Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    1993, Vol. 5 Issue (4) : 1-5,27     DOI: 10.6046/gtzyyg.1993.04.01
Applied Research |
ASSESSMENT OF FLOOD AND WATERLOGGING DAMAGE BY TM IMAGE
Dai Changda, Tang Lingli, Chen Gang
China Remote Sensing Satellite Ground Station
Download: PDF(1151 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

In the summer of 1991, the region of Yangtze and Huaihe river suffered a catastrophic flood disaster, TMimages provide rich information of flood scope, surface moisture and vegetation growing, on which crop harvest depends. Through numerical analysis and applied processing of TMimage acquired on July, 14, five days after flood peak, the components humidity(H) Greenness (G)and other now indices were obtained. After a new supervised classification a map with heavy, moderate, light degree of disaster, undamaged region and residential spots was achieved as well as each class area. checking in the field indicated that the result was accurate.

Keywords Surface deformation      Radar interferometry      D-InSAR      Time series     
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
MU Jing-Qin
YAO Guo-Qing
LI Guan-Bao
LIU Bao-Hua
DING Zhong-Jun
Cite this article:   
MU Jing-Qin,YAO Guo-Qing,LI Guan-Bao, et al. ASSESSMENT OF FLOOD AND WATERLOGGING DAMAGE BY TM IMAGE[J]. REMOTE SENSING FOR LAND & RESOURCES, 1993, 5(4): 1-5,27.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.1993.04.01     OR     https://www.gtzyyg.com/EN/Y1993/V5/I4/1


[1] Towshcrd Y. R. etc. Preliminary analysis of landsat-4 Thematic Mapper products. International Journal of Rcmotc sensing, 1983, 4( 4):815~823


[2] 戴昌达等. TM图像的光谱信息特征与最佳波段组合.环境遥感,1989, 4(4)


[3] Colwell, R. N, etc. Manual of Remote Sensing, Second edition. Falls, Church, Va. 1983


[4] Eric P. Crist etc. A Physically-Based Transformation of Thematic Mappcr Data-The TM Tasseled Cap. IEEE Transaction on Gcoscicnce and Remote Sensing, 1984, GE-22( 3):256~263


[5] 王杰生.遥感图像应用处理中的一个分类新算法.环境遥感,1992, 7(2)

[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] SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[3] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[4] LAI Peiyu, HUANG Jing, HAN Xujun, MA Mingguo. An analysis of impacts from water impoundment in Three Gorges Dam Project on surface water in Chongqing area base on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2021, 33(4): 227-234.
[5] YU Bing, TAN Qingxue, LIU Guoxiang, LIU Fuzhen, ZHOU Zhiwei, HE Zhiyong. Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification[J]. Remote Sensing for Natural Resources, 2021, 33(4): 26-33.
[6] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[7] SUN Chao, CHEN Zhenjie, WANG Beibei. Expansion monitoring of construction land based on SAR time series: A case study of Xinbei District, Changzhou[J]. Remote Sensing for Land & Resources, 2020, 32(4): 154-162.
[8] ZHANG Ling, LIU Bin, GE Daqing, GUO Xiaofang. Detecting tiny differential deformation of Tangshan urban active fault using multi-source SAR data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 114-120.
[9] WANG Lingyu, CHEN Quan, WU Yue, ZHOU Zhongfa, DAN Yusheng. Accurate recognition and extraction of karst abandoned land features based on cultivated land parcels and time series NDVI[J]. Remote Sensing for Land & Resources, 2020, 32(3): 23-31.
[10] Biqing WANG, Wenquan HAN, Chi XU. Winter wheat planting area identification and extraction based on image segmentation and NDVI time series curve classification model[J]. Remote Sensing for Land & Resources, 2020, 32(2): 219-225.
[11] Yuting YANG, Hailan CHEN, Jiaqi ZUO. Remote sensing monitoring of impervious surface percentage in Hangzhou during 1990—2017[J]. Remote Sensing for Land & Resources, 2020, 32(2): 241-250.
[12] Guoce SONG, Zhi ZHANG. Remote sensing monitoring method for dust and wind accumulation in multi-metal mining area of Xin Barag Right Banner,Inner Mongolia[J]. Remote Sensing for Land & Resources, 2020, 32(2): 46-53.
[13] Shuang ZHU, Jinshui ZHANG. Medium resolution remote sensing based winter wheat mapping corrected by low-resolution time series remote sensing images[J]. Remote Sensing for Land & Resources, 2020, 32(1): 19-26.
[14] Jiaqi ZUO, Zegen WANG, Jinhu BIAN, Ainong LI, Guangbin LEI, Zhengjian ZHANG. A review of research on remote sensing for ground impervious surface percentage retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(3): 20-28.
[15] Xifeng CAO, Lin SUN, Zifei ZHAO, Xiaofeng HAN, Mingjie YAN. Application of MODIS remote sensing products in the estimation of grass yield in Sanjiang Source Area[J]. Remote Sensing for Land & Resources, 2018, 30(4): 115-124.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech