<p>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 <i>establish effective data
procurement methods from both before and after planned </i>disturbances. <br></p><p>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 <i>establish effective
data classification methods from both before and after planned </i>disturbances.<br></p><p>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 <i>determine
if a significant difference exists between the classification accuracies
between planned disturbance types</i>. Overall, effective data procurement and
classification parameters were established for both <i>before </i>and <i>after </i>two
common types of <i>planned </i>disturbances
within the CHF region, with slightly better results for prescribed burns than
for selective timber harvests.<br></p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17152946 |
| Date | 19 December 2021 |
| Creators | Zachary M Miller (11819132) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Quantification_of_Land_Cover_Surrounding_Planned_Disturbances_Using_UAS_Imagery/17152946 |
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