17
If you look at the product page at LPDAAC, under Layers there is a table that lists each of the bands in the dataset and their characteristics.
For the NDVI layer, it is a 16-bit signed integer with a fill value of -3000, and a valid range from -2000 to 10000. However, there is also a scale factor of 0.0001, or 1/10,000. This means that a value of 10000 in ...
11
First of all, you should take note that the simulated Sentinel-2 images come from the SPOT 4 (and maybe SPOT 5) satellite. So you'll have less bands than with the actual Sentinel-2. For SPOT 4 and 5, the bands are
B1 = green, B2=red, B3=NIR, B4=SWIR
Second, NDVI is more often (NIR-RED)/(NIR+RED), and you should make sure that you have reflectances in ...
9
For Raster calculator see the attached image. I suppose you have a Red-Green-Blue-NIR image as input and NDVI is calculated as (NIR-Red)/(NIR+Red) as in reference http://en.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index
Four-band NIR images have usually band order 1) natural red, 2) natural green, 3) natural blue, 4) near infrared. NDVI formula ...
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 ...
8
Using the raster calculator, you'll want to rescale your NDVI image. The formula you'll want to use is:
NDVI = 2/255*image-1
This is simply the linear regression between 0 to 255 and -1 to 1, applied to raster math.
7
To begin, you need to know the which spectral bands are which in your base image. NDVI is calculated from reflectance rather than radiance or DN. Therefore you will need to make sure your imagery has been converted to express reflectance. The equation to calculate NDVI is as follows:
(near infrared - red)/(near infrared + red)
If you are using LISS IV ...
7
When remote sensing vegetation, the time of year is very important. In most climates, vegetation has significantly more biomass (i.e., leaves etc.) during the summer, which means that it is easier for the sensor to discern the health of vegetation at that time of year. Two NDVI images of the same location from different times of the year may look different ...
7
Your best bet would be to mosaic the raw red band and near infrared band images from which the NDVI images are derived. There are techniques for creating seamless mosaics for images, e.g. through the use of histogram matching and feathering techniques. For areas of overlap, the feathering method will calculate the output value as a weighted combination of ...
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 ...
7
You need to supply levelplot() with a bit more information, via its at= argument, so that it can know the set of intervals to which you want the colors in col.regions applied.
library(rasterVis)
## A raster with values between 0.15 and 0.40
r <- raster(matrix(runif(400, min = 0.15, max = 0.4), ncol = 20))
## Set up color gradient with 100 values ...
7
Between 770 and 1040 nm, you are nearly on a plateau in term of reflectance from the green vegetation (in fact, it decreases a little bit when the wavelength increases), so your green vegetation NDVI will be comparable. However, dry vegetation and bare soil usually have an increasing reflectance when the wavelength increases. So, if you want to optimise the ...
6
I agree with Vesanto on the first lines. However, you should note that the band names start at "2" because on former IRS satellites the band "1" is for panchromatic (removed on IRS P6 because of the LISS IV sensor).
for NDVI, the equation is (NIR-red)/(NIR+red). (Red is used because of absorption by chlorophyll in this wavenlength).
The red is 0.62 - 0....
6
First I would use bands 4(red) and 5(nir) for Landsat 8 according to the description of the OLI instrument, and 3(red) and 4(NIR) for the Landsat TM and ETM.
Second, you define an output in dtype=rasterio.uint16, but NDVI should be a float (between -1 and 1). You should either initialize your raster as dtype=rasterio.float32 , or multiply your values by ...
6
Take raster NDVI dataset and use a raster calculator-type function to create a binary surface of 1 and NULL (no data); 1 is where values are between 0.1 and 1; NULL is everything else.
For each farm vector feature, clip your binary-classified NDVI dataset.
For each clipped NDVI dataset, count the number of cells that are not NULL, and multiply this number by ...
6
It depends upon the intended use of the Landsat data. Generally speaking, if you are doing multi-temporal analyses, you need atmospherically corrected data, otherwise DN format is sufficient. I would recommend reading the following landmark paper on the subject:
Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). ...
6
There's no need for a for (or any other kind of) loop since such stuff is entirely included in the raster package. If you want to calculate the NDVI, then basically all you need is
## required package
library(raster)
## calculate ndvi from red (band 1) and near-infrared (band 2) channel
ndvi <- overlay(StackBand1, StackBand2, fun = function(x, y) {
(y-...
6
No, the NDVI threshold value will not be the same for the time series due to differences in phenology and unique conditions on the ground. As Kersten mentioned in the comments, you may want to consider using Global Forest Watch data, which is well respected in the environmental community.
You have uncovered one of the limitations of working with ...
6
The code that you copied uses an Earth Engine object of type "Image", and Image objects have a method/function named clip. Your reworked code (i.e. Landsat) uses an Earth Engine object of type "ImageCollection", and ImageCollection objects do not have a method named clip, so it produces an error. To get around this, you can either:
Convert the ...
6
Do you want an interactive map, or are you fine with plotting the NDVI timeseries for one (or few) specific points? In the latter you raster::extract() the NDVI-Stack values under your points, and use the returned dataframe as input for your plot.
Edit:
I was thinking... You actually can do some interactive point-selection using raster::click(). So as a ...
6
Testing and comparing a Landsat 7 image (different area to yours, but same sensor data) reveals that the values are in fact both "correct".
You (and I) have fallen into the trap of the Raster rendering definition - effectively the Raster styling. This is found by right clicking the layer -> properties -> Style
In the screen you can define how to ...
6
You have downloaded the data and you can find it if you unpack the .tar.gz file using 7zip or similar software for unpacking files. The .tar.gz file is the fourth file from the top that can be seen in your first screenshot.
Do note that you have to unpack the .tar.gz twice in order to get to the data. You will easily recognize it as you will see the ...
6
The best explanation I came across is this:
the ratio of the difference of the red and infrared radiances over their sum as a means to adjust for or “normalize” the effects of the solar zenith angle. Originally, they called this ratio the “Vegetation Index” (and another variant, the square-root transformation of the difference-sum ratio, the “Transformed ...
6
This is how I would do it (it requires the Spatial Analyst extension which I think you have):
import arcpy
from arcpy.sa import *
arcpy.CheckOutExtension = "Spatial"
ndvi_raster = Raster("ndvivaluefrompixel")
grey_raster = Raster("grayscalepixelvalue")
output_raster = Con(ndvi_raster >= -0.000005 * grey_raster + 0.314367, 2, 1)
output_raster.save("C:/...
5
You can easily download freely available ASTER Level 1B (at-sensor calbirated radiances) from the USGS EarthExplorer site. A simple quick search for a polygon roughly covering New Mexico returns > 100 results, but individual scenes are much smaller, so for a full cover of the state you'd have to stitch them together. USGS Glovis should have the same data, ...
5
The simplest option would be the use of Raster Calculator with a conditional statement.
Your statement may look something like the following with 'threshold' replaced with whatever value you like. The resulting raster will give you pixels with value=1 where values are greater than threshold, and NoData where values are less than threshold.
Con("NDVI_img" &...
5
Reproducing the map example you provided is primarily a cartographic effort and requires very little analysis if you have already calculated NDVI. I would use the following workflow to produce the map similar to the one you provided a link to.
Collect the NDVI data to use in your analysis. In the example, they
use "Summer" 1989 to 2001. In your case, you ...
5
There is no specific GIS software for doing this: most will handle the RGB image and the Lidar data.
Basically, NDVI is (NIR - RED)/(NIR + RED). Most of the time, aerial Lidar gives you the NIR value (to be checked in metadata) and the first band of your RGB image gives you the RED value. Just make sure that your data are calibrated to reflectance (or, if ...
5
Earth Explorer is an invaluable source for NAIP imagery. Access these images using the following steps:
Define your area of interest in the "Search Criteria" tab. Here you
can specify a county in CA by navigating to the Predefined Area tab > Add Shape > California > County > YourCounty
Select data: Data Sets tab > Aerial Imagery > NAIP ...
5
For visualisation purpose, you can select a resampling method from the display properties. right click on layer > properties, then display tab / resampling during display using : cubic convolution (cubic convolution yields the smoothest display, bilinear interpolation also works).
here is an example with a S2 image, with cubic convolution (top) and ...
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