Tag Info

Hot answers tagged

5

I've had to map ditches from 1 m LiDAR derived DEMs of agricultural landscapes before. It's certainly a challenging task to come up with a workflow that is suitable. You're ability to successfully extract a ditch network will depend on a number of factors. For example, are you only interested in roadside ditches? If so, are the roads on embankments (as is ...


5

Ok, I'm sorry to post a question and then answer it myself so quickly, but I found a nice set of course slides from Utah State University that has a lecture on opening raster image data with GDAL. For the record, here is the code I used to open the PRISM Climate Group datasets (which are in the EHdr format). def ReadBilFile(bil): import gdal ...


4

Vegetation extraction is a bit more complex than running the spatial analysis tools that you named. For better results I would suggest the following: run analysis on a 4 band image (e.g. R,G,B,NIR) change image to be symbolized as 432 for RGB not 321 create training samples that represent vegetation and run a supervised classification These steps will ...


4

The result from NDVI will be continuous (i.e. decimal) values between -1 to +1, therefore the raster must be able to store these values, and will use signed pixel depth. If you truly want 8-bit unsigned, you will need to adjust the expression in the raster calculator by linearly scaling to values between 0-255 and then applying the int() function on the ...


4

Panchromatic images are created when the imaging sensor is sensitive to a wide range of wavelengths of light, typically spanning a large part of the visible part of the spectrum. Here is the thing, all imaging sensors need a certain minimum amount of light energy before they can detect a difference in brightness. If the sensor is only sensitive (or is only ...


3

You appear to be looking at a suite of software options. A way of doing it in ArcMap model builder using off the shelf tools could be: point to raster (ensure snap to raster environment is set.) Expand (by 1 pixel to create your block of nine) Extract by mask. This method assumes that your points are not so close that their masks overlap. If overlap is ...


3

Answer for others so confused people as I am: To know how to deal with downloaded raw Landsat data - what else in pre-processing do I need? Firstly check their processing level in_MTL.txt file (included in downloaded Landsat image: http://landsat.usgs.gov/Landsat_Processing_Details.php) Processing level = DATA_TYPE L1T - terrain corrected processing. ...


3

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 & ...


3

Changes in vegetation over the month between your scenes could be part of the issue. It is also possible that there is some haze over areas of your scene outside of your dark object location(s), and therefore this haze is not being removed during your atmospheric correction. Another reason that you see contrast between the two scenes could be due to ...


3

There seems to be two camps about this one. Some prefer to mosaic before classification, others prefer to classify the images before mossaicking. Personally, I would classify the images first, then mosaic them. Have a look at the discussions on this page and you'll find arguments for and against both methods. Generally, they state that you should ...


3

After running Raster Calculator, use the Copy Raster tool with the pixel_type parameter set to 8_BIT_UNSIGNED, as shown below.


3

I would recommend calculating soil moisture indices from Landsat TM bands. MTRI has an interesting article on creating soil moisture index (SMI) from Landsat TM 5. Also, I would recommend exploring soil moisture estimates using TM band 6 (Thermal IR). Attached is a good tutorial on calculating indices from Landsat TM bands using ArcGIS 9.x (as you ...


2

Here is one possible workflow using ModelBuilder and Spatial Analyst tools that works for me: You supply the input raster, an XY coordinate for the location at which to sample for the region to be reclassified, and the new value of the classification. The output is a new raster (it won't let you overwrite the input raster), but that can be overcome ...


2

On Europe, there are: CORINE land cover (downloadable from there) LUCAS (see it there).


2

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 ...


2

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.


2

Here is how I would consider doing this if I absolutely had to have every neighborhood value (which is what it sounds like). First create 2 fields for the point geometry (one for x and one for y) and calculate the geometry for the x field and y field. I would be sure my point file was in a projected coordinate system like UTM Meters or State Plane Feet. ...


2

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. ...


2

I did this type of thing for a college project some years back using 25cm aerial photography. It is a difficult thing to accomplish. I ran a number of texture analysis on the imagery and added the bands to the RGB imagery to have more information during the classification process. While it is not a substitute for the NIR band, it did provide some additional ...


1

Are you trying to merge the DEM with the imagery as a fourth band? I don't think elevation is a valid dataset for land use classification. However, as you indicated, the pattern of the land derived from the DEM might be better. You may want to look into Composite Bands. That will add your land pattern file as a new band to your imagery. The composite ...


1

the best way is to use vrt (virtual raster template). with gdalbuildvrt you can extract each band in a single vrt file using the -b option. Then you concatenate into a single stacked image using gdalbuildvrt -separate . If you wish, you can then create a tiff file using gdaltranslate (with the -co TILED=YES option), but this is not necessary as QGIS reads ...


1

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 ...


1

Probably the easiest thing to do is use gdal_translate to create the resized image with something like: gdal_translate -outside 50% in.tif in.small.tif But if you do need to use ImageMagick, try translating your raster to a VRT first: gdal_translate in.tif in.vrt The resulting XML document should include all the relevant metadata, including the ...


1

If you save the Geotiff to png format using gdal_translate, an additional file with the extension.aux.xml will be written. It contains the metadata that your target format does not know. So it should be possible to process your image with ImageMagick, and use the information in the .aux.xml file afterwards.


1

And a free option is BR's EXIF extractor. Lovely little program that does the job well. It's been saying that it is in beta for a few years now. I've never had any issues with it. [edited to add link - Luke,thanks for the heads up]


1

U can do this task in eCognition. The process steps are Do segmentation; preferably multiresolution (of scale parameter 5) or chessboard segmentation ( of scale parameter 1; this will useful to understand the pixel values) Now in the Feature View, you can see the Object features >> Layer values >> Mean >> in which your uploaded image layers. Double click ...


1

The GDAL utilities are command line tools of convenience for standard geoprocessing tasks but if what you want is not there then you will have to write code to call the underlying API yourself to build your own tool. Here are a set of tutorials on using GDAL which will explain how to do what you want. However, I recommend that, unless you absolutely have ...


1

If by "using GDAL" you include writing code using the library as opposed to the more limited capabilities available only using the utilities from the command line, then the GDAL API tutorial walks you through all the steps to open a raster image, access its various properties (size, # of bands, rotation/skew, etc), and finally how to read and write a ...


1

Just landed on this query. Hope you have found the answer by now, or if not, here I guess is the answer. For a given roi object, say 'oroi', use this command to get the vertices >> oroi -> GetProperty, data=a so, here 'a' will have the X,Y,Z values of the vertices. Actually do 'a(*)+=0.5' to get the vertices values (at the center of the pixels). ...


1

You can also use GRASS for this work, I have found that it provides robust results for indices calculation when atmospheric correction is applied as per the modules.



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