Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index

Abstract

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer‐ and hardwood‐dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer‐ and hardwood‐dominated stands are separated, image spatial information estimates tree crown size much better for conifer‐dominated stands than for the hardwood‐dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information.

Acknowledgements

This research was partly supported by NSF grant 0351430, USDA Forest Service Agenda 2020 and NASA grant NNX06AE28G. The initial manuscript was completed while C. S. was a Charles Bullard Fellow in Forest Research at Harvard Forest, Harvard University from 01 September 2005 to 31 May 2006. The authors acknowledge the assistance from three undergraduate interns for field data collection during the summer of 2005: Ms Andrea Nifong, Ms Elizabeth Ward and Mr Bo Xiao.