Modeling, Mapping and Analysis of Floods Using Optical, Lidar and SAR Datasets-a Review

被引:1
作者
Kubendiran, I. [1 ]
Ramaiah, M. [1 ]
机构
[1] Vellore Inst Technol, Sch Comp Sci Engn & Informat Syst, Vellore 632014, India
关键词
remote sensing; floods; disaster management; flood forecasting; models; SAR; DIFFERENCE WATER INDEX; SURFACE-WATER; MORPHOMETRIC-ANALYSIS; SPECTRAL INDEXES; EXTRACTION; IMAGES; RISK; CLASSIFICATION; AREAS; NDWI;
D O I
10.1134/S0097807823600614
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Occurrence of natural disaster can never be prevented, and floods are one among the major natural disaster that affects the human life and economy of the country. Considering the global loss due to floods, various government and non-governmental organisations are focusing on minimising the losses and provide emergency measures during floods. Adopting the recent technologies integrating various datasets will assist in providing response strategies before and after disaster. Flood are mostly based on the climatic conditions and environmental factors and the present review focuses on the reviewing the various remote sensing methodologies that are used in mapping and analysing floods. The review carried out examines various remote sensing methodologies adopting multispectral, light detection and ranging and radar datasets for mapping and predicting floods. The review identified the limitations in flood prediction, risk assessment and hazard analysis and suggests a framework that can be adopted for effective mapping of flood extent and in suggesting the regions that the rescue team should focus during a disaster event.
引用
收藏
页码:438 / 448
页数:11
相关论文
共 85 条
[1]   Household vulnerability and resilience in flood hazards from disaster-prone areas of Punjab, Pakistan [J].
Ahmad, Dilshad ;
Afzal, Muhammad .
NATURAL HAZARDS, 2019, 99 (01) :337-354
[2]   Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data [J].
Anusha, N. ;
Bharathi, B. .
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES, 2020, 23 (02) :207-219
[3]  
Ashraf Muhammad Aqeel, 2011, Biomass and Remote Sensing of Biomass, P129
[4]   Rule-based classification of high-resolution imagery over urban areas in New York City [J].
Bhaskaran, Sunil ;
Nez, Eric ;
Jimenez, Karolyn ;
Bhatia, Sanjiv K. .
GEOCARTO INTERNATIONAL, 2013, 28 (06) :527-545
[5]   Morphometric analysis to determine floods in the Upper Krishna basin using Cartosat DEM [J].
Bhatt, Surabhi ;
Ahmed, S. A. .
GEOCARTO INTERNATIONAL, 2014, 29 (08) :878-894
[6]   Supervised feature-based classification of multi-channel SAR images [J].
Borghys, D ;
Yvinec, Y ;
Perneela, C ;
Pizurica, A ;
Philips, W .
PATTERN RECOGNITION LETTERS, 2006, 27 (04) :252-258
[7]   Normalized Difference Flood Index for rapid flood mapping: Taking advantage of EO big data [J].
Cian, Fabio ;
Marconcini, Mattia ;
Ceccato, Pietro .
REMOTE SENSING OF ENVIRONMENT, 2018, 209 :712-730
[8]  
Csornai G., 2005, P 2004 ENVISAT ERS S
[9]  
Davies R., China-Thousands Evacuate Floods in Shanxi
[10]   Coastline extraction from SAR images and a method for the evaluation of the coastline precision [J].
Dellepiane, S ;
De Laurentiis, R ;
Giordano, F .
PATTERN RECOGNITION LETTERS, 2004, 25 (13) :1461-1470