Paddy Damage Assessment due to Cyclonic Storm using Remotely Sensed Data By ABHIJAT ARUN ABHYANKAR October 4, 2010

Scheme of Presentation  Overview of the problem  Literature review  Case study: Orissa Super cyclone  Approaches for Damage assessment using R. S. Data  Results and discussion  Concluding remarks

Overview of the Problem Indian Coastline (around 7500 kms) No. of Cyclones crossing Indian Coastline per year-8 to 9 (Abhyankar et al., 2004) State with maximum no. of Cyclone crossing-Orissa state (Abhyankar et al., 2004) Severe cyclonic event leads to loss of human life and property, Inundation of low areas, health issues, crop damage and loss of fertility, drinking water pollution, beach erosion No standard tool available to assess damage to paddy or crops Problem statement/objective Rapidly and quantitatively identify affected areas due to cyclone using remotely sensed data Usefulness of the present study Improve damage assessment, expediting the relief funds

Literature Review AuthorYearStudy area and satellite Results and Discussion Ribbes, F., et al.1999Indonesia on Java Island, Jatisari Village Radarsat-1 SAR At the beginning of the cycle, flooded rice fields have low backscatter range (-14 to -12 dB). At the end of reproductive phase the backscatter reaches -6 dB and remain stable The paper attempt to retrieve date of transplantation based on inverse algorithm which relates plant height and corresponding backscatter coefficient values Inoue, Y. et al.2002Experimental paddy field, NIAES, Japan C Band HH polarization, incidence angle of 35°, backscattering value for 20 day transplanted paddy was dB Chakraborty, M. et al. 2005West Bengal, India Radarsat-1 SAR S1 beam the backscatter value for 5 day transplanted rice was -13 dB S7 beam for 5 days old transplanted rice was from -18 to -13 dB Water backscattering value in S1 mode was -14 dB, S7 mode -20 dB. Choudhury, I. et al. 2007Barhaman district of West Bengal Radarsat-1 SAR and Envisat ASAR 20 day transplanted rice the predicted backscattering values for rice dB

Approaches 1.Deterministic 1. Change in dB 1. Histogram approach 1. Deterministic1. Supervised classification 2. Probabilistic a) IRS as a base with SGT 2. Probabilistic with Max. likelihood 3.Minimum b) Field data 2. Tool for complete/ 3. Discriminant Classifier Difference and SAR partial/non 2. ANN in Area imagery submerged rice 4.Spatial 2. Histogram Correlation approach Coefficient a) IRS as a base b) field data and SAR imagery (A)(B)(C) Non Threshold Determining water threshold in pre event SAR using Pre event IRS and Pre event Radar Assessing changes in backscattering values of Landcover of Interest Threshold Utilizing Pre event SAR and Field data as baseline information QualitativeQuantitative

Standard FCC of IRS 1D LISS III for October 11, 1999 Landover map of Kendrapara district using IRS 1D LISS III image of October 11, 1999 LandcoverArea (thousand hectares) Water Forest9.877 Fallow land Other vegetation Rice Area of Kendrapara in thousand hectares=255.02

Framework for Identification of completely submerged Landcovers from Cyclone Disasters using Remotely sensed Data Damage Monitoring Tool Landcover classification Delineation of submerged areas by setting threshold to classify water/non-water in SAR Overlay Base map (masking of all landcovers other than landcover of interest) Procurement of cloud free IRS 1D LISS III ImageProcure pre and post event Radarsat images Pre processing (speckle noise removal and Incidence angle correction) Completely submerged landcover of interest i.e. paddy

Case Study-Orissa Super cyclone Pre event : IRS 1D LISS III image of October 11, 1999 Radarsat-1 SAR images of October 11, 1999 Post event : Radarsat-1 SAR images of November 2, 1999 and November 4, 1999

October 11, 1999 November 2, 1999 November 4, 1999

Multi-date georeferenced FCC dataset

Total 666 pixels 486 pixels for estimation 120 pixels-known water and 366 pixels are of known non water pixels Remaining 180 pixels for validation,. Water 45 pixels and Non water 135

Water in Radarsat-1 SAR image of October 11, 1999 Completely submerged areas under water on November 2, 1999 Completely submerged areas under water on November 4, 1999

Known Classified WaterNon water Water1182 Non water10356 Known Classified WaterNon water Water3510 Non water0135 Confusion matrix for estimation set of October 11, 1999 Overall accuracy=474/486=97.5% Confusion matrix for validation set of October 11, 1999 Overall accuracy=170/180=94.4%

Result Paddy completely submerged on November 4, 1999 due to Orissa super cyclone ANN Supervised Classification with Maximum Likelihood Discriminant Probabilistic Deterministic Approach C dBSpatial Correlation Coefficient dBMinimum Difference in Area dBProbabilistic dBDeterministic Approach A Paddy crop completely submerged (in thousand hectares) Threshold for Water Method

Concluding Remarks The remote sensing as a tool can play an important role in damage assessment and relief/enumeration operations Microwave data of Radarsat-1 SAR HH polarized data and Envisat ASAR VV polarized data find application in the submergence analysis The Remote sensing data results obtained are quantitative in nature Water subclasses namely, sea, pond and river have significantly different backscattering responses in SAR. Methods developed in the present study using Remote sensing data would find application in disaster monitoring and management The developed methods using Remote Sensing data can be used at different management levels for decision making The developed methods could be applicable to other similar kind of disaster namely floods, heavy rainfall, tsunami etc. The developed methods can play a key role in decision making of national policy on disaster management.

Decision making tool for various management strategy with recommended models HighAffected Land Parcels Maximum Likelihood, ANN, Discriminant Operational Management level i.e. District/sub district MediumAffected District ProbabilisticTactical Management (planning level) i.e. State LowAffected States DeterministicStrategic Management (policy level) i.e. Central AccuracyOutputMethods recommended Level of Management

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