Table of Contents
Remote Sens., Volume 9, Issue 11 (November 2017)
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Cover Story This paper was written as part of a PhD project aiming to improve greenhouse gas emissions [...] Read more. This paper was written as part of a PhD project aiming to improve greenhouse gas emissions estimates from peatland fires in Indonesia. While measuring peatland depth of burn for a previous paper, it was noted that digital terrain models (DTMs) produced from LiDAR were showing unexpected values before the fire burned the vegetation. The effects of vegetation structure were tested on LiDAR-derived DTM accuracy in a UK forest during winter when there are no leaves to block ground survey equipment (i.e., similar to post-burn forests). Over 650 ground control points were used to create a reference DTM to compare two LiDAR-derived DTMs in leaf-on and leaf-off conditions. The LiDAR point cloud was used to characterise the overlying vegetation structure, revealing that leaf-on vegetation and, in particular, dense ground-cover vegetation causes the greatest DTM errors. View the paper