Ecological land classification and soil moisture modelling in the boreal forest using LiDAR remote sensing
SOUTHEE, FLORENCE MARGARET
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Ecological land classification (ELC) is used to classify forest types in Ontario based on ecological gradients of soil moisture and nutrient fertility determined in the field. If ELC could be automated using terrain surfaces generated from airborne Light Detection and Ranging (LiDAR) remote sensing, it would enhance our ability to carry out forest ecosite classification and inventory over large areas. The focus of this thesis was to determine if LiDAR-derived terrain surfaces could be used to accurately quantify soil moisture in the boreal forest at a study site near Timmins, Ontario for use in ELC systems. Analysis was performed in three parts: (1) ecological land classification was applied to classify the forest plots based on soil texture, moisture regime and dominant vegetation; (2) terrain indices were generated at four different spatial resolutions and evaluated using regression techniques to determine which resolution best estimated soil moisture; and (3) ordination techniques were applied to separate the forest types based on biophysical field measurements of soil moisture and nutrient availability. The results of this research revealed that no single biophysical measurement alone could completely separate forest types; furthermore, the best LiDAR-derived terrain variables explained only 36.5% of the variation in the soil moisture in this study area. These conclusions suggest that species abundance data (i.e., indicator species) should be examined in tandem with biophysical field measurements and LiDAR data to improve classification accuracy.