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16

IDL is a fantastic stand-alone programming language (you do not need ENVI). I particularity like it for very fast matrix processing on large arrays. @Aaron makes IDL sound much less flexible then it really is. The majority of IDL development came out of the Physics and Astronomy communities. There is robust support for mathematical and statistical ...


14

As Chad Cooper mentioned, what you want to perform is called Object-Based Image Analysis (OBIA). It's a fairly complex process which segments and then classifies an image. There are many programs out there which will perform this for you. However, you will require high-resolution, multi-spectral imagery. Incorporating LiDAR will probably help you out ...


13

From a remote sensing perspective, the main benefit of IDL is that it extends the capability of ENVI similar to how the Python arcpy site-package extends the functionality of ArcGIS. If you will not have access to the ENVI platform, consider learning a different programming language. Additionally IDL is a commercial product whereas Python is open-source and ...


7

Erdas used to work together wih ESRI, but now it is ENVI that has joint its forces. I would therefore use ENVI for the compatibility. But if you are looking for an good open source solution, I recommend Orfeo Toolbox (http://orfeo-toolbox.org/otb/ ). You can either use the library, the command line application or a complete GUI (called Monteverdi). ...


6

Manually using a stereoscope with a high enough resolution image, it is possible to estimate stand height. Although Landsat has a 30meter resolution and is too crude to estimate tree height. (LIDAR data would be necessary if your set on using Landsat) Depending upon the species your trying to measure NAIP imagery may be a better option, with a 1 meter ...


6

you can find the H and V index in all MODIS product file name. These indices refer to the grid below (from the MODIS Website). For instance you have H8V6 (MOD17A3.A2000001.h08v06.055.2011276103801.hdf).


6

There is a new, 30 meters resolution SRTM DTM coming out. As stated on the NASA JPL official page, The next release is planned for later in 2014, and it is expected to include all of South America plus North America south of the United States. It is incomplete, for now, it has only limited coverage. You can read an article about SRTM coverages here ...


5

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 ...


5

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 ...


5

Since Landsat satellites are not placed a true polar orbit -- they are in a "near polar" orbit -- their heading (azimuth) is never zero. See NASA's Landsat Handbook and Landsat Science. It is closest to zero at the equator (8.2°) but deviates from this the closer it gets to the poles. Thus, yes, knowing the center coordinates (latitude, actually) of the ...


5

Form the (i)python basis to the more complex manipulation: Dr M. Disney - Introduction to image data handling These two blog have many examples: Luca Congedo - From GIS to Remote Sensing REMOTESENSING.IO Things became more interesting with more spectral bands: http://www.spectralpython.net/ Another book about this topic: Image Analysis, ...


5

As someone who did feature capture from imagery for a while, I would caution you against expecting a pool at a spring. The majority of the ones I've encountered (both in capture and on the ground in person) don't have one. We often referred to ancillary sources to suggest/confirm a spring. Depending on your purposes, USGS quad sheets or hydrography datasets ...


5

those are different things. Image classification is the process of creating a thematic image where each pixel is assigned a number representing a class (can include the class 'unclassified'). In an aerial image the classes can be soil, vegetation, water etc. image classification algorithms examples are k-means or ISO-DATA. Pattern recognition is the ...


5

This varies greatly on the characteristics of the scene. Fire scar mapping studies using Landsat-5 TM have used the following three band combinations: Spain: Bands 4, 5, 7 CHUVIECO, E., and CONGALTON, R., 1988, Mapping and inventory of forest fires from digital processing of TM data. Geocarto International, 4, 41–53. Amazonia: Bands 3, 4, 5 PEREIRA, ...


5

I've been in LiDAR processing for a couple of years now. The best approach we've found is to classify the suspect water points to something other than ground. Should be easy just classifying based on intensity (near nadir points will have high intensity, whereas turbid water will be close to 0) and laser shots are usually absorbed near shore anyway. ...


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 ...


4

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 ...


4

If you look at the product page at LPDAAC, under Layers there is a table that lists each of the bands in the dataset and their characteristics. For the NDVI layer, it is a 16-bit signed integer with a fill value of -3000, and a valid range from -2000 to 10000. However, there is also a scale factor of 0.0001, or 1/10,000. This means that a value of 10000 ...


4

As you've probably found, removing clouds from Landsat imagery is not a trivial problem. There is a decent amount of scholarly research about methods for implementing masking and correction for clouds and their shadows. So due to the complexity of this issue, you probably won't find any one size fits all solutions that work perfectly out of the box. That ...


4

You cannot do this in any reliable fashion. The reason is that every MODIS product (like LST) is created from a number of observations (basically, the MOD01 radiance product) and you do not know how that is done. The MOD11A2/MYD11A2 products do not give you the number of observations that go into each product, nor the extremes. But if you really want to do ...


4

there are a few examples of animal counting by remote sensing (whales, gnu, crocodiles, seals...), but they used higher resolution satellite images (<1m) or aerial photographs (see this paper) and there was a clear spectral difference with the background (sometimes in UV or infra-red)). As a rule of thumb, you should have around 10 pixels to detect an ...


4

You can try 'lasoverlap.exe' to quickly both visualize and quantify the overlap. And you can use 'lasoverage.exe' to remove the extra layers when there is overlap among points based on the scan angle. Both these modules are part of LAStools, which come with a toolbox for both ArcGIS and QGIS. Attached is a visualization of an example visualization produced ...


4

Not that I know. Sometimes the sensors are named and then the definition change (e.g. Advanced Very High Resolution Radiometer would not be called VHR anymore, but it was in 1978) Your definition is quite practical but does not tell you what you have between the two ranges (e.g. 15 bands like MERIS, Rapideye red Edge is 40 nm... I would put those two in the ...


4

Landsat and Modis are optical sensors, which means that they provide digital numbers of reflected materials that are within the electromagnetic spectrum. These values correspond to the wave length of the corresponding satellite band. To get elevation from just the raw values would be impossible. The only potential means to collect elevation information would ...


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 ...


4

I just finished writing a script to accomplish this task using the free and open-source GIS Whitebox Geospatial Analysis Tools (download here), for which I am the lead developer. The source code of the script can be found here. Although the script is not yet part of the current official Whitebox release (v. 3.2.1) you can get an early version of it by ...


3

Penn State offers a wide variety of free online classes (for no course credit). You can take a look at the Penn State Online Geospatial Education Program Class Calendar. From there you may want to take a look at: Geog 883: Remote Sensing and Image Analysis and Applications: An intermediate-level course focusing on the use of remotely sensed imagery in ...


3

You could square your differences, and then normalize them to the range from 0 to 1 by dividing with the largest occurring value. This can be done using the Raster Calculator, which is documented here: http://resources.arcgis.com/en/help/main/10.1/index.html#//009z000000z7000000 . Depending on what you want to do, you can then define a threshold with a ...


3

For specific remote sensing tasks you could check out BEAM. If you are not afraid of command line, I would suggest a combination of GRASS (for storage and datahandling and analysis), QGIS(for visualization) and GDAL/OGR and pktools (for analysis). All these are open-source. A very good instructional site is here.


3

Idrisi Selva through the Clark University Lab is an amazing alternative for image processing. I think there are ArcGIS plug-ins for it as well.



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