Estimation of wood volume of <i>Eucalyptus dunnii</i> and <i>urograndis</i> of different ages using TM/Landsat 5i
DOI:
https://doi.org/10.5902/1980509834751Keywords:
Optical remote sensing, Spectral bands, Vegetation indices, Predictive modelsAbstract
The aim of this study was the generation of trade volume models with bark (VB) in Eucalyptus dunnii and urograndis stands at different growth site conditions, using reflectance data from different regions of the electromagnetic spectrum and vegetation indices. The multiple linear regressions generated, resulting in three predictive models, being the most significant ones, in decreasing order were for the species of Eucalyptus dunnii and Eucalyptus urograndis of 50 and 59 months of age, respectively, followed by the model for 25-month old Eucalyptus urograndis. Although, the regression equation for 34-month old Eucalyptus dunnii was not significant. The vegetation index adjusted to the soil (SAVI 0.5) was relevant in the construction of the three models dueto the different degrees of contribution of the soil component, as a result the non-closure of the crowns in young stands at different ages. The short wave-infrared region (SWIR 1 – TM5) also composed the models of Eucalyptus dunnii and Eucalyptus urograndis, analyzed together and the predictive model for Eucalyptus dunnii, demonstrating the importance of the forest canopy structure because this region of the electromagnetic spectrum is related to increased lignin content and because of the evolution of its productive capacity.
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ALBA, E.et al. Uso de imagens de média resolução espacial para o monitoramento de dosséis de Eucalyptus grandis. Revista Scientia Agraria, Curitiba, v. 18, n. 4, p. 1-8, out-dez. 2017.
ALMEIDA, A. Q.et al. Relações empíricas entre características dendrométricas da Caatinga brasileira e dados TM Landsat 5. Pesquisa Agropecuária Brasileira, Brasília, v. 49, n. 4, p. 306-315, abr. 2014.
BERRA, E. F.et al. Estimativa do volume total de madeira em espécies de eucalipto a partir de imagens de satélite Landsat. Ciência Florestal, Santa Maria, v. 22, n. 4, p. 853-864, out.-dez. 2012.
BERRA, E. F.; FONTANA, D. C.; KUPLICH, T. M. Tree age as adjustment factor to NDVI. Árvore, Viçosa, vol. 41, n. 3, fev. 2017.
BOLDRINI, I. I. A flora dos campos do Rio Grande do Sul. In: PILLAR, V. P.; MÜLLER, S. C.; CASTILHOS, Z.M.S.; JACQUES, A.V. A. (eds.). Campos Sulinos, conservação e uso sustentável da biodiversidade. MMA, Brasília, p. 63-77, 2009.
CANAVESI, V.; PONZONI, F. J.; VALERIANO, M. M. Estimativa de volume de madeira em plantios de Eucalyptus spp. utilizando dados hiperespectrais e dados topográficos. Revista Árvore, Viçosa, v. 34, n. 3, p.539-549, jun. 2010.
CHANDER, G.; MARKHAM, B. L.; BARSI, J. A. Revised Landsat-5 thematic mapper radiometric calibration. Geoscience and Remote Sensing Letters, Maceió, v. 4, p.490-494, jul.2007.
DANIELSSON, J.et al. Using a bootstrap method to choose the sample fraction in tail index estimation. Journal of Multivariate analysis, Quebec, v. 76, n. 2, p. 226-248, fev. 2001.
DUBE, T.; MUTANGA, O. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, Calgary, v. 101, p. 36-46, mar. 2015.
EFRON, B. The Jackknife, the Bootstrap, and Other Resampling Plans CBMS-NSF Regional Conference Series in Applied Mathematics. Society for Industrial & Applied Mathematics, 100p. 1987.
EFRON, B.; TIBSHIRANI, R. Improvements on cross-validation: the 632+ bootstrap method. Journal of the American Statistical Association, Richmond, v. 92, n. 438, p. 548-560, jun. 1997.
GOEL, N. S. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews, Morgantown, v. 4, p. 1–212, dez. 1988.
GOERGEN, L. C. G.et al. Distinção de espécies de eucalipto de diferentes idades por meio de imagens TM/Landsat 5. Pesquisa Agropecuária Brasileira, Brasília, v. 51, p. 53-60, jan. 2016.
GUTMAN, G.et al. Assessment of the NASA–USGS Global Land Survey (GLS) datasets. Remote Sensing of Environment, Toronto, v. 134, p. 249-265, jul. 2013.
HUETE, A. R. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, Toronto, v.25, p.295-309, ago. 1988.
INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS - INPE. Centro de Dados de Sensoriamento Remoto. Catálogo de imagens: satélite Landsat 5. Disponível em: http://www.dgi.inpe.br/CDSR/. Acesso em: 15 mar. 2012.
ITT - VISUAL INFORMATION SOLUTIONS. Atmospheric correction module: QUAC and FLAASH user’s guide. 2009. 44p.
KROSS, A. et al. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. International Journal of Applied Earth Observation and Geoinformation, v. 34, p. 235–248, sep. 2015.
KUINCHTNER, A.; BURIOL, G. A. Clima do Estado do Rio Grande do Sul segundo a classificação climática de Köppen e Thornthwaite. Disciplinarum Scientia, Santa Maria, v. 2, p. 171-182, jan-fev. 2001.
JORDAN, C. F. Derivation of leaf-area index from quality of light on the forest floor. Ecology, Davis, v.50, p.663-666, jul.1969.
LI, S.; QUACKENBUSH, L. J.; IM, J. Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery. Remote Sensing, Flagstaff, v. 11, n. 16, aug. 2019.
LU, D.et al. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management, Amsterdam, v. 198, n. 1-3, p. 149–167, ago. 2004.
MACEDO, F. L.et al. Estimativa do volume de madeira para Eucalyptus sp. com imagens de satélite de alta resolução espacial. ScientiaForestalis, Piracicaba, v. 45, n. 114, p.237-247, jun. 2017.
NETER, J.et al. Applied Linear Statistical Models. Chicago: IRWIN, 1996. 1408 p.
PONZONI, F. J.et al. Caracterização espectro temporal de dosséis de Eucalyptus spp. mediante dados radiométricos TM/Landsat5. Cerne, Lavras, v. 21, p. 267-275, abr-jun. 2015.
REIS, A. A.et al. Relationship Between Spectral Data and Dendrometric Variables in Eucalyptus sp. stands. Floresta e Ambiente, Seropédica, v. 25, n. 2, jun. 2018.
ROUSE, J. W. et al. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Greenbelt: NASA, 1974. 371p.
SANQUETTA, C. R.et al. Inventários florestais: planejamento e execução. Curitiba: Multi-Graphic Gráfica e Editora, 2014. 406p.
SANTOS, H. G.et al. Sistema brasileiro de classificação de solos. Brasília: Embrapa, 2013. 353p.
SILVA, C. A.et al. A principal component approach for predicting the stem volume in Eucalyptus plantations in Brazil using airborne LiDAR data. Forestry, Farnham, v. 89, n. 4, p. 422-433, mar. 2016.
SOIL SURVEY STAFF. Keys to soil taxonomy. Washington: USDA-NRCS, 2014. 372p.
SOUZA, C. L.; PONZONI, F. J. Relação entre índice de área foliar, estimado através de sensoriamento remoto, e parâmetros dendrométricos em floresta implantada de Pinus spp. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 9; 1998, Santos. Anais... Santos: INPE/SELPER, 1998, p. 1549-1560.
ZHAO, Q.et al. Comparison of machine learning algorithms for forest parameter estimations and application for forest quality assessments. Forest Ecology and Management, Arizona, v. 434, p. 224–234, feb. 2019.
WATZLAWICK, L. F.; KIRCHNER, F. F.; SANGUETTA, C. R. Estimativa de biomassa e carbono em floresta com araucária utilizando imagens do satélite IKONOS II. Ciência Florestal, Santa Maria, v. 19, p. 169-181, abr.-jun. 2009.
