Abstract
• Street-level photographs were fused with satellite data to map LAI. • The new method for LAI mapping was rapid and validated using field data. • The approach will allow high-resolution maps of LAI to be generated rapidly. 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.
Highlights
IntroductionDatabases of SLP typically contain 360° images of urban landscapes taken from the ground level (Anguelov et al, 2010; Yu et al 2018)
In this article we propose and test two methods for fusing SLP images with satellite-based remote sensing to map LAI; one that was developed for analysing normal photographs of vegetation canopies, and one designed for hemispherical photographs
Large SLP databases could provide a new source of data for characterising vegetation and the ecosystem services that it provides to people (Richards and Edwards, 2017)
Summary
Databases of SLP typically contain 360° images of urban landscapes taken from the ground level (Anguelov et al, 2010; Yu et al 2018). SLP images have been used to quantify parameters such as green cover in streets (Li et al, 2015), canopy cover, and the shade provided by tree canopies (Li et al, 2018; Richards and Edwards, 2017). These methods are well-suited for urban areas where SLP databases are well-curated and frequently updated. The objectives of the study were to (1) use SLP images as a data source to map LAI across a case study city, (2) assess the repeatability of the two methods by comparing the results from different subsets of sampled photographs, and (3) assess the accuracy of the SLP LAI methods against independently-obtained field data
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