Estimation of forest structural information using RapidEye satellite data

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

“…The produced models (1, 3, 4, 5, 8, 9, 10, 11 and 12) allowed predictions of the BA, SV, SHI, SII, SDDBH, PI, DDI and M (R 2 adj values were between 0.50 and 0.70, p < 0.01). Vegetation indices were commonly used and promising independent variables in estimation of forest stand variables [55,57]. In the present study, only VI was included in models (9-12), whereas no vegetation indices were included in model (1,3,4,5,8), though eight vegetation indices were involved as potential regressors to establish the predict models.…”

“…For instance, Steininger [58] and Castillo-Santiago et al [35] documented that the best results for spectral information (vegetation indices) to explain variation in forest structure were at lower biomass level. Eckert [59] and Wallner et al [55] explained the effectiveness of the their vegetation indices (e.g., GR and SR) for estimating forest stand variables as follows: a low value for the vegetation indices implies the presence of stands of coniferous forest with shady areas and relatively low stand density, while higher values for these indices imply broadleaved forest with a closed canopy. In the present study, we obtained very low and non-statistically significant correlation coefficients between the forest stand variables and vegetation indices, e.g., GEMI, GR, MSI, and SVR.…”

“…For example, Eckert [1] reported that the estimation of tropical rainforest biomass/carbon could be improved by developing and applying forest stratum-specific models. Wallner et al [55] firstly stratified forest inventory plots based on forest types and then produced separate predictive models and found that stratification improved the regression models. Similarly, we also produced the models for the other stand variables using all data excluding those from the pure plantation; however, no significant/obvious improvement was observed for R 2 adj , in contrast to M. Therefore, these are not listed in Tables 4 and 5.…”

“…In terms of the forest stand variables to be predicted, most studies focused on the estimation of conventional forest stand variables such as SV, NT, BA, and QMD using remotely sensed data [35,55,74,75]. Only a few studies have investigated the extraction of the more complex structural variables such as tree size diversity and tree position diversity [13,76,77].…”

“…In the present study, we used all possible subsets. Following Castillo-Santiago et al [35], Ozdemir and Karnieli [13] and Wallner et al [55], we employed a cut-off value for variance inflation factor (VIF) of less than four, and restricted the number of independent variables to four to avoid multicollinearity.…”