Bhuvan — ISRO Satellite Data¶

India's own satellite imagery — Cartosat, ResourceSat, LISS-III/IV — free through ISRO's Bhuvan portal.

Provider

ISRO / National Remote Sensing Centre (NRSC)

Website

bhuvan.nrsc.gov.in

Access Level

🟡 Free Registration Required

Formats Available

GeoTIFF, WMS, Shapefile

Key Sensors

Cartosat-3 (0.25m), LISS-IV (5.8m), LISS-III (23.5m)

Indian Satellite Sensors on Bhuvan¶

Satellite	Sensor	Resolution	Swath	Revisit
Cartosat-3	PAN	0.25 m	16 km	4 days
Cartosat-2S	PAN	0.65 m	9.6 km	4 days
Cartosat-1	PAN stereo	2.5 m	30 km	5 days
ResourceSat-2A	LISS-IV	5.8 m	23/70 km	5 days
ResourceSat-2A	LISS-III	23.5 m	141 km	24 days
OceanSat-3	OCM	360 m	1,420 km	Daily

Downloading Imagery from Bhuvan¶

Step 1: Register¶

Go to bhuvan.nrsc.gov.in → Register
Login → Go to "Thematic Data" → "Satellite Data"

Step 2: Find Your Area¶

Open "Bhuvan 2D" → Navigate to your area of interest
Use the "Time Slider" to browse imagery across years

Step 3: Download¶

Go to "Downloads" → "Satellite Data"
Select: Sensor → Date Range → Area
Download GeoTIFF

Alternative: Bhuvan App (Mobile)¶

Download the Bhuvan Mobile App — view satellite imagery of any location in India on your phone, including comparison of historical images.

Bhuvan Thematic Portals¶

Portal	URL	What It Has
Bhuvan Panchayat	bhuvan-panchayat.nrsc.gov.in	Village-level satellite + scheme data
Bhuvan Disaster	bhuvan.nrsc.gov.in/disaster	Flood/cyclone inundation maps
Bhuvan Urban	bhuvan.nrsc.gov.in/urban	Urban sprawl analysis
Bhuvan Forest	bhuvan.nrsc.gov.in/forest	Forest fire, deforestation alerts
Bhuvan PMGSY	bhuvanpmgsy.nrsc.gov.in	Rural road network GIS
National Wetland Atlas	bhuvan.nrsc.gov.in/wetland	Wetland mapping

Python: Visualise a Downloaded Bhuvan GeoTIFF¶

import rasterio
import numpy as np
import matplotlib.pyplot as plt

# Load a multi-band LISS-III image (B1=Green, B2=Red, B3=NIR, B4=SWIR)
with rasterio.open("liss3_pune_district.tif") as src:
    # Read bands (1-indexed in rasterio)
    green = src.read(1).astype(float)
    red   = src.read(2).astype(float)
    nir   = src.read(3).astype(float)
    transform = src.transform

# Handle no-data (replace 0s with NaN)
red[red == 0] = np.nan
green[green == 0] = np.nan
nir[nir == 0] = np.nan

# Calculate NDVI
ndvi = (nir - red) / (nir + red)

# False Colour Composite (NIR-Red-Green)
fcc = np.dstack([
    np.clip(nir / 2000, 0, 1),
    np.clip(red / 2000, 0, 1),
    np.clip(green / 2000, 0, 1)
])

fig, axes = plt.subplots(1, 2, figsize=(14, 6))

axes[0].imshow(fcc)
axes[0].set_title("False Colour Composite (NIR-R-G)\nVegetation = Red")
axes[0].axis('off')

im = axes[1].imshow(ndvi, cmap='RdYlGn', vmin=-0.2, vmax=0.8)
axes[1].set_title("NDVI — Vegetation Index")
axes[1].axis('off')
plt.colorbar(im, ax=axes[1], fraction=0.046, label='NDVI')

plt.suptitle("Bhuvan LISS-III Analysis — Pune District", fontsize=13)
plt.tight_layout()
plt.savefig("bhuvan_liss3_analysis.png", dpi=150, bbox_inches='tight')
plt.show()

✏️ Practice Exercise¶

Exercise 5.1 — Compare Your District: 2005 vs 2023

Goal: Use Bhuvan's time slider to visually compare land use change.

Open bhuvan.nrsc.gov.in → Bhuvan 2D
Navigate to your district
Open the Time Slider (bottom panel)
Move slider from 2005 to 2023
Observe changes — note any urban expansion, deforestation, or water body change

Deeper dive: - Download LISS-III image for 2005 and 2023 for your district - Load in QGIS → Calculate NDVI for both years - Use Raster Calculator: NDVI_2023 - NDVI_2005 to see where vegetation improved vs declined

Next Dataset: Sentinel-1 & Sentinel-2 →