Automating analysis of vegetation with computer vision: Cover estimates and classification

Area of Science:

• Ecological monitoring
• Computer vision applications
• Pattern recognition in ecology

Background:

• Traditional vegetation cover estimation methods (quadrat-based) lack consistency across observers, sites, and time.
• Previous automated cover estimation from photographs required significant manual input.
• Ecological studies rely on accurate vegetation cover data for understanding ecosystem dynamics.

Purpose of the Study:

• To develop and validate an automated system for estimating vegetation cover and type using computer vision.
• To address the limitations of manual and inconsistent visual cover estimation methods.
• To provide a repeatable, cost-effective, and reliable tool for long-term vegetation monitoring.

Main Methods:

• Utilized computer vision and pattern recognition algorithms for automated vegetation cover estimation.
• Employed top-down photographs of 1m x 1m quadrats.
• Modeled vegetation color distribution using a multivariate Gaussian for cover estimation.
• Classified vegetation types (graminoids/grasses and forbs) using illumination-robust local binary pattern features.

Main Results:

• Automated estimates of grass and forb cover effects aligned with field estimates for most treatments (8/9).
• Total vegetation cover estimates showed less agreement, particularly at productive grassland sites.
• The system demonstrated high repeatability and reduced manual labor compared to traditional methods.

Conclusions:

• Automated vegetation cover estimation using computer vision is a viable and effective alternative to manual methods.
• This approach enhances the reliability, cost-efficiency, and scalability of ecological monitoring.
• The developed system offers potential for increased spatial and temporal resolution in vegetation sampling.