Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Accurate estimation of forest Leaf Area Index (LAI) is crucial for understanding ecosystem function and health.
• Satellite remote sensing offers a scalable approach for monitoring forest LAI over large areas.

Purpose of the Study:

• To estimate the Leaf Area Index (LAI) of a Crimean pine forest ecosystem in Turkey.
• To compare the effectiveness of WorldView-2 and Aster satellite imagery for LAI estimation.
• To evaluate the utility of spectral bands versus vegetation indices (VIs) in predicting LAI.

Main Methods:

• Field data collection involved 108 sample plots in a Turkish forest stand.
• Hemispherical photography with a fish-eye camera was used to determine ground-truth LAI values.
• Multiple regression analysis modeled relationships between LAI and satellite-derived spectral data (bands and VIs).

Main Results:

• WorldView-2 satellite imagery demonstrated superior performance in predicting forest LAI compared to Aster imagery.
• Vegetation indices (VIs) derived from satellite data yielded more accurate LAI estimations than individual spectral bands.
• The study identified optimal remote sensing approaches for forest LAI assessment.

Conclusions:

• High-resolution satellite imagery, such as WorldView-2, is highly effective for precise forest LAI estimation.
• Utilizing vegetation indices significantly enhances the accuracy of satellite-based LAI predictions.
• These findings support the use of advanced remote sensing techniques for forest ecosystem monitoring.