Mateus Sabadi Schuh

@ufsm.br

Postgraduate Program in Forest Engineering/ Federal University of Santa Maria
Postgraduate Program in Forest Engineering

Mateus Sabadi Schuh


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https://researchid.co/mateuschuh

RESEARCH INTERESTS

Geotechnologies applied to forest sciences.
21

Scopus Publications

232

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Use of hyperspectral sensor and random forest for broadleaf and narrowleaf weed differentiation
    New Forests, 2025
  • Efficiency and costs of different sampling methods in an inventory of a Terra Firme Forest, Pará state, Brazil
    Fabio de Jesus Batista, Elce Lima Castro, Fernanda Kelly Ribeiro Gonçalves Leite, Luciana Maria de Barros Francez, Deivison Venicio Souza, et al.
    Ciencia Florestal, 2025
    O inventário florestal na Fazenda Caculé, localizada em Paragominas-PA, teve como objetivo comparar a eficiência e custos de diferentes processos de amostragem (simples ao acaso, conglomerado sem estratificação e conglomerado com pós-estratificação) em uma floresta de terra firme. A fazenda, com uma área total de 8.637 hectares, foi dividida em um grid com coordenadas equidistantes de 700 metros. Neste grid, foram selecionadas aleatoriamente 47 unidades amostrais (UAs). A análise considerou quatro tratamentos diferentes: simples ao acaso por conglomerado (T1); simples ao acaso por subunidade (T2); por conglomerado (T3) e pós-estratificado por conglomerado (T4). Para cada processo de amostragem foram utilizados os estimadores paramétricos tradicionais para obtenção das estimativas de produção por hectare. O T4 exigiu uma análise prévia dos dados a fim de identificar os estratos existentes (análise de agrupamento). Na estimativa da produção (m³) por hectare a média foi de 154,9855 m³, com desvio variando de ±5,33 a 36,54 m³.ha-1. O erro de amostragem teve pouca variação, 4,67% a 6,92%. T2 e T4 apresentaram os menores erros. A suficiência amostral para o erro pré-definido apresentou alta variação (14 a 43 UAs), sendo T4 o melhor tratamento. O custo fixo por Unidade Amostral (UA) foi de $269,22. Em comparação com os demais tratamentos, T4 foi o mais eficiente em termos de tempo, recursos e esforço necessários para atingir a precisão de 10%, o que reduz os custos do inventário florestal.
  • 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.
  • Flooded rice variables from high-resolution multispectral images and machine learning algorithms
    Fernando Coelho Eugenio, Mara Grohs, Mateus Sabadi Schuh, Luan Peroni Venancio, Cristine Schons, et al.
    Remote Sensing Applications Society and Environment, 2023
  • 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.
  • Estimated flooded rice grain yield and nitrogen content in leaves based on RPAS images and machine learning
    Fernando Coelho Eugenio, Mara Grohs, Mateus Schuh, Luan Peroni Venancio, Cristine Schons, et al.
    Field Crops Research, 2023
  • Use of Machine Learning Algorithms in the Classification of Forest Species
    Táscilla Magalhães Loiola, Roberta Aparecida Fantinel, Fernanda Dias Dos Santos, Franciele De Bastos, Mateus Sabadi Schuh, et al.
    Anuario do Instituto De Geociencias, 2023
    Optimization in the process of managing forest resources seeks alternatives that make data collection possible. One of them alternatives is spectroradiometry, which consists of measuring the spectral response, having as product the response of the target in relation to the incident radiation along the electromagnetic spectrum, and that, using machine learning, with pre-selected models, makes it possible to identify. Given the above, the study aimed to use machine learning algorithms to classify species by vegetation indices from reflectance data. The study was developed at the Federal University from Santa Maria, working with the species Ficus benjamina, Inga marginata, Handroanthus chrysotrichus, Psidium cattleianum, Salix humboldtiana, Corymbia citriodora and Myrcianthes pungens, and spectral readings of the leaves were taken using the FieldSpec®3 spectroradiometer connected to RTS-3ZC3 integrating sphere. The reflectance values with wavelength ranged in amplitude from 350 ƞm to 2,500 ƞm and spectral resolution of 1 ƞm. Vegetation indices were calculated using the software R Studio, being: NDVI, SAVI, RVI, GNDVI, NDWI, NDWI2, GEMI, DVI, TVI, RVI, MSAVI, WDVI. The algorithms used to develop machine learning were: Random Forest (RF), k-Nearest Neighbors (K-NN), Naive Bayes (NB) and Support Vector Machine (SVM). RF proves to be the most appropriate for data validation, with 85% global accuracy, followed by SVM, with 71%, K-NN with 64% and NB with 35%. The indices with the best performance to point the species were NDWI and SAVI.
  • ARTIFICIAL INTELLIGENCE AND ORBITAL IMAGES APPLICATION FOR ANALYSIS OF SPATIAL LAND USE AND COVERAGE PATTERNS
    Roberta Aparecida Fantinel, Rudiney Soares Pereira, Ana Caroline Paim Benedetti, Fernando Coelho Eugenio, Juliana Marchesan, et al.
    Floresta, 2022
    The study aimed to analyze the performance of different machine learning (ML) algorithms in predicting land use and land cover patterns from time series spectral data from Thematic Mapper (TM) and Operational Land Imager (OLI) sensors. The QGIS software was used, where the import of TM / Landsat 5 images began in 2004 and 2009 and OLI/Landsat 8 for 2015 and 2019, to obtain information to characterize and differentiate usage patterns and land cover. Subsequently, training and testing of the algorithms, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naive Bayes (NB), were carried out in the proportions of 80%-20%, 70%-30%, 60%-40% in the KNIME software. The performance was analyzed based on global accuracy and the Kappa index. The RF and SVM for the years 2004 and 2009 showed the best performance (global accuracy), while for the years 2015 and 2019, they were the K-NN and the RF. The Kappa index values indicated that the classifications of the algorithms varied from 0.80 – 1.00. The proportion of 60% (training) and 40% (test) was the one that provided the best results for all the dates analyzed. The data from the pixels sampled from the land use and land cover patterns of the TM and OLI sensor images proved to be efficient for the ML process in the KNIME software.
  • Influence of flight hours on spectral behavior of rice (Oryza Sativa L.) and soybean (Glycine Max L.) cultivars in images obtained by remotely piloted aircraft system
    Fernando Coelho Eugenio, Mara Grohs, Caroline Lorenci Mallmann, Cristine Tagliapietra Schons, Mateus Sabadi Schuh, et al.
    Journal of Applied Remote Sensing, 2021
    The research aimed to evaluate the spectral behavior of remotely piloted aircraft system (RPAS) images taken of agricultural crops during the day to answer the following hypotheses: (1) there are no significant differences between spectral reflectance information obtained through RPAS images and data from spectroradiometry; (2) the survey time can be defined as a limiting factor in obtaining accurate reflectance data; and (3) it is possible to characterize homogeneous observation windows throughout the day. With regular intervals of 1 h, 12 surveys were made in rice and soybean cultivars, using a Sequoia® camera, embedded in RPAS, and the FieldSpec®3 spectroradiometer, to validate the image spectra. Normalized Difference Vegetation Index (NDVI), Optimized Soil Adjusted Vegetation Index (OSAVI), and Transformed Chlorophyll Absorption Reflectance Index (TCARI) derived from aerial images were subjected to Kruskal–Wallis and hierarchical cluster tests to define homogeneous observation times. The Sequoia® system made it possible to obtain reflectance data that met the validation criteria, even operating under varying interaction factors during the day, so the flight schedule did not result in loss of information about the vegetation. The hierarchical cluster analysis allowed the detection of temporal patterns of NDVI, OSAVI, and TCARI throughout the day. For rice and soybeans, three more homogeneous observation windows were characterized. In terms of temporal pattern of NDVI, OSAVI, and TCARI, the Kruskal–Wallis analysis was quite sensitive to the inherent variation of the crops throughout the day, so a hypothesis of homogeneous monitoring windows input throughout the day could not be proven.
  • Remotely Piloted Aircraft Systems (RPAS) and machine learning: A review in the context of forest science
    Fernando Coelho Eugenio, Tiago Luis Badin, Pablo Fernandes, Caroline Lorenci Mallmann, Cristine Schons, et al.
    International Journal of Remote Sensing, 2021
  • Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery
    Fernando Coelho Eugenio, Mara Grohs, Luan Peroni Venancio, Mateus Schuh, Eduardo Leonel Bottega, et al.
    Remote Sensing Applications Society and Environment, 2020
  • 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
  • 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
  • Remotely piloted aircraft systems and forests: A global state of the art and future challenges
    Fernando Coelho Eugenio, Cristine Tagliapietra Schons, Caroline Lorenci Mallmann, Mateus Sabadi Schuh, Pablo Fernandes, et al.
    Canadian Journal of Forest Research, 2020
  • 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
  • Analysis land use and land cover using sentinel-2a images and artificial intelligence
    Matheus Frigo Wolfer, Juliana Marchesan, Elisiane Alba, Mateus Schuh, Dionatas Henrique Honnef, et al.
    Anuario do Instituto De Geociencias, 2020
  • 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
  • Use of satellite images in algebraic geoprocessing in the microregion of campanha ocidental, Rio Grande do Sul state, Brazil
    Emanuel Araújo Silva, Rudiney Soares Pereira, Caroline Kist da Silva, Laura Camila de Godoy Goergen, Mateus Sabadi Schuh
    Floresta E Ambiente, 2014
  • Spectroradiometry of comercial wood veneers in the visible and near infrared spectra
    Floresta, 2014

RECENT SCHOLAR PUBLICATIONS

  • Use of hyperspectral sensor and random forest for broadleaf and narrowleaf weed differentiation
    P Fernandes, SD Pereira da Silva, RA Fantinel, NB Junior, ...
    New Forests 56 (5), 46 , 2025
    2025
    Citations: 3
  • Efficiency and costs of different sampling methods in an inventory of a Terra Firme Forest, Pará state, Brazil
    FJ Batista, EL Castro, FKRG Leite, LMB Francez, DV Souza, MS Schuh, ...
    Ciência Florestal 35, e88427 , 2025
    2025
  • Eficiência e custos de diferentes processos de amostragem no inventário de uma Floresta de Terra Firme, estado do Pará, Brasil
    FJ Batista, EL Castro, FKRG Leite, LMB Francez, DV Souza, MS Schuh, ...
    Ciência Florestal 35, e88427 , 2025
    2025
  • Espectrorradiometria na distinção de espécies florestais utilizando Random Forest (RF), Support Vector Machine (SVM) e Artificial Neural Network (ANN)
    I da Silva Narvaes, MS Schuh, PD Souza, MM Ziembowicz, JAS Favarin, ...
    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
  • Flooded rice variables from high-resolution multispectral images and machine learning algorithms
    FC Eugenio, M Grohs, MS Schuh, LP Venancio, C Schons, TL Badin, ...
    Remote Sensing Applications: Society and Environment 31, 100998 , 2023
    2023
    Citations: 7
  • 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, JA Spiazzi Favarin, RA Fantinel, ...
    Journal of Applied Remote Sensing 17 (2), 024509-024509 , 2023
    2023
    Citations: 8
  • Estimated flooded rice grain yield and nitrogen content in leaves based on RPAS images and machine learning
    FC Eugenio, M Grohs, M Schuh, LP Venancio, C Schons, TL Badin, ...
    Field Crops Research 292, 108823 , 2023
    2023
    Citations: 15
  • “The Metamorphosis”: Jhumpa Lahiri and her exophonic writing: Jhumpa Lahiri and her exophonic writing
    A Moraes da Costa
    2023
  • Estimativa da biomassa acima do solo em floresta de terra firme na Amazônia com dados LiDAR aerotransportado e upscaling com imagens orbitais
    MS Schuh, RS Pereira, JA Farias, FC Eugenio, EA Silva, FFM Rovani, ...
    2023
    Citations: 1
  • Use of Machine Learning Algorithms in the Classification of Forest Species
    TM Loiola, RA Fantinel, FD dos Santos, F de Bastos, MS Schuh, ...
    Anuário do Instituto de Geociências 46, 1-8 , 2023
    2023
    Citations: 3
  • Artificial intelligence and orbital images application for analysis of spatial land use and coverage patterns.
    RA Fantinel, RS Pereira, ACP Benedetti, FC Eugenio, J Marchesan, ...
    2022
    Citations: 1
  • Remotely Piloted Aircraft Systems (RPAS) and machine learning: A review in the context of forest science
    F Coelho Eugenio, TL Badin, P Fernandes, CL Mallmann, C Schons, ...
    International Journal of Remote Sensing 42 (21), 8207-8235 , 2021
    2021
    Citations: 30
  • Influence of flight hours on spectral behavior of rice ( Oryza Sativa L.) and soybean ( Glycine Max L.) cultivars in images obtained by remotely piloted aircraft system
    FC Eugenio, M Grohs, CL Mallmann, CT Schons, MS Schuh, P Fernandes, ...
    Journal of Applied Remote Sensing 15 (4), 044505-044505 , 2021
    2021
  • Análise do uso e cobertura da terra utilizando imagens sentinel-2A e inteligência artificial
    MF Wolfer, J Marchesan, E Alba, M Schuh, DH Honnef, HS Oliveira, ...
    Anuário do Instituto de Geociências 43 (4), 395_403-395_403 , 2020
    2020
    Citations: 3
  • Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery
    FC Eugenio, M Grohs, LP Venancio, M Schuh, EL Bottega, R Ruoso, ...
    Remote Sensing Applications: Society and Environment 20, 100397 , 2020
    2020
    Citations: 67
  • Aboveground biomass estimation in a tropical forest with selective logging using random forest and LiDAR data.
    J Marchesan, E Alba, M Sabadi Schuh, JA Spiazzi Favarin, ...
    Floresta 50 (4) , 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
  • Remotely piloted aircraft systems and forests: a global state of the art and future challenges
    FC Eugenio, CT Schons, CL Mallmann, MS Schuh, P Fernandes, ...
    Canadian journal of forest research 50 (8), 705-716 , 2020
    2020
    Citations: 45
  • Influence of forest coverage in the surface albedo.
    E Alba, J Marchesan, M Schuh, JA Spiazzi Favarin, E Araújo Silva, ...
    Floresta 50 (1) , 2020
    2020

MOST CITED SCHOLAR PUBLICATIONS

  • Estimation of soybean yield from machine learning techniques and multispectral RPAS imagery
    FC Eugenio, M Grohs, LP Venancio, M Schuh, EL Bottega, R Ruoso, ...
    Remote Sensing Applications: Society and Environment 20, 100397 , 2020
    2020
    Citations: 67
  • Remotely piloted aircraft systems and forests: a global state of the art and future challenges
    FC Eugenio, CT Schons, CL Mallmann, MS Schuh, P Fernandes, ...
    Canadian journal of forest research 50 (8), 705-716 , 2020
    2020
    Citations: 45
  • Remotely Piloted Aircraft Systems (RPAS) and machine learning: A review in the context of forest science
    F Coelho Eugenio, TL Badin, P Fernandes, CL Mallmann, C Schons, ...
    International Journal of Remote Sensing 42 (21), 8207-8235 , 2021
    2021
    Citations: 30
  • 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
  • Estimated flooded rice grain yield and nitrogen content in leaves based on RPAS images and machine learning
    FC Eugenio, M Grohs, M Schuh, LP Venancio, C Schons, TL Badin, ...
    Field Crops Research 292, 108823 , 2023
    2023
    Citations: 15
  • Aboveground biomass estimation in a tropical forest with selective logging using random forest and LiDAR data.
    J Marchesan, E Alba, M Sabadi Schuh, JA Spiazzi Favarin, ...
    Floresta 50 (4) , 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, JA Spiazzi Favarin, RA Fantinel, ...
    Journal of Applied Remote Sensing 17 (2), 024509-024509 , 2023
    2023
    Citations: 8
  • Flooded rice variables from high-resolution multispectral images and machine learning algorithms
    FC Eugenio, M Grohs, MS Schuh, LP Venancio, C Schons, TL Badin, ...
    Remote Sensing Applications: Society and Environment 31, 100998 , 2023
    2023
    Citations: 7
  • Uso de imagens orbitais no geoprocessamento algébrico da microrregião da Campanha Ocidental, Rio Grande do Sul
    EA Silva, RS Pereira, CK Silva, LCG Goergen, MS Schuh
    Instituto de Florestas, Universidade Federal Rural do Rio de Janeiro , 2014
    2014
    Citations: 6
  • Albedo trend analyses in atlantic forest biome areas
    E Alba, RS Pereira, J Marchesan, EA Silva, KVS Batista FdJ
    Journal of Agricultural Science 10 (10), 298-307 , 2018
    2018
    Citations: 5
  • Use of hyperspectral sensor and random forest for broadleaf and narrowleaf weed differentiation
    P Fernandes, SD Pereira da Silva, RA Fantinel, NB Junior, ...
    New Forests 56 (5), 46 , 2025
    2025
    Citations: 3
  • Use of Machine Learning Algorithms in the Classification of Forest Species
    TM Loiola, RA Fantinel, FD dos Santos, F de Bastos, MS Schuh, ...
    Anuário do Instituto de Geociências 46, 1-8 , 2023
    2023
    Citations: 3
  • Análise do uso e cobertura da terra utilizando imagens sentinel-2A e inteligência artificial
    MF Wolfer, J Marchesan, E Alba, M Schuh, DH Honnef, HS Oliveira, ...
    Anuário do Instituto de Geociências 43 (4), 395_403-395_403 , 2020
    2020
    Citations: 3
  • 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
  • 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
  • Estimativa da biomassa acima do solo em floresta de terra firme na Amazônia com dados LiDAR aerotransportado e upscaling com imagens orbitais
    MS Schuh, RS Pereira, JA Farias, FC Eugenio, EA Silva, FFM Rovani, ...
    2023
    Citations: 1
  • Artificial intelligence and orbital images application for analysis of spatial land use and coverage patterns.
    RA Fantinel, RS Pereira, ACP Benedetti, FC Eugenio, J Marchesan, ...
    2022
    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
    M Ziembowicz, S Pereira
    Australian Journal of Basic and Applied Sciences 11 (14), 17-24 , 2017
    2017
    Citations: 1
  • Spectroradiometry of commercial wood veneers in the visible and near infrared spectra.
    DB Lippert, AC Paim Benedetti, MM da Costa Nunes, LA Kervald, ...
    Floresta 44 (1) , 2014
    2014
    Citations: 1