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6

Please stick to one question per post in the future. It makes answering significantly easier. Question 1 - How to calculate EVI: The formula that you are using is "correct", in the sense that you are using the constants calculated for MODIS. Question 2 - Why does QGis and ArcGIS provide different results: Minuscule differences in results most likely ...


5

You can use Cell Statistics instead of raster calculator to get the average. I do not expect there is a limit to the number of raster to be used, but adding 244 is also too much in raster calculator. Cell statistics can do the same job and it can handle many raster, but not tested with hundreds of images. when using Cell Statistics tool, use the ...


4

The common definitions for these coefficients when using MODIS are: L=1, C1 = 6, C2 = 7.5. The C1 and C2 coefficients are aerosol resistance terms that rely on the blue band to partial out atmospheric influence in the red band. The gain is commonly defined as 2.5 So, here is the thing. One would not expect the same atmospheric influences in UAV imagery as ...


4

Sentinel 2 L1C reflectances are multiplied by 10000. Therefore you should adapt your equation in order to take the into account (or you can divide all values by 10000, but I wouldn't do this). On the other hand, it is worth noting that, while most values will ly between -1 and 1, EVI could mathematically be out of this range. (2.5 * float("Band 8" - "Band ...


4

There are several pro's and con's for using NAIP in land classification. Pro's High spatial resolution nIR band is useful for discriminating major vegetation classes (e.g. a crop circle surrounded by arid land) Acquired usually during peak growing season Con's Low spectral resolution The image acquisition during peak growth season can be ...


3

You partitioned your code in a way that obscures what object types you are actually using and what led you to the point that you are stuck. In the future please try to clarify your question before asking, ideally providing example data (note; we do not care about a picture of your polygons). Also, please include the package dependencies, and all of your code ...


3

I think what you're really looking to do is an image classification. You are specifically looking for one land class, i.e. tomato farms. How many bands of imagery do you have and in which parts of the spectrum do they lie? Hopefully at least one is within the infrared, which is critical for vegetation type classification. If you have very fine resolution ...


3

The following assumes you have ArcGIS 10.2 and the Spatial Analyst extension. If you have the Spatial Analyst extension available to you, I'm pretty sure the ArcPy.sa Raster Object can be used to apply the *EVI = 2.5 * (NIR-Red)/(NIR+6*Red-7.5*Blue+1)* formula across all MODIS rasters in your directory. I'm thinking we need these steps: Get a list of all of ...


3

Well this is not exactly a GIS (definitely not arcgis) question, but remote sensing is somewhat related. Anyway, EVI (enhanced vegetation index) is similar to NDVI and to SAVI (Soil-adjusted vegetation index). To wit it corrects for atmospheric and soil distortions. As stated in the NASA website "While the EVI is calculated similarly to NDVI, it corrects ...


3

You are right: the range of the NDVI is limited to values between -1 and 1 due to its' normalization properties. The negative limit of -1 will be reached if you encounter maximal reflectance (1) in the red wavelength region and zero reflectance in the NIR. The positive limit will be reached by maximal reflectance in the NIR region and zero reflectance in the ...


2

It is possible to monitor one or several classes in TIMESAT. The option to use several classes is mainly necessary if the phenological structure is very different between the classes, i.e. different parameter settings are necessary for the different classes. In your case it might be possible to use the same settings for both classes, but obviously this ...


2

Your stritching values in Map.addLayer are for a float band type (-1 to 1), and are not really stretching much. To use float values you should not change the band's type to unsigned integer 16 bits. This would do it: var EVI=evi//.multiply(100).uint16(); and the stretching values should be wider to represent the range of the output: Map.addLayer(EVI,{min: ...


2

Try using image operator, like: water = evi.lt(0.1).and(mndvi.gt(evi).or(mndvi.gt(ndvi)))


1

In your code example evi <- list.files(files.evi, pattern = glob2rx("*MYD13Q1*.tif$"), full.names = TRUE) in the part after "pattern = ..." you specifically select only the second part of your files, starting with "MYD". the not selected files start with "MOD13Q1", so they are not selected by your code :) You need to change it to: evi <- list....


1

You can use sum() reducer to compute frequency. I took your code to reduce it and change the approach. As I said to you in another post, reducers need to be applied as the last process. The process itself is just: var decision_tree = function(image){ var mndwi = image.normalizedDifference(['B3', 'B6']); var evi = image.expression( '(NIR - RED)/(NIR+...


1

In general you are encouraged to switch to the latest collection 6. Probably for most pixels the difference between the Collection 5 and Collection 6 data should be small, so empirical formulas developed for the Collection 5 are expected to keep working with the Collection 6 data. While Earth Observing System data centers have switched to generating ...


1

Ok, here's some code to solve your problem. Question 2 is addressed first with a 12-layer raster image being exported to your drive, then the second export is an example for a single year (example is with 2001). Edit: wrong link. https://code.earthengine.google.com/5b44745d195c944c4c4de06ed68fe000


1

The NDVI collections are just simple temporal composites of the actual data produced by the USGS (with a normalized ratio applied) and as such are pretty much only useful as browse products. If they don't suit your needs you should create your own with whatever modifications you feel are appropriate. In this case, I'd negative buffer the images by a few ...


1

Suppose you want to plot the raster.tif file and the polygons contained in the MLI_adm0.shp shapefile of this hypothetical data. Load them in R library(raster) library(rgdal) r <- raster('raster.tif') data.shape <- readOGR('.', 'MLI_adm0') As you mentioned, you must make sure that they have the same projection. It seems that it is not the case in ...


1

LAADS FTP is deprecated and its data moved to HTTPS. Use MODIS_1.1.3 (or higher) which covers this important migration. With this package version, your code runs smoothly on my machine. library(MODIS) tfs = runGdal("MOD13A2", collection = "006", tileH = 8, tileV = 7, begin = "2000.02.18", end = "2000.03.10", ...


1

You add an extra parenthesis. Instead of 2.5 * ((Float("Band4") - "Band3")) / (Float("Band4") + 6 * "Band3" - 7.5 *"Band1" + 1)) It should be 2.5 * ((Float("Band4") - "Band3")) / (Float("Band4") + 6 * "Band3" - 7.5 *"Band1" + 1) Or better 2.5 * Float("Band4" - "Band3") / (Float("Band4") + 6.0 * Float("Band3") - 7.5 * Float("Band1") + 1.0)


1

If you can't handle spatial data, transform it into a data.frame. In a really coarse example (model's performance will be very bad, rasters are totally random): library(raster) library(randomForest) set.seed(123) r <- raster(nrows = 10, ncols = 20) r1 <- r r1[] <- rnorm(200, mean = 0, sd = 0.5) r2 <- r r2[] <- rnorm(200, mean = 0, sd = 1) ...


1

How do you mosaic your images? I do believe that it doesn't throw away the other bands. If you keep all the bands from the input HDF-bands, then the problem arises in SPECTRAL_SUBSET = ( 1 ) - where you specify that you want the first band only, while ignoring the rest. The first band in MOD13Q1 is NDVI, EVI is the second. To fix that, you should have ...


1

You can also try Cropscape (you can upload an area of interest and get percentages in that area per crop type). They have data for 2014. http://nassgeodata.gmu.edu/CropScape/


1

You need to set the parameters of MRT and GDAL by yourself. I don't know exactly why R can't identify GDAL and MRT by default (I'm running R 3.3.1 in OS X 10.12), but I'll show you how I solve this error in my machine. I have the same issue, this is the result of printing MODIS::MODISoptions(): To install all required and suggested packages run: ...


1

Con(("dvel.img" <= 0.05) & ("evi.img" <= 0.3),1,0) The values in the new raster that meet the expression will be set to 1, all others 0. You must specify the name of the new raster in the "Output raster" box.


1

EVI = 2.5 * (NIR-Red)/(NIR+6*Red-7.5*Blue+1) I have used this formula in the raster calculator of QGIS.


1

The BYTSCL rescales your output from -1 to 1 -> 0 to 255. Remove that last bit, and you don't have your problem. BYTSCL is usually used for minimizing storage and bandwidth requirements associated with displaying data online. If you want to make sure that you do not have values below -1 and above 1, you can use this function: (float(b1) lt -1)*(-1)+(float(...


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