8

I've used Enhanced Vegetation Index (EVI) data extensively for analyzing agricultural areas. Although I've never used it with NAIP imagery, all you need is red, blue, and IR data. For your purposes, the biggest advantage of EVI is that it does not "saturate" as easily as NDVI--it offers more contrast (dynamic range) when examining highly vegetated areas ...


7

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 rescales ...


5

Bare soil and urban areas are notoriously hard to segregate. Even with a perfect atmospheric correction, there will be relatively high confusion between the two, particularly when limited to multispectral datasets. The atmospheric correction technique you are doing is a simple dark object subtraction, wherein the darkest objects in the landscape should have ...


5

In your code, S2 is an ImageCollection, so when you "create band variables" you're just getting ImageCollections in which every image inside has only the selected band, which is useful. As you well commented, add_reNDVI is a function that will take every image in the collection and calculate reNDVI. So you have to map that function over the collection to get ...


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 ...


3

The sensitivity of the normalized difference vegetation index (NDVI) to the soil background and atmospheric effects has generated an increasing interest in the development of new indices. The NDVI index is saturated in high biomass and it is sensitive to a number of perturbing factors, such as atmospheric effects, cloud, soil ...


3

Both ways work just fine and give the same results: https://code.earthengine.google.com/3f766772035a8890b0caf231eb652a1f. There is also a small mistake in the formula in your code - wrong bracket. // compute composite for all images aquired in the last 3 days var now = ee.Date(Date.now()) var composite = s2 .filterDate(now.advance(-3, 'day'), now) ....


3

QGIS has a plugin called zonal statistics that will allow you to calculate the mean NDVI. It will also calculate the pixel count, and the sum of all pixel values within each polygon. If you wish to sample multiple rasters, I'd recommend using the Zonal Stats module in RSGISLib. This will allow you to iterate over multiple rasters using the same shapefile, ...


3

Depending on the camera you are 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 ...


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 ...


2

Here is a raster algebra statement that will get you the EVI. ( ("band4" - "Band1") / ("Band4" + 6 * "Band1" - 7.5 * "Band3" + 1) ) * 2.5


2

Do you have access to another image from the same year but referenced to a different maturity stage? Imagine your image is from the spring, if you have an image from late summer, you'll get the changes in crops and those would help distinguish Agriculture from Forest. Anyway you have a lot of Vegetation indices options, most common are: NDVI EVI less ...


2

In QGIS: Raster menu -> Miscellaneous -> Build Virtual Raster (Catalog)... Ensure the "Separate" option is ticked so each raster is put into a separate band Raster menu -> Conversion -> Translate (Convert Format)... Output to GeoTIFF Command-line GDAL: gdalbuildvrt -separate NRG.vrt NIR.tif RED.tif GREEN.tif gdal_translate NRG.vrt NRG.tif Note there are ...


2

You can use Zonal Statistics as Table to accomplish this. The zonal data can be either raster or vector.


2

NDVI= (NIR-RED) / (NIR+RED) Found this which may help - http://www.harrisgeospatial.com/docs/broadbandgreenness.html Other resources which may be useful - https://blogs.esri.com/esri/arcgis/2013/07/24/band-combinations-for-landsat-8/ Band Combinations for Landsat 8 by kevin_butler on July 24, 2013 1763 208 50K Landsat 8 has been online for a couple of ...


2

I think the equation in the raster calculator should be written as follows: (("TOA_B5.tif"+1) * (256-"TOA_B4.tif") * ("TOA_B5.tif"-"TOA_B4.tif"))**(1.0/3.0) **: means power convert (1/3) integer division into float division (1.0/3.0) You need to change "TOA_B5.tif" and "TOA_B4.tif" to the corresponding layer names exist in the table of content in ArcMap


2

Classification: (in case you don´t have a classified image already and/or if that´s new to you) Try the Semi-Automatic Classification Plugin, you find the user manual and some examples/tutorials here. The general steps are to enhance spectral signature/general image preprocessing: Especially multi band imagery should be spectrally cleaned and enhanced in ...


2

You dont mention how fast your model executes. I tried code below for three polygons, generating about 60000 random trees in one minute. Make sure not to input impossible combination of tree density and minimum tree distance or it will run forever. import arcpy from random import randint arcpy.env.overwriteOutput = True #Change these five lines arcpy.env....


2

This index has a slope correction so is not bound to a theoretical -1 to 1 range. Just for reference, here is the pseudo-code for the MSAVI2 that does not require the estimation of L. You may want to either check it against your code or, if using a software function, manually derive the index to see if it makes a difference. msavi2 = (2 * nir + 1 - sqrt( (...


1

map() function is for ImageCollection object. Computing median, you're converting ImageCollection object in Image object, thas why doesn't work your code. Try this: // Creating a buffer around my region of interest var buffer = ee.Geometry.Point([103.83841283715412,1.3736828122619071]).buffer(10000); Map.centerObject(buffer, 12); // Load Landsat 8 surface ...


1

The nominal scale of the a MOD13A2 image is indeed around 1000m: var sampleImage = ee.Image(ee.ImageCollection("MODIS/006/MOD13A2").first()) .select('NDVI'); print('Scale in meters:', sampleImage.projection().nominalScale()); However, your code example is not printing out the scale of an MOD13A2 image, but rather is printing out the ...


1

I have used NDVI to measure vegetation cover in sparse spaces. Though the data can be complex to find, i do think it's worth it. NDVI stands for Normalized Difference Vegetation Index and it is picked up using remote sensing. If you are interested in remote sensing or want to learn more, you should check out this interactive project I have pulled together ...


1

Your problem is the vectorization of an area that is defined by some criteria in a raster file. That could be done by meany means, but the simplest in QGIS is with: Raster->Conversion->Polygonize (Raster to vector) You could have a look to this answer: Calculating area of rasters in QGIS? it will lead you to what you need. A relevant criterion should be ...


1

Mix them how? You have lots of options. You can mix them visually by draping the vegetation data over the DEM, or the hillshade derived from the DEM, and set the transparency of the top layers to build a map that shows both data. You can create contours from the DEM and set those over the vegetation data.


1

Google Earth uses SRTM DEM data as source of elevation for most part of the world (see here). DEM is mostly DTM, do not considers any object over the surface. So Google Earth might not be a good source for you. I'd recommend few ideas, use high-res elevation data (ie. LiDAR) volumetric shadow analysis in-situ height information (ie. survey or inventory ...


1

You don't need to extract NDVI or EVI from MOD13Q1 data sets as they are two separate bands in your data set like the pixel reliability and VI Quality bands. There is a scale factor of 0.0001. You can multiply your NDVI and EVI data to be in the expected data value range. In order to analyse time-series you can use TIMESAT application which allows the usage ...


1

@HDunn is right, you have to convert to reflectance using the quantification value (10000) which is in the product metadata (not the granule metadata). For instance, EVI = G.(Nir-Red)/(Nir+C1.Red-C2.Blue+L) If all the reflectances are multiplied by 10000, adding L=1 does not have much effect.


1

No way to calculate NDVI from SAR imagery, like Sentinel-1 images(any polarisations). To calculate NDVI you could get 2 bands (Near Infrared and Red bands) from Sentinel-2 or Landsat-8. So, about SAR. You just only can get some value about biomass or LAI (Leaf Area Index) and after that you can try to search some formulas to indexing vegetation for ...


1

My search on the web for PolsDP turned up this answer first. PolsDP is a Polarimetric SAR data processing software being developed at the CSRE, IIT Bombay, under the sponsorship of SAC, ISRO. It aims to be a toolbox for PolSAR data processing, allowing the user to easily run various steps like exporting data from SLC files, processing, ...


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