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In general, there are two approaches to classification: pixel-based and object-based: Pixel-based: Each spatial pixel is evaluated by itself against a set classification parameters. In this case, pansharpening the image will not help you at all. Object-based / Segmentation: In this approach pixels are evaluated as groups and segmented into groups based on ...


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Most surfaces reflect anisotropically, so their reflectance varies with the angle between illumination and viewer (ie, Sun and MODIS sensor). MODIS's wide swath means that it is able to view every location on the ground every day, albeit from different orbit tracks (which have a 16-day repeat interval). Observations recorded from these sets of 16 different ...


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Two of the best commercial high resolution multispectral products available are Worldview-2 and Worldview-3. These sensors are commonly used for natural resources and biodiversity applications. You can learn more about these products here. Another more cost efficient option is to use RapidEye medium resolution imagery (details). Of course, if your budget ...


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The exact release dates are as mentioned below. November 2014: Mexico, South America January 2015: Australia, Southeast Asia April 2015: North America, Europe July 2015: China, Japan September 2015: Northeast Africa source: http://www.digital-geography.com/announcement-realease-worldwide-high-resultion-srtm-data-30m/#.VPWT3_mUeUY


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This is a really good website giving a summary of a couple techniques the writer tried and which one gave him the best results: https://blamannen.wordpress.com/2011/07/12/destripe-landsat-7-etm-some-thoughts/ The basic premise is what Chris W said. You're going to have to interpolate the missing values by using the lines above and below the missing data and ...


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The biggest data provider is DigitalGlobe. They also have the arguably best satellite (WorldView-3). You can buy directly from them, or you can go through one of the many resellers. A price of around 16$ per sqkm is usual for 4 spectral bands and 20$ for 8-bands - link to list with prices. A slightly cheaper option is Pleiades and SPOT data from Airbus ...


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You should do a Supervised classification. Where you have defined polygons you can convert them to a centroid. Depending on the number of plants that touch, you will process them differently. Take note, that from experience doing classifications from RGB imagery for vegetation produces poor results.


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It is maybe not really the answer but I canĀ“t post it as comment... @Mr. Che I tried to calculate Forest Index following paper Wentao Ye; Xi Li; Xiaoling Chen and Guo Zhang: A spectral index for highlighting forest cover from remotely sensed imagery", Proc. SPIE 9260, Land Surface Remote Sensing II, 92601L (November 8, 2014); doi:10.1117/12.2068775 so as ...


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It's no so elegant, but renaming LE70010752014040CUB00_B6_VCID_1 to LE70010752014040CUB00_B61 do the trick. If there's a way i can do this with patterns it'll be better.


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The RandomForests algorithm is often used in forestry. There are two implementations of the randomForests algorithm that I regularly use. The first is a pixel-based classifier implimented in R using the randomForests package. I believe this is most sophisticated and flexible approach you are likely to find. There are a many resources to get you started ...


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Satellite imagery is generally not labelled according to potential mapping scale, due to the indirect relationship between the two concepts. WR Tobler's rule of thumb is "divide the denominator of the map scale by 1,000 to get the detectable size in meters. The resolution is one half of this amount." For your scales, that would mean 2.5m resolution imagery ...



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