{"id":1384,"date":"2015-07-08T03:24:15","date_gmt":"2015-07-08T03:24:15","guid":{"rendered":"https:\/\/grindgis.com\/?p=1384"},"modified":"2017-08-23T13:13:23","modified_gmt":"2017-08-23T13:13:23","slug":"vegetation-indices-arcgis","status":"publish","type":"post","link":"https:\/\/grindgis.com\/?p=1384","title":{"rendered":"Calculating the Vegetation Indices from Landsat 8 image Using ArcGis 10.1"},"content":{"rendered":"

[vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]Table of Contents:<\/strong><\/span><\/p>\n

Step 1: Calculating Reflectance value from the Satellite data<\/a><\/p>\n

Step 2: Correcting the Reflectance value with sun angle<\/a><\/p>\n

Step 3: Calculating NDVI from 4th and 5th bands<\/a><\/p>\n

Step 4: Calculating SAVI from Reflectance<\/a><\/p>\n

Step 5: Calculating Tasseled cap indices<\/a><\/p>\n

Step 6: Tasseled cap Brightness indices<\/a><\/p>\n

Step 7: Similarly calculate the other indices\u00a0Tasseled cap wetness index and\u00a0Greenness index<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

The Normalized Differential Vegetation Index (NDVI) is a standardized vegetation index which allows us to generate an image showing the relative biomass. The chlorophyll absorption in Red band and relatively high reflectance of vegetation in Near Infrared band (NIR) are using for calculating NDVI. ( From ESRI<\/a>)<\/p>\n

\u201cAn NDVI is often used worldwide to monitor drought, monitor and predict agricultural production, assist in predicting hazardous fire zones, and map desert encroachment. The NDVI is preferred for global vegetation monitoring because it helps to compensate for changing illumination conditions, surface slope, aspect, and other extraneous factors\u201d (Lillesand 2004).<\/em><\/p><\/blockquote>\n

Monitoring the intensity and the density of the green vegetation growth can be done using the reflection from the red band and the infrared band. Green vegetation reflects more energy in the near- infrared band than in the visible range. It observe red band more for the photosynthesis process. Leaves reflects less in the near-infrared region when they are stressed, diseased or dead. Features like Clouds, water and snow show better reflection in the visible range then the near-infrared range, while the difference is almost zero for rock and bare soil.<\/p>\n

Output of the NDVI method creates a single-band dataset that only shows greenery. Values close to zero represent rock and bare soil and negative values represent water, snow and clouds. Taking ratio or difference of two bands makes the vegetation growth signal differentiated from the the background signal. NDVI method was developed by the NASA scientist popularly known as Normalized Difference Vegetation Index (NDVI). By taking a ratio of two bands drop the values between -1 to +1. Table below shows the red and infrared bands reflectance values of features and their NDVI values.\u00a0Water has an NDVI value less than 0, bare soils between 0 and 0.1, and vegetation over 0.1.\u00a0Increase in the positive NDVI value means greener the vegetation.<\/p>\n

\"NDVI<\/p>\n

RED=<\/strong> DN values from RED\u00a0band<\/p>\n

NIR=<\/strong> DN values from Near Infrared band[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

\n\n\n\n\n\n\n\n\n
\n

COVER TYPE<\/span><\/b><\/p>\n<\/td>\n

\n

RED<\/span><\/b><\/p>\n<\/td>\n

\n

\u00a0\u00a0\u00a0 NIR<\/span><\/b><\/p>\n<\/td>\n

\n

NDVI<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n

\n

Dense vegetation<\/span><\/p>\n<\/td>\n

\n

0.1<\/span><\/p>\n<\/td>\n

\n

0.5<\/span><\/p>\n<\/td>\n

\n

0.7<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Dry Bare soil<\/span><\/p>\n<\/td>\n

\n

0.269<\/span><\/p>\n<\/td>\n

\n

0.283<\/span><\/p>\n<\/td>\n

\n

0.025<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Clouds<\/span><\/p>\n<\/td>\n

\n

0.227<\/span><\/p>\n<\/td>\n

\n

0.228<\/span><\/p>\n<\/td>\n

\n

0.002<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Snow and ice<\/span><\/p>\n<\/td>\n

\n

0.375<\/span><\/p>\n<\/td>\n

\n

0.342<\/span><\/p>\n<\/td>\n

\n

-0.046<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Water<\/span><\/p>\n<\/td>\n

\n

0.022<\/span><\/p>\n<\/td>\n

\n

0.013<\/span><\/p>\n<\/td>\n

\n

-0.257<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Fig: Table 1<\/span><\/em><\/strong><\/p>\n<\/div>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

Table shows typical reflectance values in the red and infrared channels, and the NDVI for typical cover types. Water typically has an NDVI value less than 0, bare soils between 0 and 0.1 and vegetation over 0.1.<\/p>\n

Here we are using the Landsat image acquired from USGS Earth Explorer. The data is in GeoTiff format with 16 bit radiometric resolution (ranges from 0-65535). Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images consist of nine spectral bands with a spatial resolution of 30 meters for Bands 1 to 7 and 9. The resolution for Band 8 (panchromatic) is 15 meters. In addition it also have two Thermal IR bands with a spatial resolution of 100m (later resampled into 30 m).Before calculating the NDVI the DN data must be converted to reflectance using the equations given in their website. Here the IR and NIR bands are 4 and 5 respectively.<\/p>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]Table shows the Spectral characteristics of Landsat 8<\/strong><\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Landsat 8<\/span><\/strong><\/p>\n

Operational<\/strong><\/p>\n

Land Imager<\/strong><\/p>\n

(OLI)<\/strong><\/p>\n

and<\/strong><\/p>\n

Thermal<\/strong><\/p>\n

Infrared<\/strong><\/p>\n

Sensor<\/strong><\/p>\n

(TIRS)<\/strong><\/td>\n

\n

Bands<\/span><\/b><\/p>\n<\/td>\n

\n

Wavelength<\/span><\/b><\/p>\n<\/td>\n

\n

Resolution<\/span><\/b><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
<\/td>\n<\/tr>\n
\n

Band 1 – Coastal aerosol<\/span><\/p>\n<\/td>\n

\n

0.43 – 0.45<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 2 – Blue<\/span><\/p>\n<\/td>\n

\n

0.45 – 0.51<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 3 – Green<\/span><\/p>\n<\/td>\n

\n

0.53 – 0.59<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 4 – Red<\/span><\/p>\n<\/td>\n

\n

0.64 – 0.67<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 5 – Near Infrared (NIR)<\/span><\/p>\n<\/td>\n

\n

0.85 – 0.88<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 6 – SWIR 1<\/span><\/p>\n<\/td>\n

\n

1.57 – 1.65<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 7 – SWIR 2<\/span><\/p>\n<\/td>\n

\n

2.11 – 2.29<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 8 – Panchromatic<\/span><\/p>\n<\/td>\n

\n

0.50 – 0.68<\/span><\/p>\n<\/td>\n

\n

15<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 9 – Cirrus<\/span><\/p>\n<\/td>\n

\n

1.36 – 1.38<\/span><\/p>\n<\/td>\n

\n

30<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 10 – Thermal Infrared
\n(TIRS) 1<\/span><\/p>\n<\/td>\n

\n

10.60 – 11.19<\/span><\/p>\n<\/td>\n

\n

100 * (30)<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n
\n

Band 11 – Thermal Infrared
\n(TIRS) 2<\/span><\/p>\n<\/td>\n

\n

11.50 – 12.51<\/span><\/p>\n<\/td>\n

\n

100 * (30)<\/span><\/p>\n<\/td>\n

<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0(Obtained from USGS website)<\/em><\/p>\n<\/div>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

Step 1:<\/u><\/span> Calculating Reflectance value from the Satellite data<\/strong><\/h2>\n

OLI spectral radiance data can also be converted to TOA planetary reflectance using reflectance rescaling coefficients provided in the landsat8 OLI metadata file. The following equation is used to convert DN values to TOA reflectance for OLI image:<\/p>\n

<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column width=”1\/2″][vc_column_text]<\/p>\n

 <\/p>\n

 <\/p>\n

 <\/p>\n

\u03c1\u03bb’ = M \u03c1Q cal + A \u03c1<\/span><\/strong>[\/vc_column_text][\/vc_column][vc_column width=”1\/2″][vc_column_text]Where:<\/strong><\/p>\n

\u03c1\u03bb’<\/strong> = TOA planetary reflectance, without correction for solar angle. Note that \u03c1\u03bb’ does not contain a correction for the sun angle.<\/p>\n

M \u03c1<\/strong> = Band-specific multiplicative rescaling factor from the metadata (Reflectance_Mult_Band_x, where x is the band number)<\/p>\n

A \u03c1<\/strong> = Band-specific additive rescaling factor from the metadata (Reflectance_Add_Band_x, where x is the band number)<\/p>\n

Q cal<\/strong> = Quantized and calibrated standard product pixel values (DN).<\/p><\/blockquote>\n

(Metadata means the .txt file you can find along with the data<\/u><\/em>)[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]I.\u00a0<\/span><\/strong>Open ArcMap and add satellite images band 4 and band 5 for\u00a0calculating NDVI.<\/p>\n

\"Arc<\/p>\n

II.\u00a0<\/span><\/strong>Open Arc toolbox in ArcMap<\/p>\n

II.\u00a0<\/span><\/strong>From the Arc toolbox open Raster calculator (Arc toolbox\u00a0>Spatial Analyst tool\u00a0>\u00a0Map Algebra tool\u00a0>\u00a0Raster Calculator tool)<\/p>\n

\"Raster[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]IV.\u00a0<\/span><\/strong>Find M \u03c1 <\/strong>Band-specific multiplicative rescaling factor from the metadata\u00a0\u00a0\u00a0 (Reflectance_Mult_Band_x, where x is the band number)<\/p>\n

A \u03c1 <\/strong>Band-specific additive rescaling factor from the metadata (Reflectance_Add_Band_x, where x is the band number)<\/p>\n

\"metadata\"<\/p>\n

An example of where the M \u03c1 <\/strong>Band-specific multiplicative rescaling factor from the metadata<\/p>\n

(Reflectance_Mult_Band_x, where x is the band number) is found within the metadata file<\/p>\n

Apply and perform values in Raster calculator for both RED\u00a0and NIR bands (4 and 5 bands)<\/strong><\/p>\n

Example<\/u><\/em><\/strong><\/p>\n

Band 4 reflectance= (2.0000E-05 * (“sub_tif_Band_4\u201d)) + -0.100000<\/em><\/p>\n

Where<\/em><\/p>\n

REFLECTANCE_MULT_BAND_4 = 2.0000E-05<\/p>\n

RFLECTANCE_ADD_BAND_4 = -0.100000<\/p>\n

sub_tif_Band_4<\/em>= 4th<\/sup> band (Red band)<\/p>\n

Set the output folder and file name then click \u201cOK\u201d<\/p><\/blockquote>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]\"Raster
\nRaster Calculator function to perform reflectance calculation<\/strong><\/p>\n

\"Reflectance
\nReflectance calculated for band 4<\/strong><\/p>\n

V.<\/span><\/strong> Calculate the Reflectance of band 5th<\/sup> by using the same method.<\/p>\n

\"Reflectance
\nReflectance calculated for band 5<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

Step 2:<\/u><\/span> Correcting the Reflectance value with sun angle<\/strong><\/strong><\/h2>\n

Reflectance with a correction for the sun angle is then:
\n<\/a>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column width=”1\/2″][vc_column_text]<\/p>\n

 <\/p>\n

 <\/p>\n

\u03c1\u03bb = \u03c1\u03bb ‘\/cos \u03b8SZ = \u03c1\u03bb ‘\/sin \u03b8SE<\/span><\/strong>[\/vc_column_text][\/vc_column][vc_column width=”1\/2″][vc_column_text]Where:<\/strong><\/p>\n

\u03c1\u03bb <\/strong>= TOA planetary reflectance<\/p>\n

\u03b8SE<\/strong> = Local sun elevation angle. The scene center sun elevation angle in degrees is provided in the metadata (Sun Elevation).<\/p>\n

\u03b8SZ<\/strong> = Local solar zenith angle; \u03b8SZ = 90\u00b0 – \u03b8SE.<\/p><\/blockquote>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]I.<\/span><\/strong> Find Sun Elevation from the metadata<\/p>\n

Apply and perform the values in Raster calculator for both RED\u00a0and NIR band reflectance values (4 and 5 bands)<\/strong><\/p>\n

Example<\/u><\/em><\/strong><\/p>\n

Band 4 corrected_ reflectance= (“4th_rflctnce”) \/ Sin (49.36816761)<\/em><\/p>\n

Where:<\/p>\n

SUN_ELEVATION = 49.36816761<\/p>\n

4th_rflctce =uncorrected reflectance <\/em><\/p>\n

Set the output folder and file name then click \u201cOK\u201d<\/p><\/blockquote>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]\"Raster
\nRaster Calculator function to perform sun angle corrected reflectance<\/strong><\/p>\n

\"corrected
\nSun angle corrected reflectance for band 4th<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]II.<\/span><\/strong> Calculate the Reflectance of band 5th<\/sup> by using the same method<\/p>\n

\"Reflectance
\nSun angle corrected reflectance for band 5th<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/a><\/p>\n

Step 3:<\/u><\/span> Calculating NDVI from 4th<\/sup> and 5th<\/sup> bands<\/strong><\/strong><\/h2>\n

As we discussed earlier to find NDVI we use the formula of<\/p>\n

\"NDVI\"<\/p>\n

Here band 4= RED\u00a0and band 5=NIR<\/p>\n

I.<\/span> <\/strong>Open Raster calculator and apply this formula and execute the program as follows<\/p>\n

NDVI=<\/strong> (<\/strong>Band 5 corrected<\/em><\/strong> –<\/strong>Band 4 corrected<\/em><\/strong>)\/ (<\/strong>Band 5 corrected + Band 4 corrected<\/em><\/strong>)<\/strong><\/p><\/blockquote>\n

Set the output folder and file name then click \u201cOK\u201d<\/p>\n

\"Raster
\nRaster Calculator function to perform NDVI<\/strong><\/p>\n

\"Normalized
\nNormalized Differential Vegetation Index<\/strong><\/p>\n

The value of NDVI is always between 1 and -1<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/a><\/p>\n

Step 4:<\/u><\/span> Calculating SAVI from Reflectance<\/strong><\/strong><\/h2>\n

Soil Adjusted Vegetation Index shows background soil conditions. SAVI is a hybrid between NDVI and PVI (Perpendicular Vegetation Index).<\/p>\n

\"SAVI\"<\/p>\n

Where NIR is the 5th<\/sup> band and RED is the 4th<\/sup> band respectively. L is the Soil brightness correction factor. The value of L varies by cover of green vegetation. High vegetation areas L= 0; the areas have no green vegetation then L=1. Generally L=0.5<\/span> in most causes and it works well. When L=0 then NDVI=SAVI<\/p>\n

I.<\/span> <\/strong>Open Raster calculator and apply this formula and execute the program as follows<\/p>\n

SAVI=<\/strong> (<\/strong>Band 5 corrected<\/em><\/strong> –<\/strong>Band 4 corrected<\/em><\/strong>)\/ (<\/strong>Band 5 corrected + Band 4 corrected<\/em><\/strong>)*(1+L)<\/strong><\/p><\/blockquote>\n

\"Raster
\nRaster Calculator function to perform SAVI<\/strong><\/p>\n

\"Soil
\nSoil Adjusted Vegetation Index<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/a><\/p>\n

Step 5:<\/u><\/span> Calculating Tasseled cap indices<\/strong> <\/strong><\/h2>\n

Tasseled cap indices give the measure of Greenness, Brightness and Wetness of each pixel and utilize a linear combination of 6 Landsat bands (From 2nd<\/sup> band to 7th <\/sup>band). Tasseled cap indices is\u00a0calculated by the following equation<\/p>\n

Tas_cap <\/em><\/strong>\u1f31<\/em><\/strong> = \u00a0 \u00a0\u00a0<\/em><\/strong>(coeff<\/em><\/strong>\u2082<\/em><\/strong> * band2) <\/em><\/strong>+ (coeff<\/em><\/strong>\u2083 * band3) <\/em><\/strong>+ (coeff\u2084<\/em><\/strong> * band4) <\/em><\/strong>+ <\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0(coeff<\/em><\/strong>\u2085 * band5) +\u00a0<\/em><\/strong>(coeff<\/em><\/strong>\u2086* band6) <\/em><\/strong>+ (coeff\u2087 * band<\/em>7<\/strong>)<\/strong><\/em><\/strong><\/p><\/blockquote>\n

Where Tas_cap <\/span><\/em>\u1f31<\/span><\/em> <\/strong>is the calculated tasseled cap index for brightness, greenness, or wetness from the coefficient given[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/p>\n

\n\n\n\n\n\n\n
\n

\u00a0<\/span><\/p>\n

Index<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n2<\/span><\/strong><\/p>\n

(Blue)<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n3<\/span><\/strong><\/p>\n

(Green)<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n4<\/span><\/strong><\/p>\n

(Red)<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n5<\/span><\/strong><\/p>\n

(NIR)<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n6<\/span><\/strong><\/p>\n

(SWIR
\n1)<\/span><\/strong><\/p>\n<\/td>\n

\n

Band
\n7<\/span><\/strong><\/p>\n

(SWIR
\n2)<\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Brightness<\/span><\/p>\n<\/td>\n

\n

0.3029<\/span><\/p>\n<\/td>\n

\n

0.2786<\/span><\/p>\n<\/td>\n

\n

0.4733<\/span><\/p>\n<\/td>\n

\n

0.5599<\/span><\/p>\n<\/td>\n

\n

0.508<\/span><\/p>\n<\/td>\n

\n

0.1872<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Greenness<\/span><\/p>\n<\/td>\n

\n

\u22120.2941<\/span><\/p>\n<\/td>\n

\n

\u22120.243<\/span><\/p>\n<\/td>\n

\n

\u22120.5424<\/span><\/p>\n<\/td>\n

\n

0.7276<\/span><\/p>\n<\/td>\n

\n

0.0713<\/span><\/p>\n<\/td>\n

\n

\u22120.1608<\/span><\/p>\n<\/td>\n<\/tr>\n

\n

Wetness<\/span><\/p>\n<\/td>\n

\n

0.1511<\/span><\/p>\n<\/td>\n

\n

0.1973<\/span><\/p>\n<\/td>\n

\n

0.3283<\/span><\/p>\n<\/td>\n

\n

0.3407<\/span><\/p>\n<\/td>\n

\n

\u22120.7117<\/span><\/p>\n<\/td>\n

\n

\u22120.4559<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0Given by Muhammad Hasan Ali Baig<\/a> et al (2014)<\/p>\n

\u00a0<\/span><\/p>\n<\/div>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/a><\/p>\n

Step 6:<\/u><\/span> Calculating Tasseled cap Brightness indices<\/strong><\/strong><\/h2>\n

I.<\/span><\/strong> Calculate the Reflectance of Band 2, 3, 4, 5, 6, 7, using the same method mentioned above (Process 1 and 2)<\/p>\n

II.\u00a0<\/span><\/strong>Open Raster calculator and apply the formula for Brightness index and execute the program as follows<\/p>\n

Tas_cap <\/em><\/strong>\u1f31<\/em><\/strong> = (<\/em><\/strong>0.3029<\/strong> *(2nd_Band_correted))+<\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0(<\/em><\/strong> 0.2786<\/strong> *(3rd_Band_correted))+<\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0(<\/em><\/strong>\u00a00.4733<\/strong> *(4th_Band_correted))+<\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0(<\/em><\/strong> 0.5599<\/strong> *(5th_Band_correted))+<\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (<\/em><\/strong> 0.508<\/strong> *(6th_Band_correted))+<\/em><\/strong><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 (<\/em><\/strong> 0.1872<\/strong> *(7th_Band_correted))<\/em><\/strong><\/p><\/blockquote>\n

Raster Calculator function to perform SAVI Raster Calculator function to perform SAVI<\/p>\n

\"AVI
\nRaster Calculator function to perform Brightness index<\/strong><\/p>\n

\"Tasseled<\/strong>
\nTasseled cap brightness index<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]<\/a><\/p>\n

Step <\/u><\/span>7<\/u><\/span>:<\/u><\/span> Similarly calculate the other indices using the same method mentioned above<\/strong><\/strong><\/h2>\n

\"Tasseled
\nTasseled cap wetness index<\/strong><\/p>\n

\"Tasseled
\nTasseled cap Greenness index<\/strong>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”30″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]References:<\/em><\/p>\n

http:\/\/landsat.gsfc.nasa.gov\/?p=3186<\/a><\/em><\/p>\n

http:\/\/hydrology1.nmsu.edu\/teaching\/soil698\/greenseeker\/what_is_ndvi.htm<\/em><\/p>\n

http:\/\/www.pvts.net\/pdfs\/ndvi\/3_3_ndvi.PDF<\/a><\/em><\/p>\n

http:\/\/wiki.landscapetoolbox.org\/doku.php\/remote_sensing_methods:normalized_difference_vegetation_index<\/a><\/em><\/p>\n

Muhammad Hasan Ali Baigab, Lifu Zhanga, Tong Shuaiab & Qingxi<\/em><\/p>\n

Tonga- \u201cDerivation of a tasselled cap transformation based on Landsat 8 atsatellite reflectance\u201d (2014) Remote Sensing Letters<\/em><\/p>\n

http:\/\/www.tandfonline.com\/loi\/trsl20<\/a><\/em><\/p>\n

Grant J. Firl, Lane Carter\u201d Calculating Vegetation Indices from Landsat 5 TM and Landsat 7 ETM+ Data\u201d(2011)<\/em>[\/vc_column_text][\/vc_column][\/vc_row][vc_row margintop=”30″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][tagline_box call_text=”NOW READ:” call_text_small=”Know Basics of Remote Sensing Quickly and Become Expert” title=”READ” color=”blue” margin=”0″ href=”https:\/\/grindgis.com\/what-is-remote-sensing\/know-basics-of-remote-sensing”][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row margintop=”0″ marginbottom=”15″ paddingtop=”0″ paddingbottom=”0″ bg_color=”#ffffff” bg_repeat=”repeat” bg_stretch=”no” en_border=”no” border_color=”#e8e8e8″][vc_column][vc_column_text]Table of Contents: Step 1: Calculating Reflectance value from the Satellite data Step 2: Correcting the Reflectance value with sun angle Step 3: Calculating NDVI from 4th and 5th bands Step 4: Calculating SAVI from Reflectance Step 5: Calculating Tasseled cap indices Step 6: Tasseled cap […]<\/p>\n","protected":false},"author":1,"featured_media":1434,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4,288],"tags":[31,305,304,301,298,302,303,306,299,300],"class_list":["post-1384","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-remote-sensing","tag-arcgis","tag-arcgis-raster-calculator","tag-bands","tag-landsat-8","tag-ndvi","tag-nir","tag-red","tag-reflectance","tag-sdvi","tag-tasseled-cap-indices","entry","has-media"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/posts\/1384","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/grindgis.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1384"}],"version-history":[{"count":0,"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/posts\/1384\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/grindgis.com\/index.php?rest_route=\/wp\/v2\/media\/1434"}],"wp:attachment":[{"href":"https:\/\/grindgis.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1384"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/grindgis.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1384"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/grindgis.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1384"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}