Quantification of Land Cover Surrounding Planned Disturbances Using UAS Imagery

Three prescribed burn sites and seven selective timber harvest sites were surveyed using a UAS equipped with a PPK-triggered RGB sensor to determine optimal image collection parameters surrounding each type of disturbance and land cover. The image coordinates were corrected with a third-party base station network (CORS) after the flight, and photogrammetrically processed to produce high-resolution georeferenced orthomosaics. This addressed the first objective of this study, which was to establish effective data procurement methods from both before and after planned disturbances.

Orthomosaic datasets surrounding both a prescribed burn and a selective timber harvest, were used to classify land covers through geographic image-based analysis (GEOBIA). The orthomosaic datasets were segmented into image objects, before classification with a machine-learning algorithm. Land covers for the prescribed prairie burn were 1) bare ground, 2) litter, 3) green vegetation, and 4) burned vegetation. Land covers for the selective timber harvest were 1) mature canopy, 2) understory vegetation, and 3) bare ground. 65 samples per class were collected for prairie burn datasets, and 80 samples per class were collected for timber harvest datasets to train the classifier. A supported vector machines (SVM) algorithm was used to produce four land cover classifications for each site surrounding their respective planned disturbance. Pixel counts for each class were multiplied by the ground sampled distance (GSD) to obtain area calculations for land covers. Accuracy assessments were conducted by projecting 250 equalized stratified random (ESR) reference points onto the georeferenced orthomosaic datasets to compare the classification to the imagery through visual interpretation. This addressed the second objective of this study, which was to establish effective data classification methods from both before and after planned disturbances.

Finally, a two-tailed t-Test was conducted with the overall accuracies for each disturbance type and land cover. Results showed no significant difference in the overall accuracy between land covers. This was done to address the third objective of this study which was to determine if a significant difference exists between the classification accuracies between planned disturbance types. Overall, effective data procurement and classification parameters were established for both before and after two common types of planned disturbances within the CHF region, with slightly better results for prescribed burns than for selective timber harvests.