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 ...
You can't 'remove' clouds from optical imagery, what you see is what you get; they are photographs and there is no optical data recorded from below the clouds in the same way that there is no data underneath building roofs.
If you use remote sensing data of a longer wavelength than light such as microwave, the water particles in the clouds do not absorb the ...
I will have to 2nd @blah238's suggestions of using some other method of data access than creating a single mosaiced image. A simple guess would say there is not a desktop out there that could handle the amount of data you would have to process in order to mosaic all of those tiles.
To break it down, there are probably two places where you are running out of ...
There is no way to get floor heights from a lidar pointcloud. Lidar is captured by bouncing lasers off the groundsurface and measuring the bounced back pulses. Therefore there is no way for the lasers to 'see' through the roof of a building and return a floor height.
However, a solution to this may be to classify your las point cloud into ground and non ...
To begin, you need to know the which spectral bands are which in your base image. NDVI is calculated from reflectance rather than radiance or DN. Therefore you will need to make sure your imagery has been converted to express reflectance. The equation to calculate NDVI is as follows:
(near infrared - red)/(near infrared + red)
If you are using LISS IV ...
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). ...
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 ...
Do you have access to spatial analyst?
If so, the Con function will do exactly what you want. Create a "condition" raster that is 1 where you want the values changed to Ras2 and 0 everywhere else.
Execute the statements:
Ras1 = Con(Raster("condition"), Raster("Ras2"), Raster("Ras1"))
This will replace Ras1 with your new raster. If you ...
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 ...
Here are the steps on how to do it in ArcGIS (Extract from online help)
In ArcMap, click the Add Data button. Navigate to the location of the HDF file. Click the file and click Add.
The Subdataset Selection dialog box opens. Click a single subdataset to add. Optionally, press and hold the CTRL key to select more than one.
If you choose more than one item, ...
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 ...
550gb of input TIF data is easily handled by a single ECW file. We have many customers compressing much larger datasets than this so please do not think the format is not capable in this area.
Your strategy of splitting the project into small tiles to minimize null area is also a good approach to take with the current format version as it will reduce the ...
Most MODIS QA data (including the Cloud Mask data) are not stored as separate raster bands, where each band is a grid where each cell is one value of one QA data field. Instead, the QA data are concatenated into strings of bits. So instead of having Band 1 be 00 and Band 2 be 11, they just concatenated them (right-to-left) as 1100 which is a completely ...
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, ...
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.
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
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 ...
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 ...
If you know the image size (pixels) and scale you can work out the top left corner from the centroid. You can use Excel to do the math.
Then create a txt list and create world files for each image.
I would do one manually in ArcGIS.
To georeference one see:
Then you can use the values in Excel to ...
In model builder,
iterating rasters from a workspace,
constructing raster file path and file names
feeding the path into Raster Calculator and extend file names with the desired band ID.
defining an output path
This may help.
Although its clearly better to use one of the other options mentioned you could try the following:
gdalbuildvrt index.vrt *.tif
gdal_translate -of "GTiff" -co "COMPRESS=LZW" -co "TILED=YES" -co "BIGTIFF=YES" index.vrt out.tif
This builds a GDAL virtual format and then convert to a single GeoTiff.
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
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.
you can try doing object based classification based on size and signatures of the vehicles and look at the results. Then you can remove vehicles from the image. Afaik, there is nothing that will do it in one click.
There is nothing built into the software that can solve differential equations. I am assuming that your values are derived from spatial data or you would be posting this on another site. Your best bet (if you are tied to one of these software packages) is to write some code in ArcPy that pulls in your values and does the math.
The NumPy Python library, ...