Methodology for optimizing quadrat size in sparse vegetation surveys: A desert case study from the Tarim Basin

Area of Science:

• Ecology
• Remote Sensing
• Geospatial Analysis

Background:

• Random sampling is crucial for vegetation surveys but difficult in arid environments.
• Sparse and uneven desert vegetation complicates quadrat size selection.
• Traditional methods for collecting species-area relationship (SAR) data are labor-intensive.

Purpose of the Study:

• To develop a methodology for optimizing quadrat size in desert vegetation surveys.
• To leverage unmanned aerial vehicle (UAV) technology for efficient vegetation data acquisition.
• To enhance the accuracy and efficiency of desert vegetation sampling.

Main Methods:

• Utilized low-altitude, high-precision UAV imagery for vegetation data collection.
• Simulated random sampling within the Daliyaboyi Oasis study area.
• Analyzed the frequency distribution and variation of fractional vegetation cover (FVC) index.

Main Results:

• Identified 50 m × 50 m quadrats as the most representative size for the study area.
• Demonstrated the efficiency of UAV technology in overcoming desert climate and terrain challenges.
• Showcased the benefit of sampling simulation for large sample sizes.

Conclusions:

• The proposed methodology effectively optimizes quadrat size for desert vegetation surveys.
• UAV-based sampling significantly improves efficiency and accuracy compared to traditional methods.
• This approach is highly suitable for accurate vegetation estimation in challenging environments.