Basic NDVI Analysis

Basic NDVI Analysis#

A comprehensive guide to vegetation monitoring with NDVI (Normalized Difference Vegetation Index).

What is NDVI?#

NDVI measures vegetation health by comparing red and near-infrared (NIR) light reflected by plants:

\[ NDVI = \frac{NIR - Red}{NIR + Red} \]

  • Values: -1 to +1

  • Interpretation:

    • < 0: Water, clouds, snow

    • 0 - 0.2: Bare soil, sand

    • 0.2 - 0.5: Sparse vegetation, grassland

    • 0.5 - 0.8: Dense vegetation, healthy crops

    • 0.8: Very dense vegetation, forests

Basic NDVI Workflow#

Step 1: Setup#

import ee
import geemap
from ndvi2gif import NdviSeasonality
import matplotlib.pyplot as plt

# Initialize
ee.Initialize(project='your-project-id')

Step 2: Define Study Area#

# Example: Agricultural area in Spain
roi = ee.Geometry.Rectangle([-6.2, 37.8, -6.0, 38.0])

# Visualize
Map = geemap.Map()
Map.addLayer(roi, {}, 'Study Area')
Map.centerObject(roi, 11)
Map

Step 3: Create NDVI Processor#

ndvi = NdviSeasonality(
    roi=roi,
    sat='S2',              # Sentinel-2 (10m resolution)
    periods=12,            # Monthly composites
    start_year=2023,
    end_year=2024,
    index='ndvi',
    key='median'           # Robust to clouds (cloud filtering is on by default)
)

Step 4: Generate Composite#

# get_year_composite() returns an ImageCollection (one image per year)
composites = ndvi.get_year_composite()
composite_2023 = composites.first()

# Check band names
print("Bands:", composite_2023.bandNames().getInfo())
# Output: ['january', 'february', 'march', 'april', 'may', 'june',
#          'july', 'august', 'september', 'october', 'november', 'december']

Step 5: Visualize Results#

# Visualization parameters
vis_params = {
    'bands': ['july', 'april', 'january'],  # RGB = Summer, Spring, Winter
    'min': 0,
    'max': 0.8
}

# Add to map (a 3-band RGB composite does not take a palette)
Map = geemap.Map()
Map.addLayer(composite_2023, vis_params, 'NDVI 2023')
Map.addLayer(roi, {}, 'ROI')
Map.centerObject(roi, 11)
Map

Step 6: Create Animated GIF#

# Generate temporal animation (one frame per year, RGB from three periods)
ndvi.get_gif(
    name='ndvi_monthly.gif',
    bands=['july', 'april', 'january']   # [R, G, B] periods
)

Advanced NDVI Analysis#

Multi-Year Comparison#

Compare vegetation patterns across years:

# Process multiple years
ndvi_multi = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=12,
    start_year=2020,
    end_year=2023,
    index='ndvi',
    key='median'
)

# One call returns all years (2020–2023) as an ImageCollection
composites = ndvi_multi.get_year_composite()
print("Years processed:", composites.size().getInfo())

Seasonal Analysis#

Focus on specific seasons:

# Quarterly analysis
ndvi_seasonal = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=4,  # Winter, Spring, Summer, Fall
    start_year=2023,
    end_year=2024,
    index='ndvi'
)

composite = ndvi_seasonal.get_year_composite().first()

# Visualize seasons (period names for periods=4: winter, spring, summer, autumn)
Map = geemap.Map()
Map.addLayer(
    composite.select('winter'),
    {'min': 0, 'max': 0.8, 'palette': ['red', 'yellow', 'green']},
    'Winter'
)
Map.addLayer(
    composite.select('summer'),
    {'min': 0, 'max': 0.8, 'palette': ['red', 'yellow', 'green']},
    'Summer'
)
Map.centerObject(roi, 11)
Map

High Temporal Resolution#

Bi-monthly for detailed crop monitoring:

ndvi_detailed = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=24,  # Every ~15 days
    start_year=2024,
    end_year=2024,
    index='ndvi',
    key='percentile',
    percentile=75
)

Statistical Methods for NDVI#

Maximum#

# Maximum greenness
ndvi_max = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=12,
    start_year=2023,
    end_year=2024,
    index='ndvi',
    key='max'  # Peak values
)

Percentile (Flexible)#

# 85th percentile - good balance
ndvi_p85 = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=12,
    start_year=2023,
    end_year=2024,
    index='ndvi',
    key='percentile',
    percentile=85
)

Extracting Statistics#

Zonal Statistics#

Extract mean NDVI per month:

# Get time series for ROI
composite = ndvi.get_year_composite().first()

# Extract values for each month
stats = []
for month in range(1, 13):
    band_name = composite.bandNames().get(month-1).getInfo()

    value = composite.select(band_name).reduceRegion(
        reducer=ee.Reducer.mean(),
        geometry=roi,
        scale=10,
        maxPixels=1e9
    ).getInfo()

    stats.append({
        'month': month,
        'ndvi': value[band_name]
    })

# Plot
import pandas as pd
df = pd.DataFrame(stats)
df.plot(x='month', y='ndvi', kind='line', marker='o')
plt.title('Mean NDVI by Month')
plt.xlabel('Month')
plt.ylabel('NDVI')
plt.grid(True)
plt.show()

Point Sampling#

Extract NDVI at specific locations:

# Define sample points
points = ee.FeatureCollection([
    ee.Feature(ee.Geometry.Point([-6.1, 37.9]), {'name': 'Site A'}),
    ee.Feature(ee.Geometry.Point([-6.15, 37.95]), {'name': 'Site B'})
])

# Sample composite
composite = ndvi.get_year_composite().first()
samples = composite.sampleRegions(
    collection=points,
    scale=10
).getInfo()

print(samples['features'])

Export Options#

Export as GeoTIFF#

# Exports each year's composite; filenames auto-generated as {sat}_{index}_{key}_{year}.tif
ndvi.get_export(scale=10)

Export to Google Drive#

composite = ndvi.get_year_composite().first()

ndvi.export_to_drive(
    image=composite,
    description='ndvi_2023',
    folder='earthengine_exports',
    scale=10,
    crs='EPSG:4326'
)

Interpretation Guide#

Agricultural Applications#

  • Early Season (Mar-Apr): NDVI 0.2-0.4 indicates crop emergence

  • Peak Season (Jun-Jul): NDVI > 0.6 shows healthy, mature crops

  • Harvest (Sep-Oct): NDVI drops < 0.3 after harvest

  • Bare Soil (Nov-Feb): NDVI ~ 0.1-0.2

Natural Vegetation#

  • Evergreen Forests: Consistent NDVI 0.7-0.9 year-round

  • Deciduous Forests: NDVI varies 0.3 (winter) to 0.8 (summer)

  • Grasslands: NDVI 0.3-0.6, responsive to rainfall

  • Wetlands: Variable, influenced by water levels

Common Issues & Solutions#

Issue: Low NDVI values everywhere#

Cause: Cloud contamination or wrong bands

Solution:

# Enable cloud masking
ndvi = NdviSeasonality(
    roi=roi,
    sat='S2',
    periods=12,
    start_year=2023,
    end_year=2024,
    index='ndvi',
    cloud_filter=True,     # Cloud filtering (on by default)
    max_cloud_cover=10     # ← Stricter scene cloud threshold
)

Issue: Patchy results#

Cause: Insufficient images

Solution: Use longer time periods or lower percentiles:

# Extend time period
start_year=2022, end_year=2024  # More images

# Or use median instead of max
key='median'

Issue: Negative NDVI on land#

Cause: Water bodies or very bare soil

Solution: This is normal! Mask water if needed:

# Mask water bodies
import ee

# Add water mask (example)
water = ee.Image('JRC/GSW1_3/GlobalSurfaceWater').select('occurrence')
composite_masked = composite.updateMask(water.lt(50))  # Keep only low water occurrence

Next Steps#