Remotely sensed data for ecosystem analyses: combining hierarchy theory and scene models.

Area of Science:

• Environmental Science
• Remote Sensing
• Geospatial Analysis

Background:

• Remotely sensed data are crucial for environmental monitoring and modeling across various spatial scales.
• Historically, limited satellite imaging systems constrained the scale of these analyses.
• Increased availability of diverse datasets now allows for scale-appropriate data selection.

Purpose of the Study:

• To present a framework for environmental scientists and managers to link spatial data collection needs with suitable remotely sensed data.
• To provide an objective mechanism for selecting appropriate remote sensing data and analysis techniques.

Main Methods:

• A six-step approach integrating image spatial analysis and scaling tools within hierarchy theory.
• Steps include: identifying information requirements, developing a scene model, exploratory data analysis, data selection/evaluation, choosing analytical techniques, and cost-benefit analysis.

Main Results:

• A case study demonstrated the framework's effectiveness.
• The framework objectively identified relevant monitoring problem aspects and environmental characteristics.
• It facilitated the selection of suitable remotely sensed data and analysis techniques.

Conclusions:

• The presented framework offers a structured approach for optimizing remotely sensed data selection in environmental science.
• It addresses the challenge of matching data resolution and scale to specific environmental monitoring and management objectives.
• The methodology ensures more effective and efficient use of geospatial technologies for environmental applications.