Fusing street level photographs and satellite remote sensing to map leaf area index

Abstract

Leaf area index (LAI) is an important structural parameter of vegetation, and is used in many models of climate and ecosystem services. Maps of LAI are typically produced by relating satellite remote sensing with field-based measurements of LAI, but such field measurements are time consuming to collect over large areas. In this study we develop a rapid and scalable method for mapping LAI by fusing high-resolution freely-available Sentinel 2 satellite imagery with ground measurements of LAI extracted from a large publicly available database of street-level panoramic photographs. The use of existing street-level photographs allowed large numbers of training data to be automatically obtained. The method developed here was validated against a field dataset collected using established techniques. The use of existing online databases of street-level photographs will allow rapid mapping of LAI in many situations worldwide. The technique developed here may be particularly useful in cities, which have high heterogeneity in vegetation, and high densities of street level photographs collected along road networks. The approach could be applied to map LAI at high resolution across very large areas, for national- or continental-scale comparison.