Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)

Invasive nonindigenous plants are threatening the biological integrity of North American rangelands, as well as the economies that are supported by those ecosystems. Spatial information is critical to fulfilling invasive plant management strategies. Traditional invasive plant mapping has utilized ground-based hand or GPS mapping. The shortfalls of ground-based methods include the limited spatial extent covered and the associated time and cost. Mapping vegetation with remote sensing covers large spatial areas and maps can be updated at an interval determined by management needs. The objective of the study was to map leafy spurge (Euphorbia esula L.) and spotted knapweed (Centaurea maculosa Lam.) using 128-band hyperspectral (5-m and 3-m resolution) imagery and assess the accuracy of the resulting maps. Beiman Cutler classifications (BCC) were used to classify the imagery using the randomForest package in the R statistical program. BCC builds multiple classification trees by repeatedly taking random subsets of the observational data and using random subsets of the spectral bands to determine each split in the classification trees. The resulting classification trees vote on the correct classification. Overall accuracy was 84% for the spotted knapweed classification, with class accuracies ranging from 60% to 93%; overall accuracy was 86% for the leafy spurge classification, with class accuracies ranging from 66% to 93%. Our results indicate that (1) BCC can achieve substantial improvements in accuracy over single classification trees with these data and (2) it might be unnecessary to have separate accuracy assessment data when using BCC, as the algorithm provides a reliable internal estimate of accuracy.