Fisheye-Based Forest LAI Field Measurements for Remote Sensing Validation at High Spatial Resolution

Leaf area index (LAI) field measurements based on digital hemispherical photography (DHP) and LAI-2200 instruments have been widely used for remote sensing validation in forestry. Both DHP and LAI-2200 utilize fish-eye lens to capture the largest footprint of a canopy with a wide range of view zenith angle (VZA). However, accurately measuring field LAI at high spatial resolution poses a significant challenge since the view scope of fish-eye sensor is much larger than the size of high spatial resolution pixel. Therefore, selecting appropriate VZA ranges is crucial to address this issue. In this letter, we propose an improved geometry-based method that considers the average tree height, crown depth, and high-resolution pixel size. To validate this method, we designed four simulated forest scenes with different crown shapes through the LargE-Scale Remote Sensing Data and Image Simulation Framework (LESS) model and conducted field measurements. The results indicate that our proposed method significantly enhances the accuracy of fisheye-based LAI field measurements at high spatial resolution compared to previous method, with an almost 70% reduction in RMSE. In addition, our method exhibits greater improvement in measuring forest LAI at high resolution with DHP (RMSE < 0.3) compared to LAI-2200 (RMSE < 0.5). In conclusion, our method holds great potential in accurately measuring fisheye-based forest LAI for remote sensing validation at high spatial resolution.

Automated summary

In this study, we propose an improved geometry-based method for accurately measuring fisheye-based forest LAI at high spatial resolution. Our method considers average tree height, crown depth, and high-resolution pixel size to enhance accuracy. Experimental results show that our method significantly reduces the root mean square error and achieves better results when using different instruments for measurement.