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11

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 ...


9

The operative word is Resample, the version link is provided, similar links exist for prior versions of Arcmap. Also be aware of resampling issues associated with the resampling method and the nature of the data being assessed. EDIT If you want to go the reverse route (ie decrease resolution) see Aggregate or Resample but be careful to choose your options ...


7

ArcGIS 10 Animation http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/What_is_an_animation/000900000001000000/ ArcGIS 10 Temporal Data http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/A_quick_tour_of_temporal_data_management_and_visualization/005z00000021000000/ You can record either and export to either image (animated gifs) or ...


6

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 ...


6

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 ...


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

You will need a paid Licence of the ERDAS SDK as the free (gratis) SDK is read-only. As an alternative try the Geotiff format with JPG compression. For example, using the following two commands (but you have GUIs in QGIS, for example) to convert ECWs to TIFFs you get rasters that are more or less 30% bigger than then ECWs but look the same and are also as ...


5

I would like to add Block Statistics as another method to alter the resolution of a raster. Depending upon your specific goals, Block Statistics allows fine control of how pixels are assigned based on: A user defined neighborhood (e.g. rectangle, circle, wedge etc) The type of statistics calculated within each block (e.g. mean, majority, variety etc). ...


5

First of all I should mention that this question is addressing very limited space, though important question too. The first thing that comes to my mind on this subject that you can consider temporal information in short time interval. if certain values are changed in certain areas, it may be easier to detect snow. and second solution is here from National ...


5

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 ...


5

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, ...


4

To do this correctly you need to recover the NIR and visible bands (VIS). This is because, by definition, NDVI is the ratio (NIR-VIS):(NIR+VIS). To analyze the situation, let's use subscripts (1) and (2) to denote the two 16-day values and no subscript for the one-month value. Observe that NIR-VIS = NDVI*(NIR+VIS). Also, because the two time periods have ...


4

The simplest way is to create a new point layer for identifying individual cells which have been misclassified: for each incorrect cell, create a point at that location, and update the attribute table with the corrected value. Then, rasterize the points to a layer with the same extent and cell size as your input layer, then merge the two with a raster ...


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

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

Something like this should work for you:


3

I set my windows temp variables to a different drive so all temp not just ESRI stuff is redirected ("[winkey]+[pause] > Advanced > Environment Variables'). From there slap [new] to override the user profile temp or [edit] to change the system wide temp. I don't know if this is still true, but I've encountered some geoprocessing tools which would write to ...


3

If you want to use ENVI, it's straightforward. You can find out how to do it here Given that your datasets are univariate (NDVI, temperature, pecipitation), you may want to encode them into an RGB composite dataset and plot a single "map" for each timestep.


3

You could reference individual bands in Arcmap 10 by using the full path name and "\Layer_X" where X is the band you want (Eg: "D:\GIS\layerstacks\1993_stack.img\Layer_27"). So if I wanted to save the 27th band from the stacked images, I would write a simple raster calculator statement like "D:\GIS\layerstacks\1993_stack.img\Layer_27" * 1 and put it in ...


3

Not using Arc or ENVI, but another option is GDAL. You could also use gdal_translate with the -b option. The command would look something like: gdal_translate -b 1 input.tif output.tif Where the '1' is the number of the band you want to extract. The gdal_translate man page has all the other options you may need.


3

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 ...


3

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 ...


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 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 ...


3

Pending there are no errors in your data or calculations, values for NDVI will always fall between -1 and 1. NDVI values are calculated from reflectance, which is the fraction of radiation that is reflected by a given surface. It sounds like your NDVI raster might be in integer format, which means you'll have rounding errors (e.g. raster displays '1' instead ...


2

You could classifly your NDVI image based on the index pixel values from -1 to 1. Pixels that have a value of less than zero show no reflectance in the NIR band. Values from 0.1-0.2 are usually from soils that reflect in the NIR, and values from 0.3-1 (increasing in vegetation density) are dense vegetation canopy. Within ArcMap you could use the ...


2

To start you could use one of the ESRI map service (world imagery) basemap layers to georeference off of. Depending on the date of your aerials you will have to use natural features to georeference to (e.g. forest edges or stream channels).


2

You can use GDAL to convert ADF to ECW. The ECW driver is not provided by default in GDAL, you need to compile GDAL and link to the external ECW SDK libraries provided by ERDAS.


2

Since each individual photo has such a small footprint, you could use the open source photo-stitching software Hugin to stitch a few tiles together. I have used Hugin (not for aerial photos, but for landscape photography), and it's incredibly good at tiling images. Then you'd have multiple points per scene to georeference on.



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