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13

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 ...


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 ...


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 ...


6

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" > ...


5

have you tried the orfeo toolbox?


5

If your imagery is in ESRI GRID format the editing can be done with the ARIS GRID Editor for ArcGIS: www.aris.nl/grideditor_arcmap The ARIS GRID 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 ...


5

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

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

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 ...


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

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

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

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

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

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" ...


4

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 ...


3

Well from one image only, you can do supervised or unsupervised classification. Try a few times and see if results are good. Better way, the way I did it, was making orthophotos from images. Then I had footprint of the building so i filtered terrain from the image. Then I did classification of the pixels and created vector objects. If you have DEMs, or ...


3

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 ...


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 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

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

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

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

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 ...


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

Use LDOPE-1.7 (https://lpdaac.usgs.gov/tools/ldope_tools), using "create_mask". this function takes MOD35_L2 HDF and creates a cloud mask in hdf. use MRTSwath tool for projection/re-sampling/clipping and convert new hdf to GeoTiff.


3

You appear to be looking at a suite of software options. A way of doing it in ArcMap model builder using off the shelf tools could be: point to raster (ensure snap to raster environment is set.) Expand (by 1 pixel to create your block of nine) Extract by mask. This method assumes that your points are not so close that their masks overlap. If overlap is ...



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