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Can I ask a question here mate, am sure you'all have encountered an issue where you have to clip a DEM file for a small area it shows pixels, just pixels, can this be Improved like using GEE to have the exact scale for proper representation of the map?

Thank you for the video and the code sir, I appreciate it

can i get coding?

awesome! I've been looking for a straightforward tutorial just like this. Now I wish there was one for me to know how to make buttons redirect to external links/blog posts

i'm getting same value of in inspector of good and flood filter its all same as you

T

But this code shows a lot of water bodies on the eastern side, which we all know is a huge desert. I think its mixing sand with water.

Thank you so much. Would you please share the code?

flood water should be more than simple water. but in this analysis simple water is more. why?

Is it possible to calculate total flood area of Pakistan?

can u please make more figma related videos

Which code can we use to output the final map result. please can you reply below. Thank you

Wonderful Sir, this video helped me a lot for my class report on "Impact of Flood and their mitigation strategies in Sindh " thank you Sir for your guidelines..... i really appreciate it....

Sir, what the name of methodh that you use, change detection or something else?

Hello sir, thanks for the video. I wanna ask you how to determine the min max value?

Good say sir, please i stuck 😢. My good state size is printing as zero, what do i do?

It is Nice

thanks for your kind sharing bro, How can get other countries feature boundaries?

The code says toDB is not defined.

Non-satisfied your answer, we want to see ce script, otherwise no explanation is required

could you send me code of this anaysis

Good morning Sir thanks for this kind explanation. Please can you share script with us?

bang bagi codingnya bang

Asslamualikum Sir. Thanks for This video, Can you share the flood report ! i need it

Well presented. Thank you so much.

-Get Feature Boundary from: data.humdata.org/dataset/pakistan-union-council-boundaries-along-with-other-admin-boundaries-dataset -Functions code: //declared function to convert the images types and perform the functions // Function to convert from d function toNatural(img) { return ee.Image(10.0).pow(img.select(0).divide(10.0)); } //Function to convert to dB function toDB(img) { return ee.Image(img).log10().multiply(10.0); } //Apllying a Refined Lee Speckle filter as coded in the SNAP 3.0 S1TBX: //github.com/senbox-org/s1tbx/blob/master/s1tbx-op-sar-processing/src/main/java/org/esa/s1tbx/sar/gpf/filtering/SpeckleFilters/RefinedLee.java //Adapted by Guido Lemoine // by Guido Lemoine function RefinedLee(img) { // img must be in natural units, i.e. not in dB! // Set up 3x3 kernels var weights3 = ee.List.repeat(ee.List.repeat(1,3),3); var kernel3 = ee.Kernel.fixed(3,3, weights3, 1, 1, false); var mean3 = img.reduceNeighborhood(ee.Reducer.mean(), kernel3); var variance3 = img.reduceNeighborhood(ee.Reducer.variance(), kernel3); // Use a sample of the 3x3 windows inside a 7x7 windows to determine gradients and directions var sample_weights = ee.List([[0,0,0,0,0,0,0], [0,1,0,1,0,1,0],[0,0,0,0,0,0,0], [0,1,0,1,0,1,0], [0,0,0,0,0,0,0], [0,1,0,1,0,1,0],[0,0,0,0,0,0,0]]); var sample_kernel = ee.Kernel.fixed(7,7, sample_weights, 3,3, false); // Calculate mean and variance for the sampled windows and store as 9 bands var sample_mean = mean3.neighborhoodToBands(sample_kernel); var sample_var = variance3.neighborhoodToBands(sample_kernel); // Determine the 4 gradients for the sampled windows var gradients = sample_mean.select(1).subtract(sample_mean.select(7)).abs(); gradients = gradients.addBands(sample_mean.select(6).subtract(sample_mean.select(2)).abs()); gradients = gradients.addBands(sample_mean.select(3).subtract(sample_mean.select(5)).abs()); gradients = gradients.addBands(sample_mean.select(0).subtract(sample_mean.select(8)).abs()); // And find the maximum gradient amongst gradient bands var max_gradient = gradients.reduce(ee.Reducer.max()); // Create a mask for band pixels that are the maximum gradient var gradmask = gradients.eq(max_gradient); // duplicate gradmask bands: each gradient represents 2 directions gradmask = gradmask.addBands(gradmask); // Determine the 8 directions var directions = sample_mean.select(1).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(7))).multiply(1); directions = directions.addBands(sample_mean.select(6).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(2))).multiply(2)); directions = directions.addBands(sample_mean.select(3).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(5))).multiply(3)); directions = directions.addBands(sample_mean.select(0).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(8))).multiply(4)); // The next 4 are the not() of the previous 4 directions = directions.addBands(directions.select(0).not().multiply(5)); directions = directions.addBands(directions.select(1).not().multiply(6)); directions = directions.addBands(directions.select(2).not().multiply(7)); directions = directions.addBands(directions.select(3).not().multiply(8)); // Mask all values that are not 1-8 directions = directions.updateMask(gradmask); // "collapse" the stack into a singe band image (due to masking, each pixel has just one value (1-8) in it's directional band, and is otherwise masked) directions = directions.reduce(ee.Reducer.sum()); //var pal = ['ffffff','ff0000','ffff00', '00ff00', '00ffff', '0000ff', 'ff00ff', '000000']; //Map.addLayer(directions.reduce(ee.Reducer.sum()), {min:1, max:8, palette: pal}, 'Directions', false); var sample_stats = sample_var.divide(sample_mean.multiply(sample_mean)); // Calculate localNoiseVariance var sigmaV = sample_stats.toArray().arraySort().arraySlice(0,0,5).arrayReduce(ee.Reducer.mean(), [0]); // Set up the 7*7 kernels for directional statistics var rect_weights = ee.List.repeat(ee.List.repeat(0,7),3).cat(ee.List.repeat(ee.List.repeat(1,7),4)); var diag_weights = ee.List([[1,0,0,0,0,0,0], [1,1,0,0,0,0,0], [1,1,1,0,0,0,0], [1,1,1,1,0,0,0], [1,1,1,1,1,0,0], [1,1,1,1,1,1,0], [1,1,1,1,1,1,1]]); var rect_kernel = ee.Kernel.fixed(7,7, rect_weights, 3, 3, false); var diag_kernel = ee.Kernel.fixed(7,7, diag_weights, 3, 3, false); // Create stacks for mean and variance using the original kernels. Mask with relevant direction. var dir_mean = img.reduceNeighborhood(ee.Reducer.mean(), rect_kernel).updateMask(directions.eq(1)); var dir_var = img.reduceNeighborhood(ee.Reducer.variance(), rect_kernel).updateMask(directions.eq(1)); dir_mean = dir_mean.addBands(img.reduceNeighborhood(ee.Reducer.mean(), diag_kernel).updateMask(directions.eq(2))); dir_var = dir_var.addBands(img.reduceNeighborhood(ee.Reducer.variance(), diag_kernel).updateMask(directions.eq(2))); // and add the bands for rotated kernels for (var i=1; i<4; i++) { dir_mean = dir_mean.addBands(img.reduceNeighborhood(ee.Reducer.mean(), rect_kernel.rotate(i)).updateMask(directions.eq(2*i+1))); dir_var = dir_var.addBands(img.reduceNeighborhood(ee.Reducer.variance(), rect_kernel.rotate(i)).updateMask(directions.eq(2*i+1))); dir_mean = dir_mean.addBands(img.reduceNeighborhood(ee.Reducer.mean(), diag_kernel.rotate(i)).updateMask(directions.eq(2*i+2))); dir_var = dir_var.addBands(img.reduceNeighborhood(ee.Reducer.variance(), diag_kernel.rotate(i)).updateMask(directions.eq(2*i+2))); } // "collapse" the stack into a single band image (due to masking, each pixel has just one value in it's directional band, and is otherwise masked) dir_mean = dir_mean.reduce(ee.Reducer.sum()); dir_var = dir_var.reduce(ee.Reducer.sum()); // A finally generate the filtered value var varX = dir_var.subtract(dir_mean.multiply(dir_mean).multiply(sigmaV)).divide(sigmaV.add(1.0)); var b = varX.divide(dir_var); var result = dir_mean.add(b.multiply(img.subtract(dir_mean))); return(result.arrayFlatten([['sum']])); }