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8

Please mention the sensor of Landsat 5, is it MSS or TM? Assuming it is Thematic Mapper data, you have visible red and shortwave infrared data. You can directly infer from the band reflectance values about where vegetation patches lie and hence moisture content. Band 3 (Red) can help you discriminate vegetation slopes and Band 5 (SWIR) can help you ...


5

Consider integrating DEMs into your research on soil moisture/exposure. I have used the following indices in the past for regression models (Davies et al. 2010): Site exposure index = slope∗cos(pi∗(aspect−180)/180) (Balice et al. 2000) Heat load index = 0.039 + [0.808 * cos(l) * cos(s)] – [0.196*sin(l)*sin(s)] – [0.482*cos(a)*sin(s)] (McCune and Keon ...


5

The solution, if the driver suuports it, is to call GDALOpen() with GA_Update access then use GDALAddBand or GDALDataset::AddBand. However, the geotiff driver doesn't support AddBand.


4

Though I am not able to understand the difference between the standard deviation output and the percentage output and what is the significance of using one over the other? Those refer to the threshold used to decide whether there has been any change between two images. For percentage change, it uses a symmetric relative difference formula to ...


4

From the USGS FAQ: the blue band is useful for "Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation". It's my experience that you get better results by using band combination, however.


2

Enable the GDALTools plugin (Plugins->Manage Plugins... menu) and use the Merge tool (Raster->Miscellaneous menu) and tick the Layer stack option.


2

If you double click on your raster when you add it, it will become possible to select a single band. You can then export this single band using "right click" export data, but this is not necessary.


2

Individual bands can be accessed by calling GetRasterBand(4) from your datasource. You could then write your band as array into a newly created copy. For instance like this: driver = gdal.GetDriverByName("....") tDs = driver.Create(output, cols, rows, 1, gdal.GDT_Float32) ds_in = gdal.Open('in.tif') array = ds_in.GetRasterBand(4).ReadAsArray() # get ...


1

You will want to use the following type of expression: ("YOUR_IMAGE@1" > 150) * "YOUR_IMAGE@1" The resulting image of an arid woodland shows only the pixel values > 150 and all other values are displayed as 0 (black).


1

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 ...


1

As others have mentioned, the best practical way to determine which bands are which is to look at the source metadata, which is widely available for common products like ASTER and Rapideye. You can also derive much information about the bands doing a little legwork in Erdas. This is a useful skill to have if you are given, for example, a Rapideye image ...


1

Look here for Aster info. VNIR_Band1 = 0.52-0.60 (green) VNIR_Band2 = 0.63-0.69 (red) VNIR_Band3N = 0.76-0.86 (NIR) Rapid eye comes in GeoTiff format comprised of 5 bands (listed here). The images that you have are probably composites. RGB is Red, Green, Blue that represent RGB, NIRREG (possibly NIR and near edge), and color infrared (CIR). In CIR, NIR ...


1

In order to know the wavelengths or colors that bands in a raster represent, you typically need to know what instrument they were created with. Some software can make automatic assignments based on the file format, but the labels might not always be clear. I recall doing some work in ENVI where it would give you the wavelengths but not the band numbers. I ...


1

For your Aster Image, 1 is green, 2 is red and 3 is near infra-red For RapidEye, 1 is blue, 2 is green, 3 is red, 4 is red edge and 5 is near infra-red. So your second image is Near Infra-Red/RedEdge/Green Usually, with 3 bands the image is either RGB or NIR/R/G. When I don't have a clue, I try 321 and 123 composites. With RGB one of the combination ...


1

The tool you want is Composite Bands. Assuming the original was MultiBand with 6 bands, use band 1, 2 and 3 from the RGB and the other 3 bands to combine into a single 6 band image. This tool will re-stack the bands into a single raster, so then you can use the result image with different band combinations (like 5, 4, 2).


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

I would suggest to build a virtual Dataset with gdalbuildvrt, where you can specify to add an alpha channel with the -addalpha parameter for the 3-band-datasets. http://www.gdal.org/gdalbuildvrt.html Alternatively you can use gdal_translate: gdal_translate -of vrt -expand rgba Or have a look at the last example of the manpage of gdal_translate: ...


1

If the output file format is not geotiff, rgb2pct.py creates an intermediate geotiff to write the results into before converting that to the final output format. The comments in the code state: # Create the working file. We have to use TIFF since there are few formats # that allow setting the color table after creation. From lines 127-129 of ...



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