To improve the accuracy of identifying geothermal
anomaly areas by thermal infrared remote sensing, a plateau railway corridor is
selected as the research area. The daytime surface temperature is retrieved
using the singlewindow algorithm based on Landsat8 data, and the nighttime surface
temperature is obtained by ECO2LSTE.
Next, the altitude, slope, aspect, cumulative solar radiation, NDVI,
NDSI, NDWI, and albedo are selected as input factors using the random forest
method, and the surface temperature without the solar radiation effect is
obtained. Finally, the surface temperature after removing the solar radiation
effect, fault density, buffer distance to river, and magnetic anomaly are
selected as index factors. The geothermal anomaly area is quantitatively
identified using the certainty factor model, and the results are evaluated
using the known hot spring points. The results reveal the following: (1) The
random forest method can effectively remove the temperature changes caused by
nongeothermal factors, reduce the effect of solar
radiation, and improve the accuracy of identifying geothermal anomalies. (2)
The evaluation results of geothermal anomaly areas are consistent with the
distribution of known hot spring points, and the identification results of high and mediumanomaly areas are reliable. (3)
The plateau railway corridor mainly passes through seven geothermal anomaly
areas, and the lengths of the line passing through the high and mediumanomaly areas are 72 km and 125
km, respectively. LudingKangding, Litang basin and its
surrounding areas, Batang, Gongjue, ChangduChaya,
Basu, and BomiLulang are the sections that need to
focus on preventing high temperature and heat damage. The research results have
preliminarily realized the quantitative and refined identification of
geothermal anomalies in the plateau railway corridor.
BIAN Min
,
DONG Qing
,
WANG Dong
,
MENG Deli
,
ZHAO Wenbo
,
ZHOU Houyu
. Identification of Geothermal Anomalies in a Plateau Railway
Corridor Based on Solar Radiation Effect Correction[J]. Tunnel Construction, 2022
, 42(8)
: 1461
-1472
.
DOI: 10.3973/j.issn.2096-4498.2022.08.016