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5

This varies greatly on the characteristics of the scene. Fire scar mapping studies using Landsat-5 TM have used the following three band combinations: Spain: Bands 4, 5, 7 CHUVIECO, E., and CONGALTON, R., 1988, Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 4, 41–53. Amazonia: Bands 3, 4, 5 PEREIRA, ...


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Another option if you have pre and post fire scenes is to use the differenced Normalised Burn Ratio (Key and Benson 1999), which really makes fire scars stand out. dNBR is calculated as: NBR = (R4-R7) / (R4+R7) dNBR = NBRprefire - NBRpostfire Where: RN = reflectance (not raw digital numbers) of Landsat 5 TM band 4 or 7.


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You could still use zonal statistics with the minimum option. It produces a raster would could be used for further processing Zonal Statistics


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The first thing you will want to do is look at the Google Terms of Use and Licensing. Google is very particular on how their data and software can be used. I would look at this first as it may be a show-stopper. The second thing I would consider is that the imagery in Google isn`t raw imagery; they are chips or tiles of data saved in a web tiling format. ...


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Use LDOPE-1.7 (https://lpdaac.usgs.gov/tools/ldope_tools), using "create_mask". this function takes MOD35_L2 HDF and creates a cloud mask in hdf. use MRTSwath tool for projection/re-sampling/clipping and convert new hdf to GeoTiff.


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One of the most common reason for the covariance matrix that is not invertible from samples is the presence of null variance for one band (all your pixel in one band have the same value). In this case, increasing the size of the sample increase the chances to have a non null variance. Note that the band 6 (thermal) is the most likely to be constant, ...


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.ers - header file .ecw - data file


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The task that you are describing is called image classification. There are numerous ways to classify an image--from very basic thresholding to more advanced supervised classification approaches. Image classification using multispectral data like Landsat requires basic radiometric correction, often accomplished using Dark Object Subtraction. Song et al. ...


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The good folks at the Remote Sensing Applications Center (RSAC) noticed from the metadata that the Lidar data specs are insufficient to calculate many of these grid metrics. In particular: NPS is 1.0 – 1.5 pulses/sq m Side Lap (Minimum): 25% Field of View (full): 40 degrees These three parameters, especially when combined, will likely result in data ...


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Related to answer I gave to a similar question (determine min and max elevation ... within my current extent), I wonder if this would work: import arcpy # this sets extent to current display, you can instead set it to ROI polygon arcpy.env.extent = arcpy.mapping.MapDocument.activeView.Extent # for a multi-band raster, pay attention to the band index (last ...


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Classifying urban areas from Landsat data is a common practice and usually yields accurate results. To improve your accuracy I would reassess your training data. As a rule-of-thumb: 1) the more samples the better, 2) samples should be distributed evenly throughout the scene. There are numerous studies on just this topic: Extraction of urban built-up ...



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