TY - JOUR
T1 - A Feasibility Study of Thermal Infrared Imaging for Monitoring Natural Terrain—A Case Study in Hong Kong
AU - Chiu, Lydia Sin Yau
AU - Lai, Wallace Wai Lok
AU - Santos-Assunção, Sónia
AU - Sandhu, Sahib Singh
AU - Sham, Janet Fung Chu
AU - Chan, Nelson Fat Sang
AU - Wong, Jeffrey Chun Fai
AU - Leung, Wai Kin
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/12
Y1 - 2023/12
N2 - The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey via a UAV equipped with a thermal camera are limited. Given the unique setting of Hong Kong, which has a high population living in largely hilly terrain with little natural flat land, steep man-made slopes and natural hillsides have caused significant geotechnical problems which pose hazards to life and facilities. This paper presents the adoption of a time-lapse IRT survey using a UAV in such challenging geotechnical conditions. Snapshot and time-lapse IRT studies of a selected site in Hong Kong, where landslides had occurred were carried out, and visual inspection, photogrammetry, and IRT techniques were also conducted. 3D terrain models of the selected sites were created by using data collected from the photogrammetry and single (snapshot) and continuous monitoring (time-lapse) infrared imaging methods applied in this study. The results have successfully identified various thermal infrared signatures attributed to the existence of moisture patches, seepage, cracks/discontinuities, vegetation, and man-made structures. Open cracks/discontinuities, moisture, vegetation, and rock surfaces with staining can be identified in snapshot thermal image, while the gradient of temperature decay plotted in ln(T) vs. ln(t) enables quantifiable identifications of the above materials via time-lapse thermography and analysis.
AB - The use of infrared thermography (IRT) technique combining other remoting sensing techniques such as photogrammetry and unmanned aerial vehicle (UAV) platforms to perform geotechnical studies has been attempted by several previous researchers and encouraging results were obtained. However, studies using time-lapse IRT survey via a UAV equipped with a thermal camera are limited. Given the unique setting of Hong Kong, which has a high population living in largely hilly terrain with little natural flat land, steep man-made slopes and natural hillsides have caused significant geotechnical problems which pose hazards to life and facilities. This paper presents the adoption of a time-lapse IRT survey using a UAV in such challenging geotechnical conditions. Snapshot and time-lapse IRT studies of a selected site in Hong Kong, where landslides had occurred were carried out, and visual inspection, photogrammetry, and IRT techniques were also conducted. 3D terrain models of the selected sites were created by using data collected from the photogrammetry and single (snapshot) and continuous monitoring (time-lapse) infrared imaging methods applied in this study. The results have successfully identified various thermal infrared signatures attributed to the existence of moisture patches, seepage, cracks/discontinuities, vegetation, and man-made structures. Open cracks/discontinuities, moisture, vegetation, and rock surfaces with staining can be identified in snapshot thermal image, while the gradient of temperature decay plotted in ln(T) vs. ln(t) enables quantifiable identifications of the above materials via time-lapse thermography and analysis.
KW - feature classification
KW - geotechnical studies
KW - thermal decay
KW - time-lapse infrared thermography
KW - unmanned aerial vehicle (UAV)
UR - http://www.scopus.com/inward/record.url?scp=85180617918&partnerID=8YFLogxK
U2 - 10.3390/rs15245787
DO - 10.3390/rs15245787
M3 - Journal article
AN - SCOPUS:85180617918
SN - 2072-4292
VL - 15
JO - Remote Sensing
JF - Remote Sensing
IS - 24
M1 - 5787
ER -