paula Dalla Corte, Alexandre Behling, Rubén Manso, Kauana Engel, et al.
    New Zealand Journal of Forestry Science, 2025
    Background: Studies show that forest uniformity has a direct correlation with productivity, and uniformity measures can serve as indicators of the silvicultural quality of plantations. In this context, this work aimed to determine uniformity and survival in young Eucalyptus sp. plantations using attributes obtained from passive sensors boarded on Unmanned Aerial Vehicles (UAV). Methods: Tree height was underestimated by the UAV compared to those measured in the Quality Forest Inventory (QFI). Thus, a correction factor applied to size classes was proposed to estimate these heights. The plantations’ uniformity was obtained through the uniformity indices (UI). The UIs were spatialised and integrated, resulting in two uniform surfaces, with and without planting failures. Results: The UAV survival estimates did not show significant differences compared to the inventory estimates at the 1% or the 5% significance levels. The classification of uniformity surfaces showed that the Eucalyptus saligna Sm plantation was the least uniform compared to the E. grandis W. Hill × E. urophylla S. T. Blake plantations. Conclusions: Measures of survival and uniformity by the UAV can be jointly employed to generate uniformity surfaces and to determine the areas that need more attention from silvicultural management.
  • Bioeconomy in the Amazon: Lessons and gaps from thirty years of non-timber forest products research
    Emmanoella Costa Guaraná Araujo, Thiago Cardoso Silva, Ernandes Macedo da Cunha Neto, José Augusto Spiazzi Favarin, Joana Keila da Silva Gomes, et al.
    Journal of Environmental Management, 2024
  • Identification and characterization of gaps and roads in the Amazon rainforest with LiDAR data
    JA Spiazzi Favarin, M Sabadi Schuh, J Marchesan, E Alba, R Soares Pereira
    Iforest, 2024
  • Spectroradiometry in Distinguishing Forest Species using Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network
    Igor da Silva Narvaes, Mateus Sabadi Schuh, Pábulo Diogo Souza, Matheus Morais Ziembowicz, José Augusto Spiazzi Favarin, et al.
    Revista Brasileira De Geografia Fisica, 2024
    A distinção de espécies florestais na arborização urbana é fundamental para mitigação dos efeitos locais do aquecimento global. Neste sentido, foram utilizados os algoritmos de aprendizado de máquina RF, SVM e de aprendizagem profunda ANN. Os elevados valores de acurácia encontrados (F-1 score = 0,989; 0,9434; 0,9346, Acurácia Global = 0,989; 0,9444; 0,9333 e de índice kappa = 0,988; 0,9383; 0,9259) para o algoritmo ANN, SVM e RF, respectivamente. Os erros de classificação para a predição de algumas espécies para os classificadores analisados se dão em geral pela semelhança nos valores de reflectância nas regiões do red edge (700 a 720 nm) relacionados ao conteúdo similar de clorofila e nos comprimentos de onda específicos na região do infravermelho de ondas curtas (1400 e 1420 nm) responsáveis pelas diferenças no conteúdo de água e concentração química na planta e de lignina, respectivamente. Dado a complexidade dos classificadores, em especial o algoritmo de aprendizagem profunda ANN, e também aos de aprendizagem de máquina SVM e RF, recomenda-se a alteração de seus hiperparâmetros para se evitar o sobreajuste dos resultados, ou seja, mesmo que o algoritmo esteja adaptado a uma determinada região se torne ineficaz para prever novos resultados.
  • Aboveground biomass stock and change estimation in Amazon rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms
    Juliana Marchesan, Elisiane Alba, Mateus Sabadi Schuh, José Augusto Spiazzi Favarin, Roberta Aparecida Fantinel, et al.
    Journal of Applied Remote Sensing, 2023
    Developing an efficient method to accurately estimate aboveground biomass in tropical forests is critical to monitoring the carbon stock and implementing policies to reduce emissions caused by deforestation. Thus, the objective of the present study was to estimate aboveground biomass in areas of the Amazonian Forest with selective logging, using the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) machine learning algorithms, using light detection and ranging (LiDAR) data and these combined with OLI/Landsat 8 variables, as well as mapping the biomass for the years 2014 and 2017, allowing one to analyze its dynamics between the years of analysis. The RF and SVM algorithms obtained the lowest error values in all datasets. The association of the variables increased the RF performance. Analyzing the dynamics of biomass, it was observed that the oldest exploration units (2006, 2007, and 2008) have lower biomass stocks. The highest biomass losses in 2017 came from units operated between 2012 and 2013 (the most recent record). Thus, with the method used in this study, it was possible to infer that the machine learning algorithms were efficient in estimating the biomass, emphasizing the RF and the SVM.
  • Machine learning and generalized linear model techniques to predict aboveground biomass in Amazon rainforest using LiDAR data
    Mateus Schuh, José Augusto Spiazzi Favarin, Juliana Marchesan, Elisiane Alba, Elias Fernando Berra, et al.
    Journal of Applied Remote Sensing, 2020
    LiDAR remote sensing data combined with machine learning (ML) techniques have presented great potential for large-scale modeling of tropical forest attributes. However, the large amount of information that can be derived from an aerial LiDAR survey, summed with the intrinsic heterogeneity of tropical environments (e.g., the Amazon), makes it a challenge to accurately estimate forest biophysical variables. The aim of our work is to investigate the potential and accuracy of different ML techniques and a generalized linear model (GLM) to learn the relationships between LiDAR-derived metrics and forest inventory data for aboveground biomass (AGB) prediction in Amazon forest sites under selective logging regimes. The predictive performance of three ML techniques, namely random forest (RF), support vector machine (SVM), and artificial neural network (ANN), was compared against result from the GLM technique, across 85 sample plots. Interestingly, the GLM retrieved the most accurate estimations of forest AGB (rho Spearman’s coefficient = 0.87), compared with the ML techniques (RF = 0.77, SVM = 0.67, and ANN = 0.50). A number of possible factors affecting such results are listed and discussed in the text, including sample size and number of predictor variables. Continued research is necessary to improve the confidence of AGB estimation, especially over complex forest structures.
  • Aboveground Biomass Estimation In A Tropical Forest With Selective Logging Using Random Forest And Lidar Data
    Juliana Marchesan, Elisiane Alba, Mateus Sabadi Schuh, José Augusto Spiazzi Favarin, Rudiney Soares Pereira
    Floresta, 2020
    The tropical forest is characterized by expressive biomass and stores high amounts of carbon, which is an important variable for climate monitoring. Thus, studies aiming to analyze suitable methods to predict biomass are crucial, especially in the tropics, where dense vegetation makes modeling difficult. Thus, the objective of the present study was to estimate aboveground biomass (AGB) in a tropical forest area with selective logging in the Amazon forest using the Random Forest (RF) machine learning algorithm and LiDAR data. For this, 85 sample units were used at Fazenda Cauaxi, in the municipality of Paragominas, Pará State. LiDAR data were collected in 2014 and made available by the Sustainable Landscapes Project. The software R was used for data analysis. Among the LiDAR metrics, the average height was used as it had the greatest significance to compose the model. The model presented a pseudo R² of 0.69 (value obtained by the RF), Spearman's Correlation Coefficient of 0.80, RMSE of 47.05 Mg.ha-1 (19.84%), and Bias of 2.06 Mg.ha-1 (0.87%). With the results, it was possible to infer that the average height metric was enough to estimate AGB in a tropical forest with selective logging, in addition, the RF algorithm the biomass to be estimated, which can be used to assist in monitoring and action management in areas of selective logging and serve as a basis for climate change mitigation policies.
  • Functional attributes as ecological predictors during secondary succession in a seasonal deciduous forest in southern brazil
    Francieli de Fátima Missio, Solon Jonas Longhi, Matheus Degrandi Gazzola, Marina Scheuer, Rodrigo Pinto da Silva, et al.
    Revista Arvore, 2020
    Through the variation of the functional attributes, it is possible to verify the functioning of an ecosystem, both by the species organization as by their responses to the environmental variations. In this sense, the objective of this study was to analyze the ecological strategies of the main tree species by analyzing their functional attributes. PCoA was used to verify which species are acquisitive and conservative; clustering was used to verify the functional attributes by groups; the Pearson’s correlation between the functional attributes and a CWM-RDA analysis was used to verify the environmental variables. Most species were classified as light-demanding climax and having zoochoric dispersion. The species presented phenotypic plasticity as an important ecological strategy in the composition of their functional attributes, especially when related to leaf area and specific leaf area. Most of the species belonged to the acquisitive group. The acquisitive and conservative groups indicate the resilience potential of the tree community and the change processes in ecological succession. There was a strong negative correlation between the leaf attributes, and a positive one with diameter and height, both correlations related to plant growth and development. Of the environmental variables only pH, K, and average elevation, were related to the attributes, indicating that environmental conditions are important for the establishment of the sampled species. Also, it was found that the species composition is linked to several conditions of ecological strategies associated with changes in the environment.
  • Influence of forest coverage in the surface albedo
    Elisiane Alba, Juliana Marchesan, Mateus Sabadi Schuh, José Augusto Spiazzi Favarin, Emanuel Araújo Silva, et al.
    Floresta, 2020
    The surface albedo controls the energy balance between the surface and the atmosphere, being a primordial variable to identify climatic variations. The objective of this study was to evaluate the changes of the surface albedo in different Land Use and Land Cover in the Atlantic Forest biome from images TM/Landsat 5 and OLI/Landsat 8, verifying its variation in 30 years. The images used were path-row 221-080, which covered the Floresta Nacional de São Francisco de Paula on the dates of 1987 and 2017. The albedo was obtained by the method of the Surface Energy Balance Algorithm for Land, while the mapping of Land Use and Land Cover was performed by the Bhattacharyya algorithm, identifying four thematic classes. Finally, the albedo was crossed with the thematic classes, evidencing their variation in function of the changes in the land cover. The surface albedo ranged from 6 to 22%, but the year 1987 concentrated albedo values higher than in 2017. The native forest presented superior albedo to the Forest Plantations in both dates due to the structure of the canopy of this class. The spatial analysis of the albedo exposes the relation of this climatic variable to the cover of the terrestrial surface. Thus changes in the vegetation cover cause alterations in the albedo, influencing changes in the radiation and atmospheric fluxes.
  • Use of high spatial resolution images for the monitoring of forest cover
    D. H. HONNEF, E. ALBA, J. MARCHESAN, J. A. S. FAVARIN, M. SCHUH, et al.
    Anuario do Instituto De Geociencias, 2019
  • Spectral behavior of paricá (schizolobium parahyba var. amazonicum (huber ex ducke) barneby) in plantations of different ages
    F. J. BATISTA, L. M. B. FRANCEZ, E. ALBA, M. S. SCHUH, J. A. S. FAVARIN, et al.
    Anuario do Instituto De Geociencias, 2018

RECENT SCHOLAR PUBLICATIONS

  • Uniformity measures for young Eucalyptus sp. plantations using attributes extracted from UAV flights
    AA Mallmann, AP Dalla Corte, A Behling, R Manso, K Engel, C Nardini, ...
    New Zealand Journal of Forestry Science 55 , 2025
    2025
  • Aboveground biomass and carbon stocks in subtropical forests
    HA Machado, AA Mallmann, K Engel, JAS Favarin, JLC Modesto, ...
    Ecological Indicators 172, 113294 , 2025
    2025
    Citations: 4
  • Bioeconomy in the Amazon: Lessons and gaps from thirty years of non-timber forest products research
    ECG Araujo, TC Silva, EM da Cunha Neto, JAS Favarin, ...
    Journal of Environmental Management 370, 122420 , 2024
    2024
    Citations: 29
  • Espectrorradiometria na distinção de espécies florestais utilizando Random Forest (RF), Support Vector Machine (SVM) e Artificial Neural Network (ANN)
    IS Narvaes, MS Schuh, PD Souza, MM Ziembowicz, JAS Favarin, JO Silva, ...
    Revista Brasileira De Geografia Física 17 (4), 2582–2605 , 2024
    2024
  • Identification and characterization of gaps and roads in the Amazon rainforest with LiDAR data
    JA Spiazzi Favarin, M Sabadi Schuh, J Marchesan, E Alba, ...
    iForest-Biogeosciences and Forestry 17 (4), 229 , 2024
    2024
    Citations: 1
  • Aboveground biomass stock and change estimation in Amazon Rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms
    J Marchesan, E Alba, MS Schuh, JAS Favarin, RA Fantinel, L Marchesan, ...
    Journal of Applied Remote Sensing 17 (2), 024509-1 , 2023
    2023
    Citations: 8
  • Inteligência Artificial aplicada à estimativa de Biomassa Acima do Solo na Floresta Amazônica
    J MARCHESAN, E ALBA, MS SCHUH, JA SPIAZZI FAVARIN, ...
    XX Simpósio Brasileiro de Sensoriamento Remoto, 105-108 , 2023
    2023
  • Sensores LiDAR e hiperespectral para a extração de parâmetros qualitativos e quantitativos na floresta ombrófila mista
    JAS Favarin, AP Dalla Corte, MP Ferreira, EN Broadbent
    2023
  • BIOMASS AND CARBON DIOXIDE CAPTURE: RESEARCH FOR Araucaria angustifolia (Bertol.) Kuntz
    JAS Favarin, EG Araújo, MP Ferreira, EN Broadbent, AP Dalla Corte
    BIOFIX Scientific Journal 8 (2), 43-52 , 2023
    2023
  • ATRIBUTOS FUNCIONAIS COMO PREDITORES ECOLÓGICOS DURANTE A SUCESSÃO SECUNDÁRIA EM FLORESTA ESTACIONAL DECIDUAL, SUL DO BRASIL
    F de Fátima Missio, SJ Longhi, MD Gazzola, M Scheuer, RP da Silva, ...
    Revista Árvore, //doi. org/10.1590/1806-908820200000023 , 2020
    2020
  • FUNCTIONAL ATTRIBUTES AS ECOLOGICAL PREDICTORS DURING SECONDARY SUCCESSION IN A SEASONAL DECIDUOUS FOREST IN SOUTHERN BRAZIL
    FF Missio, SJ Longhi, MD Gazzola, M Scheuer, RP Silva, JAS Favarin
    Revista Árvore 44, e4423 , 2020
    2020
    Citations: 2
  • Aboveground biomass estimation in a tropical forest with selective logging using random forest and Lidar data
    J Marchesan, E Alba, MS Schuh, JAS Favarin, RS Pereira
    Floresta 50 (4), 1873-1882 , 2020
    2020
    Citations: 12
  • Machine learning and generalized linear model techniques to predict aboveground biomass in Amazon rainforest using LiDAR data
    M Schuh, JAS Favarin, J Marchesan, E Alba, E Fernando Berra, ...
    Journal of Applied Remote Sensing 14 (3), 034518-034518 , 2020
    2020
    Citations: 18
  • INFLUENCE OF FOREST COVERAGE IN THE SURFACE ALBEDO
    E Alba, J Marchesan, MS Schuh, JAS Favarin, EA Silva, RS Pereira
    Floresta 50 (1), 1011-1020 , 2019
    2019
  • Dados de sensor LiDAR na identificação e caracterização de clareiras e estradas na floresta Amazônica
    JAS Favarin
    Universidade Federal de Santa Maria , 2019
    2019
  • Uso de Imagens de Alta Resolução Espacial para o Monitoramento da Cobertura Florestal na Região Central do Rio Grande do Sul
    DH Honnef, E Alba, J Marchesan, JAS Favarin, M Schuh, T Badin, ...
    Anuário do Instituto de Geociências 42 (4), 148-154 , 2019
    2019
    Citations: 1
  • Albedo Trend Analyses in Atlantic Forest Biome Areas
    E Alba, RS Pereira, J Marchesan, EA Silva, F de J. Batista, VS Kazama, ...
    Journal of Agricultural Science 10 (10), 298-307 , 2018
    2018
    Citations: 5
  • Comportamento Espectral de Paricá (Schizolobium parahyba var. amazonicum (Huber ex Ducke) Barneby) em Plantios com Diferentes Idades
    F de Jesus Batista, LM de Barros Francez, E Alba, MS Schuh, ...
    Anuário do instituto de geociências 41 (3), 82-95 , 2018
    2018
    Citations: 3
  • The R language in spatial analysis of forest fragments
    J MARCHESAN, E ALBA, LD PEDRALI, MS SCHUH, ...
    Australian Journal of Basic and Applied Sciences 11 (15), 1-7 , 2017
    2017
  • Evaluation of Time After Leaf Collection As An Influential Factor In Spectroradiometric Measurements
    MS SCHUH, JA SPIAZZI FAVARIN, L DESSBESELL, E ALBA, ...
    Australian Journal of Basic and Applied Sciences 11 (14), 17-24 , 2017
    2017
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • Bioeconomy in the Amazon: Lessons and gaps from thirty years of non-timber forest products research
    ECG Araujo, TC Silva, EM da Cunha Neto, JAS Favarin, ...
    Journal of Environmental Management 370, 122420 , 2024
    2024
    Citations: 29
  • Machine learning and generalized linear model techniques to predict aboveground biomass in Amazon rainforest using LiDAR data
    M Schuh, JAS Favarin, J Marchesan, E Alba, E Fernando Berra, ...
    Journal of Applied Remote Sensing 14 (3), 034518-034518 , 2020
    2020
    Citations: 18
  • Aboveground biomass estimation in a tropical forest with selective logging using random forest and Lidar data
    J Marchesan, E Alba, MS Schuh, JAS Favarin, RS Pereira
    Floresta 50 (4), 1873-1882 , 2020
    2020
    Citations: 12
  • Aboveground biomass stock and change estimation in Amazon Rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms
    J Marchesan, E Alba, MS Schuh, JAS Favarin, RA Fantinel, L Marchesan, ...
    Journal of Applied Remote Sensing 17 (2), 024509-1 , 2023
    2023
    Citations: 8
  • Obtenção de fotografias aéreas de um povoamento de Pinus taeda L. com o VANT Microdrone MD4-1000
    JAS Favarin, RS Pereira, AJ Pegoraro, DB Lippert
    XVI Simpósio brasileiro de sensoriamento remoto-SBSR 16, 9340-9346 , 2013
    2013
    Citations: 6
  • Albedo Trend Analyses in Atlantic Forest Biome Areas
    E Alba, RS Pereira, J Marchesan, EA Silva, F de J. Batista, VS Kazama, ...
    Journal of Agricultural Science 10 (10), 298-307 , 2018
    2018
    Citations: 5
  • Aboveground biomass and carbon stocks in subtropical forests
    HA Machado, AA Mallmann, K Engel, JAS Favarin, JLC Modesto, ...
    Ecological Indicators 172, 113294 , 2025
    2025
    Citations: 4
  • Comportamento Espectral de Paricá (Schizolobium parahyba var. amazonicum (Huber ex Ducke) Barneby) em Plantios com Diferentes Idades
    F de Jesus Batista, LM de Barros Francez, E Alba, MS Schuh, ...
    Anuário do instituto de geociências 41 (3), 82-95 , 2018
    2018
    Citations: 3
  • FUNCTIONAL ATTRIBUTES AS ECOLOGICAL PREDICTORS DURING SECONDARY SUCCESSION IN A SEASONAL DECIDUOUS FOREST IN SOUTHERN BRAZIL
    FF Missio, SJ Longhi, MD Gazzola, M Scheuer, RP Silva, JAS Favarin
    Revista Árvore 44, e4423 , 2020
    2020
    Citations: 2
  • Identification and characterization of gaps and roads in the Amazon rainforest with LiDAR data
    JA Spiazzi Favarin, M Sabadi Schuh, J Marchesan, E Alba, ...
    iForest-Biogeosciences and Forestry 17 (4), 229 , 2024
    2024
    Citations: 1
  • Uso de Imagens de Alta Resolução Espacial para o Monitoramento da Cobertura Florestal na Região Central do Rio Grande do Sul
    DH Honnef, E Alba, J Marchesan, JAS Favarin, M Schuh, T Badin, ...
    Anuário do Instituto de Geociências 42 (4), 148-154 , 2019
    2019
    Citations: 1
  • Evaluation of Time After Leaf Collection As An Influential Factor In Spectroradiometric Measurements
    MS SCHUH, JA SPIAZZI FAVARIN, L DESSBESELL, E ALBA, ...
    Australian Journal of Basic and Applied Sciences 11 (14), 17-24 , 2017
    2017
    Citations: 1
  • Análise temporal do vigor vegetativo por meio de espectrorradiometria
    MS SCHUH, JA SPIAZZI FAVARIN, L DESSBESELL, EA SILVA, ...
    REVISTA BRASILEIRA DE GEOGRAFIA FÍSICA 9, 1888-1894 , 2016
    2016
    Citations: 1
  • Uniformity measures for young Eucalyptus sp. plantations using attributes extracted from UAV flights
    AA Mallmann, AP Dalla Corte, A Behling, R Manso, K Engel, C Nardini, ...
    New Zealand Journal of Forestry Science 55 , 2025
    2025
  • Espectrorradiometria na distinção de espécies florestais utilizando Random Forest (RF), Support Vector Machine (SVM) e Artificial Neural Network (ANN)
    IS Narvaes, MS Schuh, PD Souza, MM Ziembowicz, JAS Favarin, JO Silva, ...
    Revista Brasileira De Geografia Física 17 (4), 2582–2605 , 2024
    2024
  • Inteligência Artificial aplicada à estimativa de Biomassa Acima do Solo na Floresta Amazônica
    J MARCHESAN, E ALBA, MS SCHUH, JA SPIAZZI FAVARIN, ...
    XX Simpósio Brasileiro de Sensoriamento Remoto, 105-108 , 2023
    2023
  • Sensores LiDAR e hiperespectral para a extração de parâmetros qualitativos e quantitativos na floresta ombrófila mista
    JAS Favarin, AP Dalla Corte, MP Ferreira, EN Broadbent
    2023
  • BIOMASS AND CARBON DIOXIDE CAPTURE: RESEARCH FOR Araucaria angustifolia (Bertol.) Kuntz
    JAS Favarin, EG Araújo, MP Ferreira, EN Broadbent, AP Dalla Corte
    BIOFIX Scientific Journal 8 (2), 43-52 , 2023
    2023
  • ATRIBUTOS FUNCIONAIS COMO PREDITORES ECOLÓGICOS DURANTE A SUCESSÃO SECUNDÁRIA EM FLORESTA ESTACIONAL DECIDUAL, SUL DO BRASIL
    F de Fátima Missio, SJ Longhi, MD Gazzola, M Scheuer, RP da Silva, ...
    Revista Árvore, //doi. org/10.1590/1806-908820200000023 , 2020
    2020
  • INFLUENCE OF FOREST COVERAGE IN THE SURFACE ALBEDO
    E Alba, J Marchesan, MS Schuh, JAS Favarin, EA Silva, RS Pereira
    Floresta 50 (1), 1011-1020 , 2019
    2019