One open source option for atmospherically correcting ASTER L1B products, in order to convert at-sensor Radiance values to Top of Canopy Reflectances, is GRASS GIS' i.atcorr module.
An implementation of the 6S algorithm in GRASS GIS
GRASS GIS features a dedicated module for the task in question called i.atcorr (in GRASS-GIS version 7 or in GRASS GIS vesion ...
When remote sensing vegetation, the time of year is very important. In most climates, vegetation has significantly more biomass (i.e., leaves etc.) during the summer, which means that it is easier for the sensor to discern the health of vegetation at that time of year. Two NDVI images of the same location from different times of the year may look different ...
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). ...
I know I'm late to the party. But here is my suggestion.
1) image size
If your 550GB originals are uncompressed you should convert them to jpeg compressed tiff files. Keep them indivually (not merged). You can compress using arcgis, gdal, whatever you like. Compression will get you to around 23GB. Do not create pyramids/overviews just yet.
To compress you ...
Thanks to @gene and https://geoscripting-wur.github.io/AdvancedRasterAnalysis/ I can now answer my question (copied and modified):
# create some raster data
r <- raster(ncols=12, nrows=12)
r <- round(runif(ncell(r))*0.7 )
# extend r with a number of rows and culomns (at each side)
# to isolate clumps ...
In general, there are two approaches to classification: pixel-based and object-based:
Pixel-based: Each spatial pixel is evaluated by itself against a set classification parameters. In this case, pansharpening the image will not help you at all.
Object-based / Segmentation: In this approach pixels are evaluated as groups and segmented into groups based on ...
You can easily download freely available ASTER Level 1B (at-sensor calbirated radiances) from the USGS EarthExplorer site. A simple quick search for a polygon roughly covering New Mexico returns > 100 results, but individual scenes are much smaller, so for a full cover of the state you'd have to stitch them together. USGS Glovis should have the same data, ...
GRASS GIS (open source, since version 6 with a new graphical user interface) offers
many image processing methods including:
Import of all common satellite, aerial and UAV data formats
Correction for atmospheric effects
Correction for topographic/terrain effects
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.
The sieve model is fairly straight forward and you can implement it in ArcGIS using "RegionGroup", "ExtractByAttributes", "SetNull" and "Nibble". It is good to understand how these methods actually work so, I encourage you to work through this method yourself. I do have a sieve function available in the Geomorphometry & Gradient Metrics Toolbox.
Reproducing the map example you provided is primarily a cartographic effort and requires very little analysis if you have already calculated NDVI. I would use the following workflow to produce the map similar to the one you provided a link to.
Collect the NDVI data to use in your analysis. In the example, they
use "Summer" 1989 to 2001. In your case, you ...
You need to provide more details! There are many "satellite's". If the data is 0-255 it is 8-bit and represents DN. In a processing workflow some analyst prefer to scale floating-point to 16-bit so, bit depth does not always reflect correction level. Since you downloaded the data from Earth Explorer and it is 8-bit it is certainly DN.
I do not know what ...
The image needs to be cut apart and re-mosaiced after adjusting the colors in each piece. This can be done.
As an example, I extracted the green band of the image. To make my work simple (the computing platform I am using, Mathematica 9, does not easily extract pixels along arbitrary polylines), I rotated it to make some of the image boundaries perfectly ...
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 ...
You can find the system requirements for ArcGIS 10.2 (the latest version) here and for ERDAS here.
The laptop you list more than satisfies the minimum requirements. For schooling you probably won't need a powerhouse and the machine you list will be more than adequate.
In the event that you want to upgrade here are some things to consider:
Processor: an i7 ...
ENVI has never been very good with formats other than the native bil and tif. I have seen the behavior you mention, but it is inconstant and dependent on how the file was saved into an img format. It would be good to know how you are saving the file. I find it very unstable to just give output an img file extension. Your best bet is use the "Save File as ...
Histogram matching works by forcing the histogram of one image to match as closely as possible the histogram of a second target image. I'm afraid that it won't work on a single image. (There is the exception of using Histogram Matching to force an image to theoretical distribution, like the Gaussian, but that won't help in this case either.) Also, I'm not ...
Assuming a spherical Earth of radius, R = 6,371,000 m,
and a latitudinal (N-S) arc length, a = 1m,
then the latitudinal arc angle, dφ = a / R = 0.000000157 rad = 0.0000089932 deg, but note what others have said about the fact that a longitudinal (E-W) arc length of 1m will have a longitudinal arc angle that increases as you move away from the equator.
r <- raster(ncols=12, nrows=12)
r <- round(runif(ncell(r))*0.7 )
rc <- clump(r)
#extract IDs of clumps according to some criteria
clump9 = data.frame(freq(rc))
clump9 = clump9[ ! clump9$count < 9, ] #remove clump observations with frequency smaller than 9
clump9 = as.vector(clump9$value) # record IDs from ...
ATCOR for Erdas Imagine will convert DN to true reflectance--this step is critical for change detection analyses. Then you can use DeltaCue add-on in Erdas Imagine to detect land cover change. More details on the DeltaCue add-on can be found here. Additionally, there is a fairly good instructional video on how to use DeltaCue to get you started.
I would ...
It is technically possible to use the pansharpening algorithm with different sensors, and all your tagged software have pansharpening tools (sometime . However, the quality of the outputs will depend:
1) on the pixel number ratio. In your case, it will be very large (15*15 = 225). IMHO this will be too large, in the litterature you hardly find successful ...
fiducial mark are used to define the coordinate system of the photograph. With film photograph, the paper moves under the objective and can get further distorted during storage and development, so you need to localise the image on each frame. On the other hand, digital sensors are fixed, so you don't need any mark on the image to define the coordinate system:...
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 ...
Using Erdas, the Sieve tool is located:
Raster tab > Thematic (Raster GIS group) > Sieve
Also, a widely accepted approach is to use GDAL's gdal_sieve.py,
describes as follows:
The gdal_sieve.py script removes raster polygons smaller than a
provided threshold size (in pixels) and replaces replaces them with
the pixel value of the largest ...
As you are trying to compare these images, the key is to do everything to make the images to be as comparable as they can be, so the actual changes can be seen.
Here are my steps:
Acquire Satellite images that are taken during the same time of the growing season, preferably peak biomass (see Christophers answer). Note, this might be some other time too, ...
Rescaling an NDVI or EVI from -1 to 1, to 0 to 1, uses the Rescale function (under Raster, Radiometric).
Clipping the top and bottom 0.5% is a percentage linear contrast stretch. To do this in ERDAS 2013, click on Panchromatic, then General Contrast. This brings up the Contrast Adjust window (seen below), and you can choose a variety of different methods of ...