assessing remote sensing estimations for burn area and tree mortality
abstract
remote sensing tools will increase the ability of land managers to visually
sample large areas more feasibly. this increase in applications of remote
sensing such as uav aerial lidar may require an assessment of algorithm
accuracy while utilizing lidar data versus ground collected data to ensure
these applications are appropriate. one such application included within this
study is the detection of trees utilizing the lidr package which allows a cost-
effective and quick survey estimating trees contained, and providing their
estimated heights. the aim of this paper is to compare these detection results to
a traditional ground tree stocking survey, exploring the viability of applying tree
detection algorithms on post-burn forestry blocks to assess the surviving trees
allowing an indication of future stocking allowing the forest manager to create a
more accurate re-planting schedule. the results derived from this assessment
deviated significantly from ground surveys with the aerial analysis providing an
estimate of 2.20 wsp/ha and the ground survey estimating 70.18 wsp/ha (well
spaced stems per hectare) within block 525_19c. although stocking results
were inconclusive the analysis resulted in several useful outputs such as a
combination of orthomosaic imagery alongside the tree detection points. these outputs resulted in an effective visual aid allowing a more detailed visualization
of the spatial extent severe burns included within the forested blocks.
collections
- undergraduate theses [325]