Calculating NDVI from Sentinel 2 images for Bicuar National Park


I recently ventured into trying to make sense of sentinel 2 data, multispectral remote sensing imagery. I wanted to calculate NDVI for Bicuar National Park, so I could see whether it’s possible to identify areas of miombo woodland within the park using variation in the NDVI, which you would expect is higher in woodland and lower in grassland.

I got some cloud free images for the area covering Bicuar and wrote a Python script which calculates NDVI, from the red band and near infra-red band:

# Import libraries
import glob
import gdal 
import os
import fnmatch
import re
import cv2

# Define a function to find files given a pattern
def find(pattern, path):
    result = []
    for root, dirs, files in os.walk(path):
        for name in files:
            if fnmatch.fnmatch(name, pattern):
                result.append(os.path.join(root, name))
    return result

# Set working directory for images
rootdir = '/sentinel_bicuar'

# Create a list of folders 
folders = next(os.walk(os.getcwd()))[1]

# Loop through each folder in turn
for i in folders:
  # Set input directory
  in_dir =  rootdir +  '/' + i

  # Search directory for desired bands
  red_file = find('*B04.jp2', in_dir)[0]
  print("Processing: " + red_file)
  nir_file = find('*B08.jp2', in_dir)[0]

  # Open each band using gdal
  red_link = gdal.Open(red_file)
  nir_link = gdal.Open(nir_file)

  # Store as an array
  red_array = red_link.GetRasterBand(1).ReadAsArray() * 0.0001
  nir_array = nir_link.GetRasterBand(1).ReadAsArray() * 0.0001
  # Create a mask filled with zeroes
  mask = red_array == 0.

  # Calculate NDVI 
  ndvi2 = (nir_array - red_array) / (nir_array + red_array)

  # Set mask values back to 0
  ndvi2[mask] = 0.

  # Create output filename based on input name 
  out_string_a ='A004323_(.*)/IMG_DATA', red_file).group(1)
  out_string_b ='IMG_DATA/(.*)_B04.', red_file).group(1)
  out_file = rootdir + '/' + out_string_a + '_' + out_string_b + '_NDVI.tif'
  print('Creating file: ' + out_file)

  # Get dimensions
  x_pixels = ndvi2.shape[0] # number of pixels in x
  y_pixels = ndvi2.shape[1] # number of pixels in y

  # Set up output GeoTIFF
  driver = gdal.GetDriverByName('GTiff')

  # Create driver using output filename, x and y pixels, # of bands, and datatype
  ndvi_data = driver.Create(out_file,x_pixels, y_pixels, 1, gdal.GDT_Float32)

  # Set nodata value

  # Set NDVI array as the 1 output raster band

  # Setting up the coordinate reference system of the output GeoTIFF
  geotrans=red_link.GetGeoTransform() # Grab input GeoTranform information
  proj=red_link.GetProjection() # Grab projection information from input file

  # now set GeoTransform parameters and projection on the output file


Then I use gdal to merge each of the resultant .tif files with an NDVI band into a single file, then clip that file with the outline of Bicuar National Park .


echo "Merging tif files" -n 0 -a_nodata 0 *_NDVI.tif -o ndvi_merge_o.tif

gdalwrap -t_srs '+proj=longlat +datum=WGS84' ndvi_merge_0.tif ndvi_merge_0_longlat.tif

gdalwarp -cutline  'bicuar_shp/WDPA_Mar2018_protected_area_350-shapefile-polygons.shp' -crop_to_cutline -dstalpha ndvi_merge_0_longlat.tif ndvi_merge_0_longlat_bicuar.tif
Bicuar NDVI

Then I can use an R script to look at the distribution of NDVI across the park

# Packages

# Import data ----
ndvi_tif_bicuar <- raster("ndvi_merge_0_longlat_bicuar.tif")

ndvi_vec <- getValues(ndvi_tif_bicuar)

hist(ndvi_vec, breaks = 100)
Bicuar histogram

I can also experiment with plotting areas of the park within a certain threshold of NDVI

ndvi_thresh <- ndvi_tif_bicuar[ndvi_tif_bicuar < 0.6] <- NA

Bicuar NDVI