The estimation of selective logging impact in Amazon forest using LIDAR data

Authors

DOI:

https://doi.org/10.5902/1980509826007

Keywords:

Airborne LiDAR, Sustainable forest management, Amazon forest, Impact

Abstract

Forest management activities are crucial for the sustainable development of Brazil. Those activities require, however, a strict monitoring that are ofen difcult to operationalize. The mapping of impacted areas by selective logging and the measurement of forest impacts because of logging operations are mostly based on extensive and costly feld surveys. In this study, the Light Detection and Ranging (LiDAR) airborne technology was used to assess the impacts caused by selective logging within 21 units of forest annual production in the Amazon. The study sites are in the states of Rondônia and Pará, within National Forests under federal forestry concession. We used two metrics derived from the point cloud LiDAR for mapping forest impacts: The Canopy Height Model (CHM) and the Relative Density Model (RDM) as forest understory metric. The results of detection of forest impacts derived from the LiDAR dataset showed similar performance of feld-based surveys. We estimated that selective logging activities had impacted an average of 6.8% (± 1.3%, standard deviation) of the forest understory of the Annual Production Units (APU) studied and caused an increase of 4.9% (± 0.9%) in areas of forest canopy opening. The LiDAR technology showed to be effective for assessing and monitoring forest impacts of selective logging in the federal forest concessions in the Amazon.

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Author Biographies

Charton Jahn Locks, Serviço Florestal Brasileiro, Universidade de Brasília, Brasília, DF

Formado em Engenharia Ambiental pela UFMS (2006) e com MBA em Gerenciamento de Projetos pela FGV (2013), Mestre em Ciências Florestais pela UnB (2017) é atualmente Analista Ambiental do Ministério do Meio Ambiente (MMA), lotado no Serviço Florestal Brasileiro (SFB).

Eraldo Aparecido Trondoli Matricardi, Universidade de Brasília, Brasília, DF

Possui graduação em Engenharia Florestal pela Universidade Federal de Mato Grosso (1986), especialização em aerofotos pela Universidade Federal de Santa Maria (1987), mestrado em Geografia (ênfase em Sensoriamento Remoto) pela Michigan State University (2003) e doutorado em Geografia (ênfase em Geoprocessamento) pela Michigan State University (2007). Atuou na iniciativa privada, Governo de Rondônia, Nações Unidas e Michigan State University. Atualmente é professor Adjunto IV da Universidade de Brasília, consultor Ad-hoc da National Science Foundation, da CAPES e do MMA-Projeto ARPA, com atividades na área de Mudanças Climáticas, Geoprocessamento, Sensoriamento Remoto e Sistema de Informação Geográfica aplicados ao Planejamento Físico-Rural, Zoneamento Ecológico-Econômico, degradação florestal, incêndios florestais, análises ambientais e às mudanças do uso e cobertura da terra.

References

ANDERSEN, H. E. et al. Monitoring selective logging in western Amazonia with repeat LiDAR flights. Remote Sensing of Environment, New York, v. 151, p. 157-165, 2014.

ASNER, G. P. et al. Selective logging in the Brazilian Amazon. Science, Washington, n. 310, p. 480-482, 2005.

ASNER, G. P. et al. A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia, Berlin, v. 168, n. 4, p. 1147-1160, 2011.

ASNER, G. P.; KELLER, M.; SILVA, J. N. M. Spatial and temporal dynamics of forest canopy gaps following selective logging in the eastern Amazon. Global Change Biology, [s. l.], v. 10, p. 765-783, 2004.

BALTSAVIAS, E. P. A comparison between photogrammetry and laser scanning. ISPRS Journal of Photogrammetry and Remote Sensing, [s. l.], v. 54, n. 2/3, p. 83-94, 1999.

BOMFIM, S. L. et al. Índice de clima econômico para concessões florestais. Scientia Florestalis, Piracicaba, v. 44, n. 10, p. 331-342, 2016.

BRASIL. Ministério do Meio Ambiente. Serviço Florestal Brasileiro. Guia para medição de produtos e subprodutos florestais madeireiros das concessões florestais. Brasília, 2012. 48 p.

COOPS, N. C. et al. Estimating canopy structure of Douglas-fr forest stands from discretereturn LIDAR. Trees, [s. l.], v. 21, n. 3, p. 295-310, 2007.

D’OLIVEIRA, M. V. N. et al. Estimating forest biomass and identifying low-intensity logging areas using airborne scanning LiDAR in Antimary State Forest, Acre State, Western Brazilian Amazon. Remote Sensing of Environment, New York, v. 124, p. 479-491, 2012.

ELLI, P. et al. Mapping selective logging impacts in Borneo with GPS and airborne LiDAR. Forest Ecology and Management, Amsterdam, v. 365, p. 184-196, 2016.

EMMERT, F. Combinação de dados de campo e métodos computacionais para o planejamento de exploração florestal na Amazônia. 2014. Tese (Doutorado) - Universidade de Brasília, Brasília, 2014.

GIANNICO, V. et al. Estimating stand volume and above-ground biomass of urban forests using LiDAR. Remote Sensing, Basel, v. 8, p. 14, 2016.

GIONGO, M. et al. LiDAR: princípios e aplicações florestais. Pesquisa Florestal Brasileira,

Colombo, v. 30, n. 63, p. 231-244, 2010.

GOODCHILD, M. F.; HUNTER, G. J. A simple positional accuracy measure for linear features. International Journal of Geography Information Science, London, v. 11, p. 299-306, 1997.

HUNTER, M. O. et al. Structural dynamics of tropical moist forest gaps. PLOS One, San Francisco, v. 10, p. 19, 2015.

IBGE. Manual técnico da vegetação brasileira. 2. ed. Rio de Janeiro: IBGE, 2012. 271 p.

IBAMA. Norma de Execução Nº 1, de 24 de abril de 2007. Diretrizes técnicas para elaboração dos planos de manejo florestal sustentável – PMFS. Diário Ofcial da União: seção 1, Brasília, DF, p. 405, 30 abr. 2007.

JENSEN, J. R. Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres. São José dos Campos: Parêntese, 2009. 672 p.

JOHNS, J. S.; BARRETO, P.; UHL, C. Logging damage during planned and unplanned logging operations in the eastern Amazon. Forest Ecology and Management, Amsterdam, v. 89, p. 59-77, 1996.

LENTINI, M. W.; ZEWEED, J. C.; HOLMES, T. P. Measuring Ecological Impacts from Logging in Natural Forests of the Eastern Amazônia as a Tool to Assess Forest Degradation. Rome: [s. n.], 2009. (Forest Resources Assessment Working Paper, 165).

MATRICARDI, E. A. T. et al. Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data. International Journal of Remote Sensing, London, v. 34, n. 4, p. 1057-1086, 2013.

MCGAUGHEY, R. J. FUSION/LDV: Sofware for LIDAR data analysis and visualization. [S. l.]: United States Department of Agriculture, Forest Service, Pacifc Northwest Research Station, 2010.

MERRY, F. et al. Balancing conservation and economic sustainability: the future os the Amazon timber industry. Environmental Management, Springer, v. 44, p. 395-407, sep. 2009.

NÆSSET, E.; GOBAKKEN, T. Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment, New York, v. 96, p. 453-465, 2005.

NEPSTAD, D. C. et al. Large-scale impoverishment of Amazonian forests by logging and fire. Nature, London, n. 398, p. 505-508, 1999.

PEREIRA, R. et al. Forest canopy damage and recovery in reduced-impact and conventional selective logging in eastern Para, Brazil. Forest Ecology and Management, Amsterdam, v. 168, p. 77-89, 2002.

PINAGÉ, E. R.; MATRICARDI, E. A. T. Detecção da Infraestrutura para Exploração Florestal em Rondônia Utilizando Dados de Sensoriamento Remoto. Floresta e Ambiente, Rio de Janeiro, v. 22, n. 3, p. 377-390, 2015.

PUTZ, F. E. et al. Sustaining conservation values in selectively logged tropical forests: the attained and the attainable. Conservation Letters, Hoboken, v. 5, p. 296-303, 2012.

SOUZA, C. M. et al. Ten-Year Landsat Classifcation of Deforestation and Forest Degradation in the Brazilian Amazon. Remote Sensing, Basel, n. 5, p. 5493-5513, 2013.

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Published

2019-06-30

How to Cite

Locks, C. J., & Matricardi, E. A. T. (2019). The estimation of selective logging impact in Amazon forest using LIDAR data. Ciência Florestal, 29(2), 481–495. https://doi.org/10.5902/1980509826007

Issue

Vol. 29 No. 2 (2019)

Section

Articles

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