King's College London

TY - JOUR

T1 - Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

AU - Simpson, Jake E.

AU - Smith, Thomas E. L.

AU - Wooster, Martin J.

PY - 2017/10/28

Y1 - 2017/10/28

N2 - Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs

AB - Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs

U2 - 10.3390/rs9111101

DO - 10.3390/rs9111101

M3 - Article

VL - 9

JO - REMOTE SENSING

JF - REMOTE SENSING

SN - 2072-4292

IS - 11

ER -