USING REMOTE SENSING TECHNIQUE TO THE SURVEING OF ENGINEERING GEOLOGY OF HIGH-GRAD HIGHWAY

doi:10.6046/gtzyyg.1996.01.05

Download: PDF(247 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

This paper introduces the majoy contents, properties and superiorities of remote sensing technique used in survey of engineering geology of high-grad highway.

Keywords Water depth Remote sensing Correlation analysis Model

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	TIAN Shu-Fang
	HONG You-Tang
	QIN Xu-Wen
	SONG Xin-Hua
	YIN Bing-Xi
	YAN Hong
	YAN Shu-Qiang

Cite this article:

TIAN Shu-Fang,HONG You-Tang,QIN Xu-Wen, et al. USING REMOTE SENSING TECHNIQUE TO THE SURVEING OF ENGINEERING GEOLOGY OF HIGH-GRAD HIGHWAY[J]. REMOTE SENSING FOR LAND & RESOURCES, 1996, 8(1): 26-28，35.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1996.01.05 OR https://www.gtzyyg.com/EN/Y1996/V8/I1/26

[1]	LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[2]	WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[3]	LYU Pin, XIONG Liyuan, XU Zhengqiang, ZHOU Xuecheng. FME-based method for attribute consistency checking of vector data of mines obtained from remote sensing monitoring[J]. Remote Sensing for Natural Resources, 2022, 34(1): 293-298.
[4]	ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[5]	XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6]	SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[7]	LI Weiguang, HOU Meiting. A review of reconstruction methods for remote-sensing-based time series data of vegetation and some examples[J]. Remote Sensing for Natural Resources, 2022, 34(1): 1-9.
[8]	DING Bo, LI Wei, HU Ke. Inversion of total suspended matter concentration in Maowei Sea and its estuary, Southwest China using contemporaneous optical data and GF SAR data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 10-17.
[9]	GAO Qi, WANG Yuzhen, FENG Chunhui, MA Ziqiang, LIU Weiyang, PENG Jie, JI Yanzhen. Remote sensing inversion of desert soil moisture based on improved spectral indices[J]. Remote Sensing for Natural Resources, 2022, 34(1): 142-150.
[10]	ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[11]	WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[12]	HE Peng, TONG Liqiang, GUO Zhaocheng, TU Jienan, WANG Genhou. A study on hidden risks of glacial lake outburst floods based on relief amplitude: A case study of eastern Shishapangma[J]. Remote Sensing for Natural Resources, 2022, 34(1): 257-264.
[13]	AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.
[14]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[15]	LIU Bailu, GUAN Lei. An improved method for thermal stress detection of coral bleaching in the South China Sea[J]. Remote Sensing for Natural Resources, 2021, 33(4): 136-142.