TY - JOUR
T1 - Urban forest topographical mapping using UAV LIDAR
AU - Shidiq, Iqbal Putut Ash
AU - Wibowo, Adi
AU - Kusratmoko, Eko
AU - Indratmoko, Satria
AU - Ardhianto, Ronni
AU - Prasetyo Nugroho, Budi
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2017/12/20
Y1 - 2017/12/20
N2 - Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.
AB - Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.
KW - DEM
KW - LIDAR
KW - drone
KW - topography
KW - vegetation
UR - http://www.scopus.com/inward/record.url?scp=85039453402&partnerID=8YFLogxK
U2 - 10.1088/1755-1315/98/1/012034
DO - 10.1088/1755-1315/98/1/012034
M3 - Conference article
AN - SCOPUS:85039453402
SN - 1755-1307
VL - 98
JO - IOP Conference Series: Earth and Environmental Science
JF - IOP Conference Series: Earth and Environmental Science
IS - 1
M1 - 012034
T2 - 5th Geoinformation Science Symposium 2017, GSS 2017
Y2 - 27 September 2017 through 28 September 2017
ER -