Sentinel-2 Derived NDVI Analysis of Karachi Using Google Colab

Introduction: This study is about the analysis of Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 satellite imagery using Google Colab. The Sentinel-2 mission provides multispectral data that is crucial for monitoring vegetation health and changes over time. Leveraging Google Colab, a cloud-based Python environment, allows efficient processing and visualization of these satellite images without the need for local computational resources.

Methodology:

1. Data Retrieval: Sentinel-2 data is retrieved from the Sentinel Hub or Copernicus Open Access Hub and stored in Google Drive for easy access and organization.

2. Data Preprocessing: GeoTIFF files are accessed from Google Drive using Python libraries like rasterio. Bands relevant to NDVI calculation (typically NIR and Red bands) are selected and loaded into the Colab environment.

3. NDVI Calculation: NDVI is computed using the formula: NDVI=NIR-Red / NIR+Red​. This index helps in quantifying vegetation density and health.

4. Geospatial Visualization: The computed NDVI values are plotted on a geographic map using matplotlib. Geographic coordinates (latitude and longitude) are derived from the image metadata to accurately position the NDVI values spatially.

5. Analysis and Interpretation: The NDVI map provides insights into vegetation distribution, health, and changes over the observed area. This analysis can aid in environmental monitoring, agricultural planning, and ecosystem studies.

By utilizing Google Colab, this study ensures scalability, reproducibility, and accessibility of satellite image analysis, making it a valuable tool for remote sensing applications.

CODES:

Connect to gdrive:

from google.colab import drive

drive.mount(‘/content/drive’)

install rsterio

!pip install rasterio matplotlib

Import Libraries

import os

import rasterio

import matplotlib.pyplot as plt

import numpy as np

from rasterio.plot import show

from rasterio.warp import transform

connect folder that has geotiff files or sentinel bands

geotiff_dir = ‘/content/drive/MyDrive/SENT_KHI24/’

geotiff_files = [f for f in os.listdir(geotiff_dir) if f.endswith(‘.tif’)]

print(“Processing and displaying GeoTIFF files:”)

for file in geotiff_files:

print(file)

Individual band displys

for file in geotiff_files:

file_path = os.path.join(geotiff_dir, file)

with rasterio.open(file_path) as src:

data = src.read(1)

plt.figure(figsize=(10, 10))

plt.imshow(data, cmap=’gray’)

plt.colorbar()

plt.title(f’GeoTIFF Image — {file} — Band 1')

plt.show()

NDVI :

import rasterio

import matplotlib.pyplot as plt

import numpy as np

import os

from rasterio.plot import show

from rasterio.warp import transform

geotiff_dir = ‘/content/drive/MyDrive/SENT_KHI24/’

nir_band_file = ‘S2_Sindh_B8.tif’ # Adjust with your actual file names

red_band_file = ‘S2_Sindh_B4.tif’ # Adjust with your actual file names

if all(os.path.exists(os.path.join(geotiff_dir, band_file)) for band_file in [nir_band_file, red_band_file]):

with rasterio.open(os.path.join(geotiff_dir, nir_band_file)) as src_nir:

nir = src_nir.read(1)

nir_transform = src_nir.transform

nir_crs = src_nir.crs

with rasterio.open(os.path.join(geotiff_dir, red_band_file)) as src_red:

red = src_red.read(1)

ndvi = (nir — red) / (nir + red)

nrows, ncols = ndvi.shape

xs = np.linspace(0, ncols — 1, ncols)

ys = np.linspace(0, nrows — 1, nrows)

x_coords, y_coords = np.meshgrid(xs, ys)

lon, lat = rasterio.transform.xy(nir_transform, y_coords, x_coords, offset=’center’)

lon = np.array(lon)

lat = np.array(lat)

plt.figure(figsize=(10, 10))

plt.imshow(ndvi, cmap=’RdYlGn’, extent=(lon.min(), lon.max(), lat.min(), lat.max()))

plt.colorbar(label=’NDVI’)

plt.xlabel(‘Longitude’)

plt.ylabel(‘Latitude’)

plt.title(‘Normalized Difference Vegetation Index (NDVI)’)

plt.show()

else:

print(“One or more required band files are missing.”)