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The missing data is masked prior to creating the composite. Since the SLC-off line errors are not in the same place for multiple satellite passes you still get a full coverage in the multi-image composite. Landsat 7 Collection 1 Tier 1 composites are made from Tier 1 orthorectified scenes, using the computed top-of-atmosphere (TOA) reflectance See for ...


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You can calculate the NDWI using basic raster calculator in arcmap. You need to use Landsat bands 3 and 5 (Landsat 8 OLI), and simply calculate a new raster with an algebraic expression: (Band 3 - Band 5)/(Band 3 + Band 5) You should preferably use images corrected to TOA. In this example I have bands 3 and 5 The result is a raster with values between -1 ...


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Otsu's method for finding an optimal threshold has been implemented in Earth Engine. See the blog post: Otsu’s Method for Image Segmentation


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NDWI should be okay for it, but you can also look at NIR-ratio, which is the fraction of the total reflectance that is contributed by the NIR-band. In a case where you have the Red Edge band, I'd not count that and simply do: NIR_ratio = NIR / ( Green + Red + NIR ) This approach gives a decent singleband ratio to look at. Shadows will consistently give ...


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Suppose you have a raster titled LandCover (used the name LC here). You can assign NA values to your RasterLayer for negative values. LC[LC < 0] <- NA This will set a lower bound to the values.


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I always convert the raw data to top-of-atmosphere reflectance first, and then derive the index. I do this to address issues of variations of earth-sun geometry during the year, and also atmospheric path length. If this is done, then the index should range from -1 to +1, which would assume a theoretically perfect response of vegetation, and water, ...


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There is no real difference in applying the ratio to integer or float data. In evaluating results, you should first, be looking at the distributional characteristics of your data and not the visual. If the data is in fact "flat" you should see this in a plot of the probability density function (PDF). Here is a worked example, using data in the RStoolbox ...


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What about values equal to 0.4? .gt(): greater than .gte(): greater or equal than Then: // Reclassify each image in NDWI collection and calculate yearly composite var reclassified = NDWI.map(function(img){ return img.gte(0.4) // or gt() })


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Try using image operator, like: water = evi.lt(0.1).and(mndvi.gt(evi).or(mndvi.gt(ndvi)))


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I think there can be a simpler approach to this. From my understanding you just want to see if the NDWI value ever exceeds 0.3 in ANY scene of the image collection. To do this you can first find out pixels in each scene that are greater than 0.3 NDWI. var gt3 = ndwi.map(function(image){ return image.gte(0.3); }); This will give you a mask layer having ...


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You can use sum() reducer to compute frequency. I took your code to reduce it and change the approach. As I said to you in another post, reducers need to be applied as the last process. The process itself is just: var decision_tree = function(image){ var mndwi = image.normalizedDifference(['B3', 'B6']); var evi = image.expression( '(NIR - RED)/(NIR+...


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It is computed as (NIr1 - NIr2) / (NIr1 + NIr2). For ETM+, NIr1 is B4 and NIr2 is B5. Note that this is the Gao (1996) version of NDWI (which may or may not be suitable for Landsat) and should not be confused with the McFeeters (1996) NDWI.


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I applied the following to successfully create a NDWI GeoTiff after I clipped a region. You need to clip a region otherwise the processing area might be too large and you'll run into memory issues. library(raster) library(rgdal) #Load the landsat landsat8 = brick("C:/Test.tif") # Review the raster dimensions and print details for inspection dim(landsat8) ...


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While an NDWI image is a single-band image, it is the product of subtracting, adding and multiplying multiple bands together--in the McFeeter's case, the values of the Green and Near Infrared (NIR) bands from a previous image are used to calculate a numerical value that is then displayed in a NDWI image (where each pixel value is between -1 and 1). Your ...


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You are the right way. Follow this process Calculate MNDWI with Band using (b1 - b2)/(b1 + b2) where b1 is green band and b2 is SWIR1. Read this paper for more information about MNDWI. Also with Band math, create the water mask. Use (b1 le 0)*0 + (b1 gt 0)*1 using MNDWI result. Where le is lower/equal than b1 and gt greater than b1. Go to Vector / Raster ...


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TOA should be applied to data before the pan-sharpening for better results. Your results extreme values are probably due to the blank part of the image (border area) being calculated along with the rest of image. It is recommended to remove NoData and/or crop to area of interest before applying any indice. Use a stretch command (standard deviations, percent ...


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