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8

Think of the geometry. The incidence angle refers to the angle from nadir, or directly beneath the satellite, which would be 0┬░. As the sensor looks out to the sides from this nadir, the angle of incidence increases as does the fov (field of view). This is why the resolution decreases with increase in incidence angle. This illustration from the Sentinel ...


7

The project website hosts the MOD16 dataset on an FTP server. As FTPs allow directory listings you can easily download complete folders without having to click individual links. This can be done with most FTP clients - a popular one would be FileZilla. Just right click the folder you want and select download. edit: The question now specifies that only one ...


7

Sentinel-2 Level 1C data are expressed in reflectance with a scaling factor, not in radiance. You have to divide by 10000 to get the reflectance. In the preliminary products shown this autumn, you had to divide by 1000. The scaling factor is given in the xml file at the root of the product directory. <QUANTIFICATION_VALUE unit="none">10000</...


7

PDAL doesn't provide anything like FUSION's "GridMetrics" at this time. We've been interested in useful statistics or metrics that PDAL could compute for algorithm builders, but we haven't gotten around to implementing anything yet. It would be straightforward to implement a custom PDAL stage to compute these. It will be more productive to ask on the mailing ...


6

Your understanding is correct. Obviously, you aren't limited to just two points in time, but that is only a minor variation. Multi temporal information is generally used for change detection, but it also provides a good tool to take phenological information into account when doing vegetation classification.


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

I answered a similar question here You can download and clip a portion of the SRTM 30m DEM with one command with the elevation Python command line tool. Install it and perform the self check with: $ pip install elevation Check if you have all the dependencies installed (mainly GDAL tools): $ eio selfcheck Download and clip a ...


5

I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


5

I am the main developer of MGET. The first step in your problem is to obtain values of the covariates that you will use to fit the model to your 90 GPS points. It sounds like you want to use the 8 bands as your covariates. You need to add 8 fields to your shapefile (one for each band) and populate them using a tool such as Extract Multi Values to Points ...


5

Kogan (2004) (p. 2891) provides the following formula for the Vegetation Condition Index (VCI): VCI = 100 * (NDVI - NDVImin) / (NDVImax - NDVImin) where, NDVI = Smoothed weekly NDVI value NDVImin = Multiyear minimum NDVI value NDVImax = Multiyear maximum NDVI value As you know, NDVI ranges from -1 to 1 and functionally ranges from 0 - 1. VCI ...


5

I am not a specialist of orbits, but I'll try to answer. Given a theoretical overpass time on a sun synchronous orbit, the exact one is not that easy to determine, as it depends on a lot of factors. the theoretical overpass time is valid at the equator, and for the local time under the satellite track when it crosses the equator (which is called the ...


5

I would encourage you to investigate the spatial wavelet analysis (SWA) method. This is an automated object oriented approach used to identifying individual tree canopies. The method has the potential to identify both tree height and canopy diameter from LiDAR derived canopy height models. The output is usually composed of a table with tree centroid coords, ...


5

I've downloaded these granules and notice the same shift. It appears to be geographic processing error on ESA's behalf. I've never encountered a shift like this before. The image metadata is the same for both, which means some error occurred before the Level 1-C product. (the 100*100Km granules have already been processed, for more information look here) I ...


4

It's normal that azimuth and range resolution of SAR sensors differ, because they depend on different variables: The azimuth resolution (AR) of a SAR system is: AR=Length_of_antenna/2 The slant range resolution (SRR) of a SAR system is: SRR=(Speed_of_light*pulse_length)/2 The ground range resolution (GRR) of a SAR system is: GRR=SRR*(1/sin(look_angle))=...


4

There are many multi-look algorithms. At the most basic, the process reduces noise (or "speckle") in SAR images by averaging adjacent pixels. Often SAR processors allow the user to define some N x N window over which to average. However, other algorithms include using median values rather than mean values. For a comparison of other algorithms, see "...


4

Suppose that we have two images that we want to co-register or one image that we want to register to earth: First step is to remove the errors in each image both geometrically and radiometrically. Each image has some geometric errors due to: Earth rotation Scan time skew Aspect ratio Panoramic effect (bowtie error) Earth curvature These errors will ...


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

The intensity image should be used for calibration and subsequent classification of geophysical features. To radiometrically calibrate the intensity, use the Calibrate tool in the Sentinel-1 Toolbox (SAR Processing > Radiometric > Calibrate). The S-1 Level 1 GRD product includes several Look-Up Tables (LUTs) to convert intensity values into sigma or gamma ...


4

Update: 11/30/2015 -- Open data access is 'imminent.' Expect to see it in the next two weeks. I think they haven't released a date because they are not sure when everything will be calibrated and operationally ready. Though they are releasing sample/PR images on a regular basis, it seems from recent mission reports that everything is not quite in place ...


4

In Windows (run OSGeo4W shell): Scaling: for %i in (*.tif) DO gdal_translate -scale -2000 10000 -0.2 1 %i outputs\%i You might find recalculating instead better: for %i in (*.tif) DO gdal_calc.bat -A %i --outfile=outputs\%i --calc="A*0.0001" --NoDataValue=0 In Ubuntu looping through files is slightly different: for i in *.tif; do gdal_calc -A $i ....


4

No, the NDVI threshold value will not be the same for the time series due to differences in phenology and unique conditions on the ground. As Kersten mentioned in the comments, you may want to consider using Global Forest Watch data, which is well respected in the environmental community. You have uncovered one of the limitations of working with ...


4

fiducial mark are used to define the coordinate system of the photograph. With film photograph, the paper moves under the objective and can get further distorted during storage and development, so you need to localise the image on each frame. On the other hand, digital sensors are fixed, so you don't need any mark on the image to define the coordinate system:...


4

Pan sharpening is a well documented image processing technique and you will find bunch of HowTo's and tools in the web (GDAL, ArcGIS, Orfeo TB, Grass, ENVI). On a common data oriented processing approach using Geotiff for example, you can use open source software in 3 steps: Synchronize the PAN and the RGB (or multichannel) stuff spatially which means, ...


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 ...


4

The most often used method that I've encountered in the literature involves a "local maxima" identification and subsequent inverted watershed creation. This link gives one example using LiDAR data and the free USFS FUSION software A simple Google scholar or other database search for "local maxima tree canopy" will yield many other peer-reviewed remote ...


4

the anomaly has been identified and is currently under investigation. It is not systematic as far as we know. Please report this kind of anomaly to the Copernicus help desk. Thanks S├ębatien CLERC S2 Mission Performance Center


4

You can download Sentinel 2 Imagery by going to the Sentinel Data Access Portal and selecting the Sentinel Data Hub Once you have arrived here select Scientific Hub In the top right hand corner select Sign Up (Enter all your info and verify the email they send you) Login to your new account. Begin your search by using the search bar and type in "...


4

In optical remote sensing in the visible spectrum you cannot see through clouds. So there is nothing you can do, except to wait for images without clouds. Cloud masks are (as far as i know) used to exclude clouded areas from (for example) landcover classification, because results there would be incorrect anyways. edit As Aaron mentioned, you can sometimes ...


3

I would strongly recommend you use the MCD43 product, instead of calculating the albedo product by yourself, for a couple of reasons: 1) Albedo products are not that easy to calculate. Based on your previous question asking if that could be achieved by using Band Math, i'd assume you, at least at that time, didn't fully understand the algorithm that goes ...


3

You could look at clustering in scikit-learn. You will need to read the data into numpy arrays (I'd suggest rasterio) and from there you can manipulate the data so that each band is a variable for classification. For example, assuming you have the three bands read into python as red, green, and blue numpy arrays: import numpy as np import sklearn.cluster ...



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