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The devastating magnitude 8.9 earthquake followed by a tsunami on March 11, 2011, profoundly affected Japan, particularly in Iwate Prefecture. This project utilizes satellite imagery from NASA to calculate the inundated areas caused by the tsunami, which reached devastating heights of up to 37.9 meters. Using advanced imaging techniques and methodologies, such as the Mahalanobis distance and grain analysis, we quantify the inland and offshore areas affected by the tsunami, revealing that approximately 580 km² was inundated inland and 139.2 km² offshore.
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Final Project: EPS 209 Carolina Munoz Saez
Introduction • Last March 13th, 2011 Japan was affected for a M 8.9 offshore earthquake, which was followed for a tsunami Tsunami waves were estimated to have reached 37.9 meters (124 ft) on a coastal city in Iwate Prefecture. The tsunami inundated a total area of approximately 500 km2(181.5 sq mi) in Japan.
http://rapidfire.sci.gsfc.nasa.gov/subsets/?subset=Japan Earth movement seen from space Pixel Size: 250m • NASA satellites have snapped pictures of the devastating floods and fires in Japan from the deadly earthquake and tsunami Band 7-2-1 Combination: Vegetation, burned areas & water Feb. 23, 2011 March 13, 2011
March 13, 2011 True-color imagery uses MODIS Bands 1, 4, and 3, corresponding to the red, green, and blue range of the light spectrum, respectively Objective: Calculate area affected by tsunami • Inland (Band 721) • Oceanic floor (True Color) Methodology: • Retrieve satellite image from a WMS • Crop interest area • Apply filters • Grain Analysis (regionprops) • Mahalanobis distance
Results Inland I1=imread ('Japan.2011054.aqua.721.250m.tif'); %cut image. M1 = [4278.5 3100.5 636 1716] G1 = imcrop(I1,M1) %Convert it to black & white: level1 = graythresh(G1); bw1 = im2bw(G1,level1);
Results Inland graindata1=regionprops(cc1,'all'); graindata2=regionprops(cc2,'all'); grain_area1=[graindata1.Area]; grain_area2=[graindata2.Area]; Area_inland=grain_area2-grain_area1
% SIXTH: Ocean Floor: AplyMahalanobis distance on the mask in both images filtSize = 25; if (exist('myPatches.mat')) load('myPatches.mat'); else % call read patch code roiPixA = getPixStd(A3, filtSize); roiPixB = getPixStd(A4, filtSize); roiPix = cat(1, roiPixA, roiPixB); myStd = max(std(roiPix)); save('myPatches', 'roiPix', 'myStd'); end % FOURTH STEP: Single grain dilatation SE = strel('disk', 150, 8); dsg1= imdilate(1-sbw1,SE); dsg2= imdilate(1-sbw2,SE); figure, imshow(dsg1) figure, imshow(dsg2) D=dsg1-(1-sbw1); %dilatationarea figure, imshow(D) %FIFTH: Ocean Floor: extractinformationforoceanfloor on dilatedgrain Mask D2 = logical(D); D3 = cat(3, D2, D2, D2); A3 = immultiply(G3,D3); A4 = immultiply(G4,D3); figure;imshow(A3); figure;imshow(A4); function [roiPixmyStd] = getPixStd(rgbImg, filtSize) % read and check image, get size [nRowsnColsnLyrs] = size(rgbImg); if (nLyrs ~= 3) error('Image is not RGB!'); end nPix = nRows * nCols; % display original image figure; imshow(rgbImg); title('RGB image'); % smooth image fprintf('Smoothing image ...\n'); myFilt = ones(filtSize) ./ filtSize^2; smoothImg = imfilter(rgbImg, myFilt); …………………
Final Results • Area_inland= 9285 pixel • Area_offshore = 22267 pixel Image Pixel Size: 0.25 Km • Area_inland= 580 Km2 • Area_offshore= 1392 Km2