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8

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 ...


7

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 ...


7

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 ...


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

In arcgis10 when you add images to a raster mosaic dataset there are options to create footprints and metadata.


6

Calculating NPP from EO data is an open research question. I will assume we talk of the land surface here, by the way. A simple and widely used way of calculating NPP is to use what is called a Production Efficiency Model, that converts incoming radiation into gross primary productivity and then subtracts respiration costs to arrive at NPP. There are many ...


5

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")) Ras1.save("Ras1") This will replace Ras1 with your new raster. If you ...


5

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 ...


4

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 ...


4

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, ...


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.


4

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.


4

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 ...


4

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: http://library.columbia.edu/indiv/dssc/eds/georef.html Then you can use the values in Excel to ...


4

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 ...


3

I'm not sure I understood your question. If you're asking if these programs have the technical ability, then, YES, ENVI, ERDAS and ArcGIS are good for calculations of AREA with a specific spectrum (given that you have as input a good aerial photo or sattelite image, with the correct bands). However, the conversion from AREA to MASS is something that (as ...


3

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.


3

I would recommend calculating soil moisture indices from Landsat TM bands. MTRI has an interesting article on creating soil moisture index (SMI) from Landsat TM 5. Also, I would recommend exploring soil moisture estimates using TM band 6 (Thermal IR). Attached is a good tutorial on calculating indices from Landsat TM bands using ArcGIS 9.x (as you ...


3

If you have gdal command line setup you can try this: gdal_translate -outsize xsize[%] ysize[%] <src_dataset> <dest_dataset> Example: creating 25% of original image. gdal_translate -otusize 25% 25% input.tif output.tif ......


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.


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

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

I have not tried it but Opticks is worth a shot: http://opticks.org/confluence/display/opticks/Welcome+To+Opticks


3

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 ...


3

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 ...


3

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, ...


3

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 ...


3

JPEG2000 Pros: Better compression than JPEG Supports both lossy and lossless compression, JPEG is lossy only Supports any number of bands, JPEG only supports 3 bands Supports more datatypes (including floating point), JPEG only supports 8 bit (byte) data Internal precomputed multiresolution representation (aka pyramids) JPEG2000 Cons: Limited ...



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