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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 ...


7

You could try something like this: import os, arcpy, glob from arcpy import env, sa from arcpy.sa import * check = arcpy.CheckOutExtension("Spatial") print check env.workspace = r'F:\Mosaic 2000' ws = env.workspace wsf= r'F:Mosaic 2000\Reflectance' rasters = glob.glob(os.path.join(ws,'*.tif')) bands = [['band1', 0.0508], ['band2',0.0254],['band3', ...


7

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.


3

In short, yes. You can do that. The sensors in most cameras are sensitive to light from UV to IR. To change the information into the standard RGB, most cameras use a Bayer Filter (see the Bayer Filter wikipedia for more info on how this is done) approach to filter the visible light into red, green and blue, while throwing away UV and IR information. As ...


2

The Geoprocessing tool that will extract out a single band from your three band raster is the Make Raster layer tool. You would set the band index parameter to the band you wish to extract. This is a quick and easy way to extract the band and as its a tool you could embed it into a models workflow. Note its not a permanent layer so you would need to save it ...


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

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 ...


2

With gdal_calc.py is, for example, --type Float32 (my answer based in your link). The next command worked for me when I used to calculate at-satellite brightness temperature: gdal_calc.py -A b6.rad.tif --calc "1260.56/log((607.76/A)+1)" --type Float32 --outfile bright_temp.tif I hope that helps.


2

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


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 ...


2

This is something you can achieve with a Virtual Raster (Catalog). This will create a metadata file (.vrt) that QGIS treats like a merged multi-band raster without having to merge all the bands. Raster --> Misc. --> Build Virtual Raster Select the bands you want to use as "Input files" Check "Separate" to put each input file into a single band (otherwise ...


2

If you want a binary layer wth values before or under 273, you can use the build mask tools (basic tools > Masking > build mask) in IDL, you can use the following relational operators gt for > ge for >= eq for == le for <= lt for <


1

you can apply your conditions to each individual band then create a composite band. This can be done on the fly (without storing the intermediate images) if you use the functions of the image analysis window.


1

You, in theory, can do this but I would question the reliability and replication of this approach. Most digital cameras are not calibrated so, it would be quite difficult to standardize the imagery to make them directly comparable through time and space. If you only intend to acquire a single image or are not planning on comparing data, there would be no ...


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

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 ...


1

To expand on Luke's answer and provide a concrete example in Python, here's a snippet that adds an alpha band to a source raster and saves it as a PNG. from osgeo import gdal src_ds = gdal.OpenShared(input_path) mask_ds = gdal.OpenShared(mask_path) mask = mask_ds.GetRasterBand(1).ReadAsArray() tmp_ds = gdal.GetDriverByName('MEM').CreateCopy('', src_ds, 0) ...


1

I would recommend using the raster or rgdal package(s) to read tiff files. This will also set you up for success in your second question, which not only needs to be a new question but also needs more than "how to locate pixels". In the raster package you can use "raster" for single band or stack for muntiband (e.g., RGB) tiff's. In rgdal you can use ...


1

The exact answer is that the number of bands in a raster is stored in a 2-byte integer, so 65536 (indexes 0 to 65535) possible bands. There is one additional restriction which is the maximum band index value supported for out-db rasters and that is 256 (indexes 0 to 255).


1

I suspect things have changed since those docs were written. If you look at the table definition by running \d raster_columns from a psql prompt, you will see: View "public.raster_columns" Column | Type | Modifiers ------------------+--------------------+----------- r_table_catalog | name | r_table_schema | ...


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

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).



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