SPECTRAL DISCRIMINATION AND CLASSIFICATION OF PINUS SP. WITH MULTISPECTRAL UAS IN A NATURALLY REGENERATING AREA

Authors

DOI:

https://doi.org/10.5380/biofix.v11i1.105508

Abstract

The spread of Pinus beyond planted areas threatens biodiversity in protected areas, and conventional monitoring of these outbreaks is costly and limited in spatial coverage. We hypothesised that the spectral response of Pinus and broadleaf trees differs consistently across the bands of a multispectral sensor onboard an unmanned aerial system (UAS), and that this difference suffices to support automated classification of invasion outbreaks. The aim was to characterise the spectral signature of both groups and to classify the occurrence of Pinus sp. in a naturally regenerating area. The study was carried out over 11.14 ha at the Canguiri Experimental Farm, Pinhais, Paraná, Brazil, using imagery from a DJI Mavic 3M drone. After object-based segmentation, more than 500 samples per band were extracted to describe the digital numbers (DN) of both groups; the NDVI and NDRE indices, texture metrics and visible bands then fed a Random Forest classifier fitted with 1,354 samples across five thematic classes. Broadleaves showed higher DN in the visible range, notably in the green band (141.81 against 123.31), whereas Pinus prevailed in the near infrared (148.95 against 134.95); the red edge showed coincident means. Classification reached an overall accuracy of 92.8%, a Kappa index of 0.892 and an F-score of 0.856 for Pinus sp., which covered 1.187 ha. We conclude that classifier performance rests on the near infrared and visible contrasts, and that the red edge contributes only in normalised form.

Author Biographies

Caio Cesar Moraes Brandelik, Universidade Federal do Paraná

Possui graduação em Engenharia Florestal pela Universidade Federal do Paraná (2011). Mestrado Profissional em Perícias Ambientais pela Universidade Federal de Santa Catarina. Atualmente atual como Engenheiro Florestal no Instituto do Meio Ambiente de Santa Catarina (IMA SC), desenvolvendo atividades voltadas ao Licenciamento Ambiental e Fiscalização Ambiental.

Daniel Zambiazzi Miller, Universidade Federal do Paraná

Graduação em Engenharia Florestal pela Universidade Federal do Paraná (2015).Mestre em Engenharia Florestal, Área de Concentração em Conservação da Natureza pela Universidade Federal do Paraná (2021). Doutorando em Engenharia Florestal, Área de Concentração em Conservação da Natureza pela Universidade Federal do Paraná.

Faiman Orlando da Silva, Universidade Federal do Paraná

Engenheiro Florestal, atualmente mestrando em Engenharia Florestal, com ênfase em Conservação da Natureza, na Universidade Federal do Paraná (UFPR), onde também realiza especialização em Prevenção e Combate aos Incêndios Florestais. Atuou na Área de Proteção Ambiental de Maputo, desenvolvendo atividades nas áreas de monitoramento ambiental, educação ambiental e engajamento comunitário.

Lénia Francisco Matsinhe, Universidade Federal do Paraná

Possui graduação em Ciência de Informação Geográfica pela Universidade Eduardo Mondlane (2022) . Atualmente é Técnica de GIS da Área de Protecção Ambiental de Maputo. Tem experiência na área de Geociências , com ênfase em Geografia Física.

Ana Paula Dalla Corte, Universidade Federal do Paraná

Engenharia Florestal, tem mestrado e doutorado pela Universidade Federal do Paraná (2010). É professor da Universidade Federal do Paraná (UFPR), lotada no Departamento de Ciências Florestais (DECIF) no nível Associado IV, ministrando disciplinas para a graduação e pós-graduação Stricto Sensu no Programa de Pós-graduação em Engenharia Florestal (UFPR). Pesquisadora de produtividade em pesquisa do CNPq desde 2015. Foi professor visitante da Universidade da Flórida entre 2019/2020 através do programa CAPES/Print. Possui pós-doutorado em Engenharia Florestal na Universidade da Flórida, Estados Unidos, vinculada ao projeto CMS4D: A Multi-Scale Data Fusion Prototype System for the next Generation of Carbon Dynamics Monitoring from Space: A Case Study in the Brazilian Cerrado Fire and Fuel in a Biodiversity Hotspot. Coordena o grupo de pesquisa ForestEyes (https://gpforesteyes.github.io/). Coordena o curso de especialização em Manejo Florestal de Precisão (UFPR). Atua em projetos de pesquisas relacionados aos seguintes tópicos: Escaneamento a laser (Lidar) aéreo, terrestre e UAV (ALS/TLS/UAV). Fotogrametria, sensoriamento remoto e análise espacial aplicados aos inventários florestais. Técnicas de amostragem para Inventário Florestal. Quantificação de biomassa florestal. Possui 17 livros publicados e 22 capitulo de livro. Possui mais de 300 artigos científicos publicados em revistas indexadas nacionais e internacionais. Participou de mais de 100 trabalhos técnicos na área de meio ambiente e engenharia florestal. Foi uma das fundadoras da Rede Mulher Florestal, foi fundadora e é a atual vice-presidente da Associação Brasileira de Mensuração Florestal e atua em grupos técnicos no Brasil e exterior. Membro do Forest Carbon Working Group (WG) da VERRA desde 2024. É revisora de diversas revistas científicas indexadas internacionais.

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Published

2026-07-31

How to Cite

Campos Modesto Pereira, J. L., Moraes Brandelik, C. C., Zambiazzi Miller, D., Orlando da Silva, F., Francisco Matsinhe, L., & Dalla Corte, A. P. (2026). SPECTRAL DISCRIMINATION AND CLASSIFICATION OF PINUS SP. WITH MULTISPECTRAL UAS IN A NATURALLY REGENERATING AREA. BIOFIX Scientific Journal, 11(1), 73–80. https://doi.org/10.5380/biofix.v11i1.105508

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Vol. 11 No. 1 (2026)

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Copyright (c) 2026 Jordan Luis Campos Modesto Pereira, Caio Cesar Moraes Brandelik, Daniel Zambiazzi Miller, Faiman Orlando da Silva, Lénia Francisco Matsinhe, Ana Paula Dalla Corte

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