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In ENVI 5x (the procedure is similar in ENVI 4x or ENVI 5 Classic), use the File > Save As menu to save your TIFF to ENVI format which is a flat binary file. This should default to BSQ, but if it doesn't you can convert using the Convert Interleave tool. You also get header (.hdr) and pyramid (*.enp) files, but you can delete those.


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Basically an edge is a rapid change of slope and therefore equivalent to large positive or negative values of the curvature of the DEM. As far as I've found in Google you can calculate curvature in ENVI: http://www.exelisvis.de/docs/ExtractingTopographicFeatures.html Afterwards you should filter the result for large pos/neg values. ENVI also offers to ...


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In order to get at the classes you describe, you will need to incorporate a sophisticated classification algorithm and ancillary data derived from the imagery. I would recommend two approaches: 1) an object-oriented image segmentation (IS) approach using IS software such as eCognition or 2) a pixel-based non-metric, decision tree (Random Forest) approach ...


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You can read this tutorial (ModisDownload: an R function to download, mosaic, and reproject the MODIS images). It has a good tool to use modis products. I you need something that is not in this tutorial you can add to your question.


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first of all, download the landsat_destripe file from exelis website. If you are using Envi 5.0, find the save_add folder in your installation destination and put it in there. Fire up Envi Classic -> Basic tools -> Preprocessing -> General purpose utilities -> Landsat ETM+ Destriping. Follow the GUI. =)


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The short answer is: all of them. Most classification algorithms can derive useful information from all of the bands. However, there are certainly bands that are better at discriminating between vegetation types. You may also want to incorporate band indices such as NDVI or EVI into your classification algorithm. NASA produced some useful tables to help ...


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As far as I know there is indeed no pre-implemented module for the automated conversion of SPOT DN values to reflectance. If you insist on doing it in ERDAS Imagine, then you should use the Spatial Modeler for the conversion. Maybe this might help you getting started. You need to look up the gain parameters of the SPOT sensors in order to apply the right ...


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I assume you are going to apply an automated classification algorithm to detect roads in the imagery. I would recommend two products: 4-band NAIP imagery at 1m spatial resolution and Landsat 8 (15m (panchromatic)- 100m (SWIR))--both of which are free and available from Earth Explorer and from a variety of state web sources. The 30m Landsat data would be ...


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I don't know about any European database of ready-made high resolution NDVI, but it's relatively easy to create it from raw Landsat imagery in 30m resolution. Access to the Landsat archive is free of charge (only registration needed) via EarthExplorer web service link. In the Data Sets tab, expand "Landsat Archive" list and check L8 checkboxes for ...


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There is two ways you can do this: Inside the toolbox: Grass: i.atcorr: performs atmospheric correction (i don´t know if it works with spot) Orfeo Toolbox: Optical calibration (works with spot 5) Inside toolbox --> band math you can read the metadata, look for the coeficients for the following equations ( for example: <SUN_ELEVATION>, ...


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Your script is configured to take a point shapefile of training data and use that to train a Random Forest classifier. The screenshot shows the form the shapefile attribute table needs to take in order to be used as a training set. The important fields here are 1) the XY coords, 2) the pixel values for each band at that XY coordinate, and 3) the class of ...


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NASA hosts a paper that can be found here which gives detailed answer to your title question. In particular page 2, paragraph 3 and page 21 starting at paragraph 2. The short answer is no, SRTM data is not necessarily bare-earth measurement and may be tree canopy. However, radar can potentially partially penetrate tree canopies, so the given height might not ...


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I'm not an SRTM expert, not even an SRTM novice. http://www.opendem.info/technology.html provides a nice methodology for correcting for tree canopy height. So, no, it does not appear that the SRTM data is corrected. However, there are ways to do such a thing. Good Luck.


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The USGS have been recommending using EarthExplorer (EE) because it has the most development resources right now. They offer the USGS Inventory Service as a machine-to-machine SOAP API for communicating with EE for querying their archive for scenes. This way, you can also order scenes for processing to Level 1 georegistered radiance products, and also get ...


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This depends what you are trying to do with the data, i.e. what really matters to you? Question is which one you need more, high spatial or high temporal resolution since it's hard to get both. In high temporal resolution I think the MODIS or AVHRR products mentioned above will be the choice, but if high spatial resolution is a must, then I think Landasat 8 ...


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from what I know, Icesat products in the poles are in the "NSIDC Sea Ice Polar polar stereographic" projection. Depending on which pole your are, you could use the EPSG code with gdalwarp to reproject your data. Arctic : EPSG 3413 Antarctic EPSG 3976


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I found to ways to read aster metadata in envi (hdf format). 1.- using "envi_file_query" to read a particular attribute ; set the name of the file FILE_NAME='link to .hdf' ; open data as aster image envi_open_data_file, FILE_NAME, /aster, r_fid=fid ;query the metadata inserted in the hdf (global attributes) envi_file_query, fid[0], nb=nb, ...


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Free method : The High-resolution images are not available for free unless you are part of a body of research and teaching. You have two options: Free access to the results of NDVI already calculated. For example : http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD13A2_E_NDVI&date=2014-06-01 Free access to MODIS imagery (but not high res... it's ...


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The effects you are seeing are atmospheric effects due to differences in atmospheric aerosols, sun angle, and Rayleigh scattering. Since you have two scenes of the same location, though at different time periods, I would recommend using a technique called Dark Object Subtraction (DOS) (Song et al. 2001). From the ENVI web site: Dark object subtraction ...


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I'd suggest to try regression analysis. Convert both images to grids, 3 bands ( ?). Create few hundred random sampling points. Sample corresponding bands and see (Excel) if correlation between 1st and 2nd rasters exist. If you are happy with correlation coefficients, use regression coefficients ( slope and intersect if relation is linear) and rasrer ...


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There is a help file here at the ESRI site http://resources.arcgis.com/en/help/main/10.2/index.html#//009t000000nn000000 In the table of contents on Arcmap go into properties for the second raster file, go to the display tab and reduce the contrast and brightness settings until close to the first raster. ...


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The values are scaled to limit the amount of saturation and storage space. Undoing the scaling with simple band math will convert the scaled values to absolute radiance. The Hyperion documentation recommends using FLAASH or ACORN to convert radiance to reflectance. Detailed instructions on how to rescale the data are available in the EO-1 User Guide v. ...


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EVI = 2.5 * (NIR-Red)/(NIR+6*Red-7.5*Blue+1) I have used this formula in the raster calculator of QGIS.


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Three band images are generally not sufficient for high quality land cover classifications. Usually at least near infrared band is required. When I was classifying one image that had four bands (r,g,b,nir) I also calculated NDVI and included it in classification. As you probably don't have nir band you could add more information for the classification using ...


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Do you have access to the point cloud from the imagery? In mosaicing the images from the drone, depending on software, you can export a 3D point cloud. You then can use LASTools to classify the ground points and then convert to a DEM.


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I wanted to write a comment, but i don´t have points enough. I think that what you need is to do a classification by texture. Last week i was on a seminar where the aim was to classify images of high resolution with texture and variograms (geostatistics). you can read this: http://www.sciencedirect.com/science/article/pii/S0098300499001181 atkinson & ...


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Glovis redirects you to Earth Explorer for the actual download, so I often opt to use Earth Explorer directly. There is another very good download site you may be interested in called Reverb | Echo. I have had issues in the past using Glovis with Google Chrome as the requisite pop-ups are blocked prior to download. These are the correct steps to take in ...


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The problems you are encountering are likely a result of your MATLAB code used to create NDVI. There should be no need for filtering or resampling to lower resolution. The following code is what I use in MATLAB to create NDVI from 4-band NAIP imagery at both half meter and 1 meter spatial resolution. file = 'F:\input\4211258_se.tif'; [I R] = ...


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Have a look at this web site, I have used this site to download the full 7 bands.


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Just found this (here): "Typically, NBR and ΔNBR images are generated shortly after a fire burns to get an initial assessment of burn severity and to support field work. During the next growing season, NBR datasets are calculated again (called “extended assessments”) to assess vegetation survival and delayed mortality." It is true that the ΔNBR Burn ...


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First you need to convert DN to reflectance and then perform atmospheric correction on your Landsat scenes to reduce the radiometric differences between time periods. I think the approach to use the polygons as a mask is not the right path to take. Rather, perform a binary classification of these scenes so that your wetlands have a value of 1 and everything ...


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Yes, you can use NDVI to calculate regrowth between two scenes. NDVI = ( NIR Band (B5) - Red Band (B4)) / ( NIR Band (B5) + Red Band (B4)) dNDVI = NDVIpostfire - NDVIfire NDVI assumes scene digital numbers (DN) have been converted to reflectance.



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