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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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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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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 ...


2

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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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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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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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 ...


1

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 ...


1

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


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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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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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