Abstract
Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively.
Highlights
There are many situations where knowledge of forest species diversity and distribution of stand characteristics are needed
This study has shown that the spatial analytical indices of remotely sensed data
The results of this study revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber and saplings size classes, and hardwood and softwood categories of species
Summary
There are many situations where knowledge of forest species diversity and distribution of stand characteristics are needed. Estimation of biomass, carbon sequestration, primary productivity, nutrient export, and quantities for clearing prior to construction are only a few examples where characteristics of forested areas are essential. Forests can encompass very large areas so that ground-based evaluations can be very expensive and time consuming. For this reason the use of remotely sensed data has become increasingly common. Several sources of remotely sensed data are currently available that might be useful for forest characterization purposes. The data can be from satellite or aircraft platforms, and can be from either passive or active instruments. The focus has been on the use of laser altimetry, e.g., Light
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