![]() However, sometimes it is difficult to utilize all the features efficiently. Multi-feature, especially multi-temporal, remote-sensing data have the potential to improve land cover classification accuracy. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them. PDF is the official format for papers published in both, html and pdf forms.You may sign up for e-mail alerts to receive table of contents of newly released issues.Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.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. 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 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). 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. This paper was written as part of a PhD project aiming to improve greenhouse gas emissions estimates from peatland fires in Indonesia. ![]()
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