The aim of this study is to detect flooded locations using SAR data and assess post-flooded conditions using a Geographic Information System (GIS) and SAR (Synthetic Aperture Radar) data images. The temporal characteristics of radar response from flooding were analyzed throughout the 2002 summer flood season. Flooded locations were identified through a change detection technique of RADARSAT SAR data images. Multiple Radarsat SAR images were acquired before, during, and after the flood inundation. From the interpretation of colour composite imagery of the multi-temporal SAR data, as well as from the temporal profiles of radar backscatter, seven types of landcover could be classified according to flooded and post-flooded recovery conditions. Landcover map of 2000.07 was divided into 7 categories: water, urban, bare ground, marsh, grassland, forest and farming. From the images, it was determined that the farming area showed flooding in 14.52km2, the forest flooded area was 3.50km2, the grassland flooded area was 1.06km2, the ground flooded area accounted for 0.09km2 and the urban flooded area was 0.04km2. The actual flooded damage to the standing farming crop depends on the duration of the flood and on the subsequent recovery status. We found that image data acquired during and after a flood is needed to assess accurately flood damage to a farming area. In this study result are proved in the scientific basis for flood damage. The findings of this study will contribute to reducing the hazards of natural disasters and will increase the flexibility in the process of managing damage caused by natural disasters.
[1]
Guy J P Schumanna, Jeffrey C Neala, David C Masonb, Paul D Batesa (2011) The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sensing of Environment 115(10): 2536-2546
[2]
K S LEE, S I LEE (2003) Assessment of post-flooded conditions of rice fields with multi-temporal satellite SAR data. International Journal of Remote Sensing 24:17, 3457-3465
[3]
Pulvirentia, M Chinib, N Pierdiccaa, L Guerrieroc, P Ferrazzolic (2010) Flood monitoring using multi-temporal COSMO-SkyMed data: Image segmentation and signature interpretation. Remote Sensing of Environment 115(4): 990-1002
[4]
R Hostachea, P Matgena, W Wagnerb (2012) Change detection approaches for flood extent mapping: How to select the most adequate reference image from online archives?. International Journal of Applied Earth Observation and Geoinformation 19: 205-213
[5]
L Giustarinia, H Vernieuweb, J Verwaerenb, M Chinia, R Hostachea, P Matgena (2015) Accounting for image uncertainty in SAR-based flood mapping. International Journal of Applied Earth Observation and Geoinformation 34: 70-77
[6]
Stefan Schlaffera, Patrick Matgenb, Markus Hollausa, Wolfgang Wagnera (2015) Flood detection from multi-temporal SAR data using harmonic analysis and change detection. International Journal of Applied Earth Observation and Geoinformation 38: 15-24
[7]
Guy J-P Schumanna, Jeffrey C Neala, David C Masonb, Paul D Batesa (2011) The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods. Remote Sensing of Environment 115(10): 2536-2546
[8]
Santiago Zazo, José-Luis Molina, Pablo Rodríguez-Gonzálvez (2015) Analysis of flood modeling through innovative geomatic methods. Journal of Hydrology 524: 522-537
[9]
Anthea L. Mitchella, Ian Tapleyb, Anthony K. Milnea, Mark L. Williamsc, Zheng-Shu Zhoud, Eric Lehmannd, Peter Caccettad, Kim Lowelle, Alex Heldf (2014) C- and L-band SAR interoperability: Filling the gaps in continuous forest cover mapping in Tasmania. Remote Sensing of Environment 155: 58-68
[10]
Mahyat Shafapour Tehrany, Biswajeet Pradhan, Shattri Mansor, Noordin Ahmad (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125: 91-101
