Automated classification of an environmental sensitivity index

Area of Science:

• Marine biology
• Environmental science
• Geographic Information Systems (GIS)

Background:

• Environmental Sensitivity Indices (ESI) are crucial for monitoring and control systems, requiring extensive field data.
• A previous ESI for the German Wadden Sea was developed using semi-manual expert analysis of field data.
• Regular updates of ESI are necessary but challenging due to the manual data analysis process.

Purpose of the Study:

• To develop an automated algorithm that emulates human expert decisions for classifying ESI sensitivity classes.
• To enable efficient and regular updates of ESI determination with new field data.
• To identify erroneous or extremely rare field data points.

Main Methods:

• Development of a novel algorithm designed to replicate expert judgment in ESI classification.
• Tuning of algorithm parameters to optimize performance.
• Validation of the algorithm against expert-classified data at numerous locations.

Main Results:

• The algorithm achieved 97% agreement with human expert decisions across tested locations.
• Automated classification procedures allow for efficient and regular ESI updates.
• The algorithm successfully identifies erroneous or extremely infrequent field data.

Conclusions:

• The presented algorithm offers a reliable and automated method for ESI determination in the German Wadden Sea.
• This automation significantly improves the efficiency of ESI updates and data quality control.
• The tool supports environmental authorities in their monitoring and management efforts.