Agricultural practices in grasslands detected by spatial remote sensing

Area of Science:

• Agricultural Science
• Environmental Science
• Remote Sensing

Background:

• Grassland surfaces have significantly decreased globally over the past 50 years due to management changes.
• These changes impact environmental and socio-economic systems, necessitating effective monitoring methods.

Purpose of the Study:

• To identify grassland management practices (mowing, grazing) in an intensive agricultural watershed in Brittany, France.
• To analyze the intra-annual vegetation dynamics using remotely sensed and field data.
• To assess the utility of Leaf Area Index (LAI) derived from satellite imagery for monitoring these practices.

Main Methods:

• Correlated vegetation indices (NDVI) and biophysical variables (LAI, fCOVER) from SPOT satellite images with field measurements from 120 grasslands.
• Utilized the K-Nearest Neighbor (KNN) supervised algorithm to classify LAI values.
• Employed a series of three SPOT images to capture intra-annual vegetation dynamics.

Main Results:

• Leaf Area Index (LAI) proved to be the most effective predictor for monitoring grassland mowing and grazing.
• LAI estimated from remote sensing data is a relevant variable for identifying grassland management practices.
• The KNN algorithm accurately mapped grassland management practices (grazing, mowing) at a field scale with a Kappa index of 0.82.

Conclusions:

• Satellite-derived LAI is a valuable tool for accurately mapping grassland management practices across large agricultural areas.
• This approach provides crucial data for understanding the environmental impacts of grassland changes.
• The study demonstrates the effectiveness of remote sensing for agricultural monitoring and environmental management.