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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

These values are scaled Kelvin. You can see in the MODIS website detailed specification about the MOD11C3 data If you multiply the raster by 0.02 you should get the values in Kelvin


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There is no definitive way to determine the bands without vendor metadata. However, you can infer which bands are which by looking at a spectral profile of image reflectance values at features such as water and vegetation. For example, near infrared values in your imagery will be very low for water features and very high for healthy green vegetation. The red ...


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Looking at their site, they clearly state that: Circa year 2000 Landsat 7 cloud-free image composite (first) Reference multispectral imagery from the first available year, typically 2000. If no cloud-free observations were available for year 2000, imagery was taken from the closest year with cloud-free data, within the range 1999–2012. Circa ...


2

I've finally found a tool that does this. This task is referred to as mono-photogrammetry or monoplotting and involves referencing a single oblique and unrectified photo to a DEM to produce georeferenced data for use in a GIS. This is similar to photogrammetry, except you only have a single image. The WSL Monoplotting-Tool is specifically designed for this ...


2

Answer from LPDAAC: It goes from 0000 to 1111, with 0000 being the best and 1111 worst. For some reason the table on the product page wasn't complete. To understand what it means and how it could be used you need to understand how it is constructed. Basically we start by assigning a VI_Usefulness of ZERO (0000) and then decrease it based on ...


2

This may be easier using the Orfeo toolbox (https://www.orfeo-toolbox.org/), this is provided with OSgeo4W and can be accessed usign QGIS or a command line interface. This tutorial uses mean shift segmentaion to generate objects, which can be the classified using SVM/random forests etc. ...


1

I don't think so. Landsat 8 range values are from 0 to 65536 (8 bits: 28 = 256; 16 bits: 216 = 65536) But usually values are between 4-5K and 21-23K.


1

Yes, the difference is correct. The reason for the difference between the two is the increased bit-depth of Landsat 8 (16 bit), when compared to Landsat 7 (8 bit). By calibrating the data to top-of-atmosphere reflectance the difference between the images should be minimal.


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The usual library is ASTER. For application of that, and background, I suggest Baldridge, A. M., S.J. Hook, C.I. Grove and G. Rivera, 2009.. The ASTER Spectral Library Version 2.0. Remote Sensing of Environment, vol 113, pp. 711-715. For water, do you want liquid or frozen water? Sea water or fresh? Distilled or tap? In any case, start at ...


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If you use the latest version of QGIS, it has no problem reading and displaying the tif files. You need to extract the .tif.gz files to .tif in advance using your operating systems tools. Then add the .tif to the canvas: You can extract a subregion with Raster -> Clipper by dragging a rectangle on the screen, or specifying a bounding box in the map ...


1

There are some studies and pilot projects about this kind of cattle detection: Please see the links: https://www.itc.nl/library/papers_2012/msc/nrm/zhengyang.pdf http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0085239#pone-0085239-g001 so the answer is yes, it's feasable to detect.


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Arc GIS have introduced the image classification tool in version 10. If You want to classify the images using Arc Map. Check this tutorial of the tool. You may also use other softwares like ENVI or ERDAS IMAGINE for this purpose. For the case of ENVI this tutorial guides you through the process and can perform both supervised and unsupervised ...


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The answer is option 2. As also documented in this post there are undocumented QA-values in the MOD13Q1. I have yet to find a good reason for the lack of documentation, and I expect that you'd have to directly contact the LPDAAC people for a correct answer. Note that in the other post, the observation can be found in the comments-section.


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There isn't much out there in terms of MOOCs for remote sensing. The most recent offering i have come across was from The European Space Agency - Monitoring Climate from Space. The next session of this MOOC starts on the 25th of July 2016. Week 1 - Observing Climate Change from Space What is Earth observation? How do we observe the Earth with satellites? ...


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The US Naval Research Laboratory has developed the "Special Sensor Ultraviolet Spectrographic Imager" (SSUSI). Versions of it have been aboard DMSP satellites since 2003, improving in accuracy. It records in the extreme ultraviolet and far ultraviolet spectral ranges. The most recent numbers I could find (from 2011) said it has 7km resolution and records ...



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