14

NDVI is defined for any two bands with near-infrared and infrared data (it is an empirical remote sensing index). As such, you can calculate it straight from the DNs. This is mostly OK if you are only classifying or analyzing vegetation on a single image without significant atmospheric effects (cirrus clouds...) However, if you are performing change ...


11

How about firing up an EC2 or rackspace instance and installing the EarthExplorer bulk download application: http://earthexplorer.usgs.gov/bulk/ You could hit the EarthExplorer service with a POST request to submit jobs programmatically: http://earthexplorer.usgs.gov/subscription/submit/ You would need to provide standingRequestName, frequency, ...


11

I saw a blog post from developmentseed for their command line utility landsat-util. Power tools for Satellite Imagery The landsat-util can be forked from github and compiled from source unless your OS offers it in a binary ready to go. The blog describes it simply as: a command line utility that makes it easy to search, download, and process Landsat ...


9

Fundamentally the question here is "what does 'scientifically valid' mean". If you are looking to do spectral modelling on the data, then the answer is possibly different than if you are looking at doing classification / image segmentation. Pansharpening (depending on the method) is simply going to change the range of the values a fairly small amount and ...


8

The USGS provides a tool for bulk downloading of Landsat Metadata: Landsat Bulk Metadata Service The MTL files will allways be distributed with the data if you download the scenes through USGS EarthExplorer. If you don't want to download the bands again and just the MTL text files for each scene you can take a look at Amazons Landsat Mirror. Each scene is ...


8

gnutls.h which is required is missing from the filesystem even if you install libcurl4-gnutls-dev which supposedly has the headers files for curl. to correct for that error run: # apt-get install libgnutls28-dev to add the missing header and pip finally to install pycurl and landsat-util


7

Take a look at i.landsat.rgb - Performs auto-balancing of colors for LANDSAT images, probably before running the pansharpening. You may also consider to convert the digital numbers of the individual channels to top-of-atmosphere radiance or reflectance with i.landsat.toar. See also http://grasswiki.osgeo.org/wiki/LANDSAT BTW: having a range of 0-65535 for ...


7

I just thought I'd add that there are some 'pure' Python solutions for several nodes in this workflow, also. Some file reading and basic processing: Spectral Python: http://spectralpython.sourceforge.net/ More classification than you'll find in pure remote sensing and GIS packages: http://scikit-learn.org/stable/ More links I can't share: 6S Python ...


7

Forgive me if this is too basic an answer: panchromatic and infrared are mutually exclusive. Panchromatic means all visible light, which is generally considered to range 0.4μm to 0.7μm in wavelength. Near (or reflected) infrared energy is generally considered to range 0.7μm to 0.9μm in wavelength, just beyond visible. See Infrared vs. Panchromatic - Mt. ...


7

One flaw in your approach. You don't need to go through DN to radiance. You can go straight to the DN to reflectance. Just stick to ((B1*0.00002)-0.1)/0.74457226676389733207607359928648.


6

If you really want to use python, and you need functionality similar to GRASS, perhaps the easiest solution would be to use GRASS via Python. That isn't specific to Landsat8, but I don't think a processing solution should be tied that closely to a specific satellite. You could implement some simple wrappers / higher level functions if you're consistently ...


6

Satellite zenith angle Images from Landsat 1 - 7 are always nadir. In other words, their satellite zenith angle is close to 0°. Likewise, the _MTL.txt you posted corresponds to a Landsat 5 image, and so is a nadir image. Off-Nadir imagery has been introduced with Landsat 8. Landsat 8 images have a field NADIR_OFFNADIR in their _MTL.txt metadata. The ...


6

The reason why it looks pixelated at 1:2500 (and probably at 1:10K or 1:20K) is that you are looking at a single resolution cell (30 metres on the ground, as pointed out by Mapperz) across multiple pixels on the screen. Lets assume that you're looking at a 30m cell at 1:1000 (in true scale, ignore that your monitor probably doesn't really do that) - that ...


6

Right now I have checked into SVN a set of updates for i.landsat.toar into SVN for Landsat8, provided by the author E. Jorge Tizado. They hopefully solve your problem (NASA changed the metadata format). You can download the updated winGRASS binary by tomorrow morning either through the OSGeo4W installer ("advanced") or as standalone daily snapshot version ...


6

You have downloaded the data and you can find it if you unpack the .tar.gz file using 7zip or similar software for unpacking files. The .tar.gz file is the fourth file from the top that can be seen in your first screenshot. Do note that you have to unpack the .tar.gz twice in order to get to the data. You will easily recognize it as you will see the ...


5

ROIs in ENVI are stored in pixel-based co-ordinates, with no geographic information at all. The size of the image used to create the ROI is stored in the ROI file, and when you open the ROI list for an image it only displays the currently loaded ROIs that match the image dimensions. Although your images are all of the same location, in the same projection ...


5

Usually you start with cloud (and cloud shadow) removal then you run the classification. One of the best papers I know about cloud detection on Landsat is Zhu and Woodcock (2012)


5

Step 1: Convert from digital numbers (DN) to radiance This is done by applying the multiplier and addition numbers as found in the metadata (.MTL) file. For the thermal bands (B10 and B11), the values are usually, but you should check the file: Add: 0.1 Multiply by: 0.0003342 (3.3420E-04) In ENVI you can apply this correction using 'band math': float(b10)*...


5

Your clipping fails because the raster has the odd nodata value of -3.4E+38. Unfortunately, you can not enter that value in the input form. So I suggest to use gdalwarp to change the nodata value and clip as well, but to the extent of the polygon layer: gdalwarp -overwrite -s_srs EPSG:32634 -dstnodata -10 -q -cutline forest_2013_extent.shp -...


5

Yes, it affects the values of NDVI, and it may not give the desired results sometimes. Information from Wikipedia: Normalized Difference Vegetation Index provides some details about the effects of cloud and snow as follows: clouds and snow tend to be rather bright in the red (as well as other visible wavelengths) and quite dark in the near-infrared ...


5

Bare soil and urban areas are notoriously hard to segregate. Even with a perfect atmospheric correction, there will be relatively high confusion between the two, particularly when limited to multispectral datasets. The atmospheric correction technique you are doing is a simple dark object subtraction, wherein the darkest objects in the landscape should have ...


5

It's going to be something like this: var landsat8= ee.ImageCollection('LANDSAT/LC8_L1T_TOA').filterBounds(geometry) var waterThreshold = 0; // water function: var waterfunction = function(image){ //add the NDWI band to the image var ndwi = image.normalizedDifference(['B3', 'B6']).rename('NDWI'); //get pixels above the threshold var water01 = ndwi....


5

You can perform a land cover classification on a single Landsat scene without performing spectral and radiometric corrections. You will only need to do those corrections if you're trying to apply reference spectra to your classification, performing a classification that covers multiple scenes or performing a classification over a time series of the same ...


5

About Tiles: There is a kml file provided by ESA that shows the location of each Tile. Overlay it with your study area and you will see which is your target tile. About acquisition: Acquisiton plans are also published by ESA. They are published as KML files. You can download historical data but the complete plan for the next year is not pusblished. ...


5

Aldo's answer is the correct one, no doubt, but if you want to make the code shorter and you don't mind loosing the 'core' of cloud masking, you can use a module: var point = /* color: #98ff00 */ee.Geometry.Point([35.83946228027344, -3.5380934964711126]); //load images for composite var sr14= ee.ImageCollection('LANDSAT/LC08/C01/T1_SR') .filterBounds(point)...


5

I am not 100% familiar with the NDBI Index, but found an article here: https://www.researchgate.net/publication/273886729_Built-up_area_extraction_using_Landsat_8_OLI_imagery Specifically on Page 14 that describes the NDBI as: NDBI = (Band 5 - Band 4) / (Band 5 + Band 4) Which is the original equation. In order to get the NDBI for the Landsat 8 the ...


4

Thanks Michal and Markus. Finally I was able to do the pansharpening with the indicated function brov. Once performed in GRASS, I exported the RGB rasters to gdal GTiff with as data type Uint16. Markus, I have been downloading and using several Landsat 8 images (tiff of each of the 11 bands), obtaining nice visual results, both in natural/false color. To my ...


4

I intend to do the same so I start an Amazon EC-2 instance and install the Bulk Download on it. But as far as I know it's a graphical application and nothing in the doc Bulk Download Tutorial lets hope that you can use it with the terminal. I read here about the possibility of using Curl but it returns an 403 access denied After writing emails to USGS, the ...


4

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 ...


4

You will need to go back to your imagery provider and get imagery from an earlier (or later) date which is cloud free Mapbox provides cloud free imagery but as it is merged from lots of different photo's you can't use it for analysis and I don't know how much it costs https://www.mapbox.com/data-platform/


Only top voted, non community-wiki answers of a minimum length are eligible