NDVI Analysis

Last updated on 2025-12-05 | Edit this page

Overview

Questions

  • What is NDVI and why is it useful?
  • How do we calculate NDVI from Landsat imagery?
  • How do we load and visualize raster data in Python?
  • How can we classify and map greenness using NDVI?

Objectives

  • Understand NDVI and the spectral bands needed to compute it
  • Learn to read geospatial raster files using rasterio
  • Calculate NDVI using Red & NIR bands from Landsat
  • Visualize NDVI as a map with color gradients
  • Create a simple vegetation classification from NDVI values

Why is NDVI Important?

NDVI is one of the most widely used vegetation indices in remote sensing because it provides a simple yet powerful way to assess plant health and landscape greenness over large areas. Healthy vegetation strongly reflects Near-Infrared (NIR) light and absorbs Red light for photosynthesis — NDVI takes advantage of this behavior to quantify vegetation vigor.

What NDVI Helps Us Understand

  • Crop health and agricultural productivity
  • Drought severity and water stress
  • Forest cover and vegetation density
  • Urban expansion and land use change
  • Seasonal phenology (spring green-up, fall senescence)
  • Disaster monitoring (wildfire burn severity, storm damage)

Why Researchers Use NDVI

  • It is easy to compute from satellite imagery
  • Works across multiple sensors (Landsat, Sentinel-2, MODIS, etc.)
  • Allows temporal comparison (year-to-year vegetation trends)
  • Useful for ecosystem monitoring & climate change studies
  • Enables land cover classification and biomass estimation
  • Supports decision-making in agriculture and forestry

NDVI Interpretation at a Glance

NDVI Range Interpretation Example Areas
-1 to 0 Water, snow, clouds, barren Lakes, rivers
0–0.2 Bare soil, built-up land Urban areas, deserts
0.2–0.5 Moderate vegetation Grasslands, shrubs
> 0.5 Dense, healthy vegetation Forests, croplands

NDVI is therefore a foundation metric in environmental science — enabling researchers, planners, and ecologists to visualize vegetation patterns, track change through time, and make data-driven decisions about land and resources.

In this lesson, we will compute NDVI for Indiana using Landsat bands and generate maps with Python.

1. Installing Required Libraries

PYTHON 

!pip install rasterio matplotlib numpy

2. Import Dependencies

PYTHON 

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

3. Load Landsat RED and NIR Bands

Make sure your directory contains Landsat .TIF files (Band 4 = Red, Band 5 = NIR).

PYTHON 

red = rasterio.open("LC08_L1TP_red.tif")
nir = rasterio.open("LC08_L1TP_nir.tif")

red_band = red.read(1).astype('float32')
nir_band = nir.read(1).astype('float32')

Plot a band to inspect:

PYTHON 

plt.imshow(red_band, cmap='Reds')
plt.title("Red Band")
plt.colorbar()
plt.show()

4. Calculate NDVI

PYTHON 

ndvi = (nir_band - red_band) / (nir_band + red_band)

Visualize NDVI:

PYTHON 

plt.figure(figsize=(7,6))
plt.imshow(ndvi, cmap='YlGn')
plt.colorbar(label="NDVI Value")
plt.title("NDVI Map of Indiana")
plt.show()

5. Classify NDVI into Vegetation Categories

PYTHON 

ndvi_class = np.digitize(ndvi, bins=[0, 0.2, 0.5])

# 0 = water/barren, 1 = low vegetation, 2 = dense vegetation
colors = ['blue', 'yellow', 'green']
plt.imshow(ndvi_class, cmap=plt.matplotlib.colors.ListedColormap(colors))
plt.title("NDVI Vegetation Classification")
plt.show()
Challenge

Challenge 1 — Try It Yourself

  • Change the NDVI color map (cmap)

  • Classify NDVI into four categories instead of three

  • Add labels or legends to your final map

PYTHON 

bins = [0, 0.2, 0.4, 0.6]
ndvi_class = np.digitize(ndvi, bins=bins)
Challenge

Challenge 2 — Mask Water Pixels

Use NDVI to mask water (<0):

PYTHON 

water_mask = ndvi < 0
ndvi_water_removed = np.where(water_mask, np.nan, ndvi)

plt.imshow(ndvi_water_removed, cmap='YlGn')
plt.title("NDVI with Water Masked")
plt.show()

Water regions become transparent/ignored in the plot.

Callout

NDVI is affected by seasonality, cloud cover, and atmospheric effects. Always check metadata to ensure you’re comparing compatible scenes.

Math

NDVI uses reflectance difference between two bands:

NDVI = (NIR - RED)/(NIR + RED)

NIR increases with vegetation health — higher NDVI = greener land.

Key Points

  • NDVI uses Red & NIR reflectance from satellite imagery

  • Landsat Band 4 = Red, Band 5 = NIR for NDVI

  • NDVI ranges from -1 (water) to +1 (healthy vegetation)

  • Python tools: rasterio, numpy, matplotlib

  • NDVI maps reveal vegetation patterns visually and quantitatively

Module Overview

Lesson Overview
Beginner Introduction to NDVI using LANDSAT Dataset.
Advanced (to be added)