Abstract
The phytosanitary status of Tectona grandis plantations are monitored conventionally with periodic data collection in the field, which is often costly and has low efficiency. The objective of this research was to develop a methodology to predict the canopy cover of T. grandis plantations using multispectral images of the Sentinel-2 (S2) satellite and photographic imagery. The study was carried out in a T. grandis plantation of seminal origin, in Cáceres, Mato Grosso state, Brazil. Hemispherical photographic (HP) images of the plant canopy were obtained with a digital camera coupled to a “fisheye” lens fixed at 1.3 m high at two dates in the rainy and the dry season. Cloudless and no shadow images of the S2 satellite bands were concurrently obtained with the field images. Multivariate permutative analysis of variance (PERMANOVA) and partial least squares regression (PLSR) were used to predict canopy cover percentage. The accuracy of the predicted T. grandis canopy cover (%) by the PLSR model approach was 77.8 ± 0.09%. The results indicate that a PLS model calibrated with 28 HP sample images can accurately estimate the percentage canopy cover for a continuous area of T. grandis plantations and facilitate mapping of canopy heterogeneity to monitor threats of diseases, mortality, fires, pests and other disturbances.
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Acknowledgements
To the Brazilian agencies “Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasil)”, “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-Finance Code 001)”, “Programa Cooperativo sobre Proteção Florestal/PROTEF do Instituto de Pesquisas e Estudos Florestais/IPEF” and “Pró-Reitoria de Pesquisa, Pós-graduação e Inovação (PROPES) do Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso (IFMT, Brasil)” for supporting our research.
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Santos, I.C.d., dos Santos, A., Costa, J.G. et al. Tectona grandis canopy cover predicted by remote sensing. Precision Agric 22, 647–659 (2021). https://doi.org/10.1007/s11119-020-09748-w
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DOI: https://doi.org/10.1007/s11119-020-09748-w