A COMBINED STUDY OF FLOOD PROPAGATION MAPPING, USING SENTINEL-1 AND LANDSAT-8 DATA. A CASE STUDY FROM RIVER EVROS, GREECE


Publiée : Ιουλ 27, 2016
A. Kyriou
K. Nikolakopoulos
Résumé

Floods are suddenly and temporary natural disasters, which influence equally important the society and the natural environment, affecting areas that are not normally covered by water. In the context of flood mapping, different remote sensing techniques may contribute in a sufficient and effective way. This paper deals with mapping the spread of water bodies from natural levees of the river Evros and therefore the flood event on the surrounding areas of the river. In this work, radar data from Sentinel-1 mission as well as optical data from Landsat-8 were utilized. Specifically, Sentinel-1 data before flood events were treated with respectively during flood, yielding an image which reflects the propagation of the event. Moreover, Landsat-8 data were acquired with the aim of identifying and mapping of flooded areas, utilizing the Normalized Difference Water Index calculation. The results of the two methods were compared and flooded areas were evaluated. 

Article Details
  • Rubrique
  • Remote Sensing and GIS
Téléchargements
Les données relatives au téléchargement ne sont pas encore disponibles.
Références
Du, Z., Li, W., Zhou, D., Tian, L. and Ling, F., 2014. Analysis of Landsat-8 OLI imagery for land
surface water mapping, Remote Sensing Letters, 5(7), 37-41.
Gao, B.C., 1996. NDWI - A normalized difference water index for remote sensing of vegetation
liquid water from space, Remote Sensing of Environment, 58, 257-266.
Huong, D. and Ryota, N., 2014, Potential flood hazard assessment by integration of ALOS PALSAR
and ASTER GDEM: a case study for the Hoa Chau commune, Hoa Vang district, in central
Vietnam, Journal of Applied Remote Sensing, 8, 083626-1 - 083626-12.
Ireland, G., Volpi, M. and Petropoulos, G., 2015.Examining the Capability of Supervised Machine
Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study
from a Mediterranean Flood, Remote Sensing, 7, 3372-3399.
Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P. and Hunt,
E.R.,2004. Vegetation water content mapping using Landsat data derived normalized
difference water index for corn and soybeans, Remote Sensing of Environment, 92, 475-482.
Ji, L., Zhang, L. and Wylie, B., 2009. Analysis of Dynamic Thresholds for the Normalized
Difference Water Index, Photogrammetric Engineering & Remote Sensing, 75(11), 1307-
Li, W., Du, Z., Ling, F., Zhou, D., Wang, H., Gui, Y., Sun, B. and Zhang, X., 2013. A comparison
of land surface water mapping using the normalized difference water index from TM, ETM+
and ALI, Remote Sensing, 5, 5530-5549.
Maggi, M., Brivio, P., Colombo, R. and Tomasoni, R., 1998, Flooded areas estimation using radar
images and digital elevation model, Proceedings of the EUROPTO Conference on Remote
Sensing for Geology, Land Management, and Cultural Heritage Ill, Barcelona, Spain
September 1998, SPIE, 3496, 46-53.
Nascimento, B., Schiavo, D.V., Ruza, M.S., Maria, Â., Hentz, K., Schikowski, A.B. and Dalla, A.P.,
Pluviometric Influence in the Indexes of NDVI and NDWI Vegetation for the
Municipality of Guarapuava-PR , South Region of Brazil, Australian Journal of Basic and
Applied Sciences, 9, 358-363.
Pierdicca, N., Chinib, M., Pulvirentia, L., Marzanoa, F. and Moria, S., 2012, Proceedings of SAR
Image Analysis, Modeling and Techniques XII, SPIE, 8536, 85360W1- 85360W11.
Pulvirenti, L., Pierdicca, N., Chini, M. and Guerriero, L., 2011. An algorithm for operational flood
mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic, Natural Hazards and
Earth System Sciences, 11, 529-540.
Pulvirenti, L., Pierdicca, N., Chinib, M. and Guerriero L., 2014, Combined use of COSMO-SkyMed
derived products and hydrodynamic models to produce physically-based maps of flood
extent, Proceedings of SAR Image Analysis, Modeling and Techniques XII, SPIE, 924,
C1- 92431C10.
Rokni, K., Ahmad, A., Selamat, A. and Hazini, S., 2014. Water feature extraction and change
detection using multitemporal landsat imagery, Remote Sensing, 6, 4173-4189.
Sahu, A., 2014. Identification and mapping of the water-logged areas in Purba Medinipur part of
Keleghai river basin, India: RS and GIS methods, International Journal of Advance
Geoscience, 2(2), 59-65.
Schumann, G., Di Baldassarre, G. and Bates, P.D., 2009. The Utility of Spaceborne Radar to Render
Flood Inundation Maps Based on Multialgorithm Ensembles, Geoscience and Remote
Sensing, 47(8), 2801-2807.
Schumann, G., Hostache, R., Puech, C., Hoffmann, L., Matgen, P., Pappenberger, F. and Pfister, L.,
High-Resolution 3-D Flood Information from Radar Imagery for Flood Hazard
Management, Geoscience and Remote Sensing, 45(6), 1715-1725.
Townsend, P.A., 2001. Mapping seasonal flooding in forested wetlands using multi-temporal
Radarsat SAR, Photogrammetric Engineering & Remote Sensing, 67, 857-864.
Westerhoff, R.S., Kleuskens, M.P.H., Winsemius, H.C., Huizinga, H.J., Brakenridge, G.R. and
Bishop, C., 2013. Automated global water mapping based on wide-swath orbital syntheticaperture
radar, Hydrology and Earth System Sciences, 17, 651-663.
Xu, H., 2006. Modification of normalised difference water index (NDWI) to enhance open water
features in remotely sensed imagery, International Journal of Remote Sensing, 27, 3025-
Articles les plus lus par le même auteur ou la même autrice