Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A

Abstract

In order to evaluate forest quality and carbon stocks and improve our understanding of ecosystems and carbon cycling processes, the accurate measurement of aboveground biomass (AGB) and other forest characteristics is crucial. This paper considers the response differences between the bands obtained from Landsat 8 and Sentinel-2A sensors, respectively, and combines the exhaustive combination of spectral indices with normalization and ratio techniques to establish suitable weights for the bands in the vegetation index using relative sensitivity and noise equivalent (NE) to improve the saturation effect between the vegetation index and forest parameters (canopy closure (CC), forest stand density (S), basal area (BA), and AGB) and extend the linear relationship between them. This paper also considers the effects of window size, direction, and principal component analysis on texture features, adds weight to textures and combines textures using linear correlation and NE, establishes texture indices to improve the limitations of information contained in individual texture features, analyzes the potential of texture features to evaluate each forest parameter under different conditions, and better captures the variation of forest parameters. In this paper, we only analyze the planted coniferous forest in Saihanba to avoid the differences in electromagnetic wave effects that are difficult to judge and analyze because of the differences in leaf size and leaf orientation between coniferous and broad-leaf forests. In contrast, the vegetation indices and texture indices obtained from Sentinel-2A could better estimate each vegetation parameter, and the linear estimation of each vegetation parameter using the new texture index reached an R2 above 0.65. The results of this study indicate that Sentinel-2A and Landsat 8 are promising remote sensing datasets for estimating vegetation parameters at the regional scale, and Sentinel-2A data can be employed as the primary source of earth observation data for assessing forest resources in the Saihanba area.

Suitability assessment for forest landscape restoration based on species diversity conservation

“…Saihanba is the largest plantation forest base in China (Sa and Fan, 2023). Statistics reveal that the plantations in the Saihanba area cover an area of approximately 561.76 km 2 , but only 33.67% of this area consists of mixed forests.…”

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

“…Because horizontal structure can be defined as the arrangement of canopy closure or expressed through the distribution of trees, CC, S, and BA were used as the forest horizontal structure based on the measured data. The new horizontal structure indices were created based on the bands and textures extracted from S2A and L8 to help extend the linear relationship between remote sensing variables and vegetation parameters [48]. In this study, new ratio vegetation indices (RVI) (Equation ( 2)) (Table S4) were created by adding weights (m) to the ratio approach based on the response differences between spectral bands.…”

“…In this study, new ratio vegetation indices (RVI) (Equation ( 2)) (Table S4) were created by adding weights (m) to the ratio approach based on the response differences between spectral bands. In addition, considering the differences between textures in each band, weights (m) was added to the texture and combined with the ratio technique to create new ratio texture indices (RTI) (Equation ( 3)) based on three different textures: the same texture (corresponding texture index (CTI), Table S5); the average texture in each direction (mean texture index (MTI), Table S6); and the principal component texture (principal component texture index (PTI), Table S7) [48]. Extracted variables are summarized in Table 4 (all variable extraction methods and detailed explanations are described in Supplementary S1).…”

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

“…In this study, we combined the significant differences in vegetation surface reflectance and spectral sensitivity exhibited by various sensors, analyzed the spectral coupling and complementary effects of the S2A and L8 sensors based on common horizontal structural information (CC, S, and BA), and established the horizontal structural indices to help extend the linear relationship between remote sensing variables and vegetation parameters [36]. We considered the differences in response between the bands obtained by L8 and S2A sensors, combined the exhaustive combination of spectral indices with the ratio technique, analyzed the sensitivity of the weights occupied by the bands in the vegetation index to the differences in each vegetation parameter using the relative sensitivities [37] and noise equivalents [38,39], and determined the optimal weights occupied by the bands to establish the ratio vegetation index (RVI) (Equation ( 2)).…”