Abstract
Abstract. Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.
Highlights
Light Detection and Ranging (LiDAR) is an active remote sensing technique that able to map various activities of the Earth’s surface and features such as vegetation and building, which provides Digital Terrain Model (DTM) with up to sub-meter vertical accuracy (Bater, 2009) and increasingly being used to map forested terrain (Reutebuch et al, 2003)
Most of previous research found that the error of LiDAR-derived DTM is highly contributed by terrain slope (Hodgson and Bresnahan, 2004; Hodgson et al, 2003; Spaete et al, 2011)
This study demonstrates the airborne LiDAR data in generating DTM over tropical forest area
Summary
Light Detection and Ranging (LiDAR) is an active remote sensing technique that able to map various activities of the Earth’s surface and features such as vegetation and building, which provides Digital Terrain Model (DTM) with up to sub-meter vertical accuracy (Bater, 2009) and increasingly being used to map forested terrain (Reutebuch et al, 2003). Dozens of filtering algorithm have been developed in separation of ground points, the difficulties in this extraction still exist where most of the algorithm need specific condition to produce good results (Meng et al, 2010) such as flat and open areas, Due to this scenario, more studies should be conducted in order to providing the understanding of the limitations of DTM accuracy relative to various intended end uses, and may provide direction in improving the DTM development (Su and Bork, 2006). But the terrain slope causing the classification of sample (a) as ground points because sample (b) as ground return is at a higher altitude (Lewis and Hancock, 2007) This situation is challenging in filtering the LiDAR data for major filtering algorithms.
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