Tag Info

Hot answers tagged

5

There is no specific GIS software for doing this: most will handle the RGB image and the Lidar data. Basically, NDVI is (NIR - RED)/(NIR + RED). Most of the time, aerial Lidar gives you the NIR value (to be checked in metadata) and the first band of your RGB image gives you the RED value. Just make sure that your data are calibrated to reflectance (or, if ...


3

I would discourage you from perusing the course that you are on. In most cases there is some degree of useful reflectance information in every pixel. Before applying a dubious technique that just excludes what is perceived as shadow, you could first attempt to correct for the effect. I have had consistent success with the Minnaert correction in very steep ...


3

NDVI is for vegetation/non vegetation discrimination. So if your vegetation is always coniferous forest, then it should be the most efficient method in your case. Otherwise you will have confusions with crop, grassland and deciduous forests. In a montainous area, single reflectance thresholds will be problematic due to the hillshade (clearly visible on ...


3

You won't have any luck finding a satellite that provides data from the ultra violet (UV) portion of the electromagnetic spectrum. The reason is that the wavelength of UV energy is so short that most of it gets absorbed or scattered by the atmosphere, or tiny particles in the atmosphere before it even gets to the Earth (from the sun) let alone back up to any ...


3

There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with: Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...


2

@nicksan is right. In its simplest form, spectral (pixelwise) classification is based on a set of examples that you define manually. In most RS-oriented software (ENVI, ERDAS, Orfeo Toolbox, etc), this is based on photointepretation, i.e. drawing polygons or sampling pixel of each ground cover class, modeling them with some learning technique and ...


2

If you are not afraid of using Python for this you can use this little tool by CESBIO called Landsat Download. The only requirements are that you have an account on earthexplorer/glovis and the datasets you want to download are available online. Landsat Download Tool


2

This will really depend on the archive times you are looking for. However, here is an FTP link to Landsat data. Most to all of the images are cloud free, or +/-10% covered: ftp://ftp.glcf.umd.edu/glcf/Landsat/WRS2/


2

To create a DHM subtract the DEM from the DEM, this can be done in Esri Raster Calculator or GDAL_CALC. This will put all your elevations on a 'level playing field'. Syntax (Substitute full paths for DEM, DSM & DHM): GDAL_CALC.py -A DSM -B DEM --outfile=DHM --CALC "A-B" The DHM will be mostly 0 (or near enough), which you make your nodata value. With ...


2

A classifier, any classifier, can classify any kind of data. These objects, as Aaron correctly states, can be pixels, objects, superpixels, bananas, sounds, DNA, etc. The main differences which, in my opinion, is really relevant between superpixel- and pixel-based classification are as follows: pixel based : The resolution of the prediction is maximal, ...


1

The Red and Blue Bands appear switched in the March Image, as a big blue roof appears red, and red roofs appear blue. To change it, go to Layer Properties -> Symbology -> the click the drop down menu for Red Band_1 and select Band_3, do the reverse for Blue. That should work.


1

I am posting this as an answer due to length limit in comment, no hopes for credits:). Very broad brush, providing you've got DEM. Extract DEM for individual polygon to dem. Define dem's elevation extremes Set zCur+=-zStep. Step to be found by iterations beforehand, e.g. reasonable drop between 'tree top cell' elevation and neighbours Below=Con (dem => ...


1

Basically, DN is te value assigned to a pixel in a digital image, so you'll always have DN's. However, it is mostly used for the raw image values, coming directly from the sensor (usually after some recalibration to optimize the use of a pixel depth and correct some internal sensor errors). TOA reflectance is the value of the reflectance on Top of ...


1

I don't know for what object this particular spectral signature ties at but i can suggest to browse a spectral library for something that is close match. http://speclib.jpl.nasa.gov/


1

Its not inexpensive eqipment and it has some limitation but it works in cases like in your question, you could find all details here: Ground-penetrating radar Ground-penetrating radar (GPR) is a geophysical method that uses radar pulses to image the subsurface. This nondestructive method uses electromagnetic radiation in the microwave band (UHF/VHF ...


1

You should provide more details about the task. In general, extracting features from the data image heavily depends on what you are trying to detect/classify, and how are you trying to do it. Here's an example. If you are interested in classifying roads from an urban scene, you may be interested in evaluating large linear filter responses over the whole ...



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