Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

Abstract

-The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and nearinfrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P. elliottii had an R² adj of 0.67 with a S yx of 31.46 m³ ha -1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.Index terms: Pinus elliottii, remote sensing, vegetation indices, volume modeling. Caracterização espectral de plantios florestais com imagens Landsat 8/OLI para planejamento e manejo florestalResumo -O objetivo deste trabalho foi avaliar o uso de imagens Landsat 8/OLI na diferenciação da idade e na estimativa do volume total de Pinus elliottii, para determinar a aplicabilidade desses dados no planejamento e no manejo da atividade florestal. Foram instaladas 53 unidades amostrais e mensuradas as variáveis dendrométricas de povoamentos comerciais de P. elliottii aos 9 e 10 anos. Os números digitais da imagem foram convertidos à reflectância de superfície e, posteriormente, foram elaborados índices de vegetação. A diferenciação da idade dos povoamentos baseou-se nos valores de reflectância da banda do vermelho e do infravermelho próximo. A análise de regressão das variáveis espectrais foi utilizada na estimativa do volume total. O aumento da idade propiciou um acréscimo da reflectância na banda do infravermelho próximo e uma redução na banda do vermelho. A equação geral de estimativa do volume total para P. elliottii apresentou R² aj de 0,67 com S yx de 31,46 m³ ha -1. Dessa forma, os dados espectrais de média resolução espacial provenientes do satélite Landsat 8/OLI podem ser utilizados na distinção das fases de crescimento dos povoamentos sendo, portanto, passíveis de utilização no planejamento e no manejo adequado da atividade florestal em escala espacial e temporal.Termos para indexação: Pinus elliottii, sensoriamento remoto, índices de vegetação, modelagem do volume.

Methodological evaluation of vegetation indexes in land use and land cover (LULC) classification

“…Corroborating with the results obtained in the present study, Liaqata et al (2017) found that SAVI was the index that best adjusted for estimated agricultural production in irrigated areas in Pakistan; González-Dugo and Mateos (2008) observed that SAVI also excels when used in irrigated cotton and beet crops in southern Spain. For planted forests areas, Alba et al (2017) demonstrated that SAVI was the index that had the best correlation with the volume estimation for a Pinus elliottii forest. In a study developed by Cassol (2013), Maciel (2002) and Bernardes (1998), the SAVI index also stood out as one of the spectral variables with the greatest relation to the forest biomass in a Mixed Ombrophilous Forest, which has a high population density.…”

“…For the variables selection, it was used the Forward method, and the indices that contributed the most to the identification of land use and occupation classes were used, as it allows to examine the contribution of each independent index to the regression model. The models were evaluated based on the adjusted coefficient of determination (R 2 aj), standard error of the estimate (Syx) and coefficient of variation (CV%), where the fitting level of the selected models for each class of land use and occupation was determined by the distribution of the residuals (Alba et al, 2017) and by the sum of the statistical scores proposed by Thiersch (1997). It was assigned the lowest weight (one) for the best statistical results of each evaluated index, the best model was designated by the sum of the scores, values from one to N, where the lowest sum of the scores indicates the selection of the best equation.…”

Espacialidad volumétrica de madera en plantaciones forestales usando redes neurales artificiales con imágenes de satélite

“…El índice RVI fue determinado por la división de la banda 4 (B4) entre banda 3 (B3) de la imagen digital Spot 6 (Ecuación (10)) (WANG et al, 2012). El DVI, definido por la ecuación (11), resultó de la sustracción de la banda 4 con la 3; el NDVI se obtuvo por la división entre la diferencia de la banda 4 con la 3 y la suma de las mismas, según la ecuación (12), y el GNDVI fue calculado con igual ecuación matemática que el NDVI, pero cambiando la banda 3 por la 2 (B2), como está en la ecuación (13) (Alba et al, 2017). El ARVI integra las bandas 4, 3 y 1 (B1), como se observa en la ecuación (14) (Pitriya et al, 2018).…”

“…Indices de vegetación utilizados en el estudio. γ = factor de autocorrección atmosférica, que normalmente se define con valor de 1 (Alba et al, 2017). L es un factor de corrección que minimiza el efecto de reflectancia del suelo, estimado como 0.25 para áreas de bosque (Ren et al, 2018).…”

Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data

“…In this sense, the utilization of remote sensing techniques enables an easier and faster method of acquisition and organization of the pertinent information in the inventory (Alba et al, 2017). Remote sensing techniques is been applied in forest studies looking to facilitate the characterization of forest formations in terms of the quantification of forest stocks (Watzlawick et al, 2009).…”