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 ProblemIndian 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 toloss 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/objectiveRapidly and quantitatively identify affected areas due to cyclone using remotelysensed data

Usefulness of the present studyImprove damage assessment, expediting the relief funds

Literature ReviewAuthor Year Study area and

satelliteResults and Discussion

Ribbes, F., et al. 1999 Indonesia 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. 2002 Experimental paddy field, NIAES, Japan

C Band HH polarization, incidence angle of 35°, backscattering value for 20 day transplanted paddy was -18.0 dB

Chakraborty, M. et al.

2005 West 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.

2007 Barhaman district of West Bengal

Radarsat-1 SAR and Envisat ASAR

20 day transplanted rice the predicted backscattering values for rice -13.0 dB

Approaches

1.Deterministic 1. Change in dB 1. Histogram approach 1. Deterministic1. Supervised classification2. Probabilistic a) IRS as a base with SGT 2. Probabilistic with Max. likelihood

3.Minimum b) Field data 2. Tool for complete/ 3. Discriminant ClassifierDifference and SAR partial/non 2. ANN in Area imagery submerged rice 4.Spatial 2. HistogramCorrelation 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

Qualitative Quantitative

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

Landcover Area (thousand hectares)

Water 21.286

Forest 9.877

Fallow land 49.776

Other vegetation 41.295

Rice 132.786

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 Image Procure pre and post event Radarsat images

Pre processing (speckle noise removal and Incidence angle correction)

Completely submerged landcover of interest i.e. paddy

Water pixels of October 11, 1999 Radarsat-1 SAR corresponding to October 11, 1999 IRS water

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-30 -25 -20 -15 -10 -5 0

dB

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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

Plot of known water and non water backscattering values

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-30 -25 -20 -15 -10 -5 0Backscattering values

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Known water

Known Non water

Total 666 pixels486 pixels for estimation120 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

KnownClassified

Water Non water

Water 118 2

Non water 10 356

KnownClassified

Water Non water

Water 35 10

Non water 0 135

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

14.731-ANN

14.684-Supervised Classification with Maximum Likelihood

14.876-14.8Discriminant

8.316-19.5Probabilistic

20.5435-12.0Deterministic

Approach C

12.5806-16.4 dBSpatial Correlation Coefficient

15.33-14.5 dBMinimum Difference in Area

14.5496-15.0 dBProbabilistic

16.2188-14.0 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

Thank you and questions