AN INTERCOMPARISON OF PASSIVE TERRESTRIAL REMOTE SENSING TECHNOLOGIES TO DERIVE LAI AND CANOPY COVER METRICS
Keywords: LIDAR, Forestry, Sustainable, Multisensor, Scale, Terrestrial
Abstract. Forest indicators such as Leaf Area Index (LAI) and vegetation cover type are recognised as Essential Climate Variables (ECVs) which support the '…research, modelling, analysis, and capacity-building activities…' requirements of the United Nations Framework Convention on Climate Change. This research compares the use of passive terrestrial remote sensing technologies for LAI and canopy cover metrics. The passive sensors used are the LAI-2200 and Digital Hemispherical Photography (DHP). The research was conducted at a Victorian reference site containing tree species with predominantly erectophile leaf angle distributions, which are significantly under-represented in the literature. The reference site contributes to a network of sites representative of Victorian land systems and is considered to be in good condition. Preliminary results indicate a low correlation (R2=0.46) between the LAI-2200 and DHP. Further comparisons to be conducted include adding a passive CI-110 plant canopy analyser and an active Terrestrial Laser Scanner. The future objective is to scale the in situ data to aerial and satellite remotely sensed datasets. The aerial remotely sensed data include LiDAR flown by a Riegl LMS Q560, and high resolution multispectral and hyperspectral imagery flown by the ASIA Eagle and Hawk system. The in situ data can be utilised for both calibration and validation of the coincident aerial imagery and LiDAR. Finally, the derived datasets are intended for use to validate the global MODIS LAI product.