TECHNICAL PROCESS AND APPLICATION OF WATERLOGGING DISASTER REMOTE SENSING MONITORING IN ZHEJIANG PROVINCE

doi:10.6046/gtzyyg.1995.04.09

Download: PDF(870 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract In this paper, remote sensing monitoring technique and its application for waterlogging disaster in Zhejiang Province is introduced. It indicated that, by means of computer processing and analysing the NOAA-AVHRR data,large spope waterlogging disaster can be monitored, and the scope level and area of waterlogging disaster can be precisely, quickly, objectively estimated. Then, the damage image, numerical chart and damage report are promptlyprovided to leadership or policymaker as scientific basis for fighting and rescuing the disaster.

Keywords Urban expansion Remote sensing Spatio-temporal patterns Differential entropy Driving forces

Issue Date: 02 August 2011

	Service

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

	HU De-Yong
	LI Jing
	CHEN Yun-Hao
	ZHANG Bing
	PENG Guang-Xiong
	LIU Hong-Bo
	ZHOU Wen
	ZHENG Jun
	ZHANG Juan

Cite this article:

HU De-Yong,LI Jing,CHEN Yun-Hao, et al. TECHNICAL PROCESS AND APPLICATION OF WATERLOGGING DISASTER REMOTE SENSING MONITORING IN ZHEJIANG PROVINCE[J]. REMOTE SENSING FOR LAND & RESOURCES, 1995, 7(4): 51-55.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1995.04.09 OR https://www.gtzyyg.com/EN/Y1995/V7/I4/51

[1] 肖乾广等·气象卫星影像用于松花江洪水监测.遥感信息,1987,2(4);27-28

[2] 赁常恭等.用气象卫星信息监测黑龙江省春涝.遥感信息,1989,4(3);4-7

[3] 穆家修·气象卫星实时监测洪涝灾害.遥感信息,1988.3(3);31-32

[4] 巴顿 I J,拜索尔 J M(李红兄译).气象卫星监测洪水.遥感信息,1992,7(2);45-96

[5] 陈渭民等编.卫星气象学.北京:气象出版社,1989

[6] 杨忠恩,骆剑承.NOAA-AVHRR卫星资料处理业务系统.遥感技术与应用,1993,8(4):11-15

[7] 骆剑承,杨忠恩.卫星资料处理业务系统及其图像显示技术.气象,1993,19(8);29-31

[8] 杨忠恩.骆剑承.NOAA-AVHRR卫星资料处理系统及其在生态环境监测中的应用.应用气象学报,1994,5(2);248-252

[9] 杨忠恩等.利用NOAA-AVHRR资料提取水体信息的初步研究.国土资源遥感,1995,(1):31-34

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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.
[13]	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.
[14]	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.
[15]	WU Fang, JIN Dingjian, ZHANG Zonggui, JI Xinyang, LI Tianqi, GAO Yu. A preliminary study on land-sea integrated topographic surveying based on CZMIL bathymetric technique[J]. Remote Sensing for Natural Resources, 2021, 33(4): 173-180.