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1

What camera are you using- and what bands? (Sounds like the micasense) You may want something that reaches towards the SWIR to see water characteristics. Anyways, from what I know- it is best to pair thermal imagery (measuring canopy temperature) with atmospheric temperature, and use something like the crop water stress index (CWSI) - see paper by Bellvert ...


4

There are other options to download Landsat7 images. Here's what I do: Go to EarthExplorer and click on register on the upper right corner to create an account. Then again go to EarthExplorer and log in with the account that you have just created. In the first tab Search Criteria, you can set the acqusition time and area of the data by different ...


3

What we see on this image is a good example of exactly how broken Landsat 7 is at this point. Below is a RGB from the specific area: You have faulty values and spatial errors all across the image. Overall, using this image for any sort of analysis is going to result in lots of strange things. The line seen in the question is clearly visible, and it extents ...


0

Calculating MADI will produce a single band image. You can accomplish this calculation in the raster calculator. This is the expression you will need to use in the raster calculator if you are using composite (stacked) imagery: "your_modis_image.tif - Band_1" / "your_modis_image.tif - Band_7" Otherwise, if your imagery is composed of non-stacked bands: "...


3

Potentials that I'd suggest that you look at are: NDVI percentiles - to indicate the highest & lowest NDVI values, without having the issues associated with anomalous min & max values. Range of NDVI values in a year - to indicate variability over the year. Potentially based on the percentiles, instead of min & max values. Bi-modality, to ...


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I have very little experience in using SAR data for DEM extraction, but from looking to the resulting unwrapped interferogram (upper right), the pixelated area seems to be the no data black area of the coherence image (water) where the software interpolated the elevation values between land areas. That can be normal due to the no-data nature of water areas. ...


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If you are lucky, your site might be included within the SPOT World Heritage data set (CNES started reprocessing SPOT data acquired before 2010 and delivering them for free, but as the cost for CNES is high, new data sets are added very progressively) https://theia.cnes.fr/rocket/#/search?collection=SpotWorldHeritage


3

For Windows OS, some GDAL builds from Gisinternals are compiled with the KEA driver as well, see http://www.gisinternals.com/packageinfo.php?file=release-1800-x64-gdal-2-1-0-mapserver-7-0-1.zip UPDATE The KEA driver needs a GDAL version 2.0 or later. The linked file works well with the gisinternals build of GDAL 2.1.0, and the resulting tif seems to be ...


1

One solution is to use the data composite available on the site Global Forest Change , but it is done on more than one year. If you are lucky, you could also find some data on the SPOT image catalog, but it was not a systematic acquisition. Finally, you can try some gap filling with coarse resolution images (MERIS or MODIS)


2

SPOT has similar characteristics and is often used as a substitute. ASTER has similar characteristics and is often used as a substitute. Both will have the required coverage dates. ASTER will not have the blue band. Globcover has 2005 data but is at a poorer resolution. It has 2010 as well. MERIS, MODIS and other coarse systems are used to fill gaps in ...


2

The combine function (which is used in foreach) does not store the relevant components into the final randomForest object. See ?randomForest::combine: The confusion, err.rate, mse and rsq components (as well as the corresponding components in the test component, if exist) of the combined object will be NULL. But the predict method returns OOB ...


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My rule of thumb is to consider that the GSD is approximately 0.3 mm on the screen at maximum magnification. You could go down to 0.1 mm for pixel peeping, but then you lose the global view without a large gain in the details (GSD is often optimistic).


2

You could try an image segmentation approach but, I would not hold my breath on usable results. As far as application of a classification algorithm to panchromatic imagery, it is quite doubtful that you will get usable results because of the lack of any spectral separability associated with your target vegetation class. The only relevant information that you ...


0

Look at Landsat-util repository You should also consider using Landsat data from AWS. You can get a good overview of different usages to process Landsat data on this Amazon AWS blog post For Landsat data downloading, go to LANDSAT-Download repository


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An alternate source is the Sentinel-2 Amazon S3 archive, where you can search fast and preview images (without need for login). This one is preferred if you're interested in individual granules and not the whole tile file (which is few GBs) http://sentinel-pds.s3-website.eu-central-1.amazonaws.com A Tip: the date slider has a minimum of 25/11/2015 but ...


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The only alternative when no cloud-free images are available, is radar. You could try find some Sentinel-1 scenes, that are freely available.


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DigitalGlobe offers a GBDX service that lets you deploy your object detection algorithms against the worlds largest satellite image archive: http://developer.digitalglobe.com/gbdx/ I think their free developer tier includes some algorithms that would help you get started. Getting acurrate building footprints with a fully automated algorithm is tough though! ...


1

Yes - you can use MODIS imagery. The MODIS sensors have been degrading a bit and as such, there are some problems there. Also, given that you don't have exactly the same bands, view angles are a bit different, vastly different spatial scale and so on, the comparison will most likely be quite noisy and as such, it may indicate that a correction that actually ...


3

This isn't really an answer, but it is funny that I just saw an article today about this problem. Maybe it will lead you in the right direction. http://www.nextgov.com/big-data/2016/05/promise-terrapattern-visual-search-engine-satellite-imagery/128673/ Terrapattern, a visual search engine for satellite imagery, released this week by a team of artists ...


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I have been using the the Semi automatic classification plugin of QGIS and I haven't had that problem. The plugin has a module for TOA correction.See the documentation



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