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14

I don't think there's a really simple way to do this, but one way would be to: Create a new polygon layer and create polygons over the areas you want to change the values of. Code the polygons with the desired land cover value. Convert the shapefile to a raster. Use the Raster Calculator to substitute the new values. Con(("POLYRAST" > 0),"POLYRAST","...


14

I have used OpenCV in the past to train for object detection for geo. Orfeo Toolbox is a good open source choice as Vascobnunes pointed out. For a closed-source version, you can take a look at Feature Analyst (that also has an ArcGIS extension). At the end, it boils down to training a support vector machine. There are several libraries that you can use for ...


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 ...


11

What you are looking for is a Mathematical Morphology application, Closing to be precise. If you use GDAL to read your image into a numpy array there is a number of libraries that support this operation. scipy.ndimage is one of them and has a function for binary hole filling. In python for a fictional binary dataset as you outlined this would transform the ...


8

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 ...


7

I am afraid satisfying roof detection cannot be achieved with only one single satellite image. You should try to use other sources of information. The following article describes a method using a DEM + aerial image pairs + cadastral data: M. Durupt, F. Taillandier. Automatic Building Reconstruction from a Digital Elevation Model and Cadastral Data: An ...


7

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 ...


6

The editing can be done with the ARIS Grid & Raster Editor for ArcMap: www.aris.nl/gridrastereditor_arcmap The ARIS Grid & Raster Editor adds a toolbar to ArcMap. This toolbar provides a set of tools to change the value of one or more cells. With these tools it is possible to: change the value of a single cell or pixel (pencil) draw a free line (...


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 ...


6

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 ...


6

GDAL's ENVI driver can be used to write headerless binary data files. The default data interleave is band sequential (BSQ), but BIP or BIL interleave options can be specified as a creation option. For example, to convert a GeoTIFF file foo.tif to a headerless file foo.bin: gdal_translate -of ENVI foo.tif foo.bin The ASCII file foo.bin.hdr will also be ...


6

One flaw in your approach. You don't need to go through DN to radiance. You can go straight to the DN to reflectance. Just stick to ((B1*0.00002)-0.1)/0.74457226676389733207607359928648.


5

have you tried the orfeo toolbox?


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

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 ...


5

I encountered similar issues as well with polygons. Maybe you have a similar problem. Error Message by ESRI: "Invalid Topology (Incomplete Void Poly)" Actual Error: "Invalid Geometry" Fix: Run "Repair Geometry" (changes data in-place, be careful, there is no undo) What happens is that the error reported is not using the ESRI terminology of Topology/...


5

"Starting with GDAL 1.10..." "I am using the Python bindings with GDAL 1.9.2..." GDAL 1.10 hasn't been released yet. Beta 1 was released a short while ago or if you're using Windows, you can grab a build of the current trunk (1.10dev) from GISInternals. If you're stuck with 1.9.2 for a while, here's some code to parse envi headers (envi.py) Some ...


5

The simplest option would be the use of Raster Calculator with a conditional statement. Your statement may look something like the following with 'threshold' replaced with whatever value you like. The resulting raster will give you pixels with value=1 where values are greater than threshold, and NoData where values are less than threshold. Con("NDVI_img" &...


5

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, ...


5

Step 1: Convert from digital numbers (DN) to radiance This is done by applying the multiplier and addition numbers as found in the metadata (.MTL) file. For the thermal bands (B10 and B11), the values are usually, but you should check the file: Add: 0.1 Multiply by: 0.0003342 (3.3420E-04) In ENVI you can apply this correction using 'band math': float(b10)*...


4

based on my experience, envi ex is good if you don't have much time and need the data "quickly", if you're using good resolution rasters. in low-res rasters, the regular version, imho, is much better, because you have a better control of the procedure. if you try the various extraction methods with the same raster in envi and envi ex, the results you obtain ...


4

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.


4

Just in case ArcMap doesn't work, you might want to try GRASS' v.clean tool. You can install GRASS with QGIS. After installing: Cleaning of topology of a SHAPE file using the GRASS Toolbox Load the SHAPE file into QGIS Use existing GRASS mapset (or create a new one) with matching projection settings Now you have to transfer the SHAPE file ...


4

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 ...


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

In ENVI 5x (the procedure is similar in ENVI 4x or ENVI 5 Classic), use the File > Save As menu to save your TIFF to ENVI format which is a flat binary file. This should default to BSQ, but if it doesn't you can convert using the Convert Interleave tool. You also get header (.hdr) and pyramid (*.enp) files, but you can delete those.


4

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 ...


4

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 ...


4

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 ...


4

30m - 45m is a lot of change in a coastline over one year and only very dynamic areas see that kind of change rate. As such, you are correct in your assessment of the impact of imagery resolution on the analysis. However, your assumption about "same time of day, so tides were more or less the same" is not a very good assumption as tides are more variable ...



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