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

Abstract

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images

“…Tree position diversity was represented by the uniform angle index (UAI), tree species intermingling index (TSII), DBH dominance index (DBHDI) and diameter differentiation index (DDI). A detailed description of these indices can be found in Meng et al [63]. Additionally, we also included humus depth (HD) and canopy closure (CC) for the calculation of the forest health indicator.…”

“…Additionally, we also calculated the 10 vegetation indices that are documented to be widely used in forestry researches. The formulas of these indices and their relevant applications in forestry are summarized in Table 1 [54,63,[66][67][68][69]. Table 1.…”

“…Normalized Difference Vegetation Index NDVI = N IR−RED N IR+RED [63,67] Simple Ratio SR = N IR RED [63,67] Ratio of NIR to GREEN GR = N IR GREEN [63,67] Ratio of GREEN to RED VI = GREEN RED [63,67] Soil Adjusted Vegetation Index SAVI = N IR−RED N IR+RED+L [63,68] Moisture Stress Index MSI = SW IR N IR [54,63] Standardized Vegetation Index…”

“…N IR+RED+0.5 [63,69] RED, GREEN, NIR and SWIR are the surface reflectance of the red, green, near-infrared and shortwave infrared bands, respectively, and L was set to 0.5 for SAVI.…”

“…We calculated the SDGL for all multispectral reflectance bands, i.e., near-infrared, red, green and the shortwave infrared band. In comparison to the first-order textural measures, the second-order textural measures were calculated using only the panchromatic band, which was documented to be well suited to textural analysis due to its relatively high spatial resolution [63,70]. In the present study, we employed the grey level co-occurrence matrix (GLCM) as the second-order textural measure to represent the textural features.…”