you can use,
Normalized Difference Built-up Index (NDBI),
for this you can run following code in Google Earth Engine Code Editor
// point of interest
var point = ee.Geometry.Point([80.62814, 7.29069])
// get land sat 8 images
var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');
// Get the least cloudy image in 2020
var image = ee.Image(
I tried the script you added, indeed using image: evi.visualize(eviParams) will give you an RGB image with high values. Just use image: evi and it should work, at least it worked for me.
You can try to export the file as EE Asset and open it in Google Earth Engine. Might be easier to analyse this way.
I am not sure about the historic images, but one can assess the most recent ones from USDA's ArcGIS server: https://gis.apfo.usda.gov/arcgis/rest/services
One can use either ArcGIS Pro or QGIS to export the regions of interest to GeoTIFF or whichever format you prefer.
PolSAR and InSAR are two fundamentally different parameters.
PolSAR looks at different polarities within one observation, so whether the signal is 'vertical' / V, or 'horizontal' H, when the signal was emitted and when it is received. This is why you'll often see a SAR dataset labelled with either VV, VH, HV, or VV (theoretically, VH and HV should be the ...
You can install and use the OpenCV module in QGIS python environment (https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_thresholding/py_thresholding.html).
It will require some coding but the above link will help.
In ArcGIS getting the desired result is difficult to achieve as lots of customization is required.
I would suggest OTB BandMath tool (from QGIS Processing Toolbox > OTB > Image Manipulation), which can handle bands calculation.
Given ExG = 2 * G - R - B, the corresponding OTB Expression is 2*im1b2 - im1b1 - im1b3.
(Please note im1 means the first image and each band is represented as b1 = red, b2= green and b3 = blue).
(Above: Output Image - used ...