Attributing Woody Landscapes Using Remote Sensing
ISBN 978-85-88783-11-9
Authors
1Jones, S.; 2Suarez, L.; 3Soto-Berelov, M.; 4Wilkes, P.; 5Woodgate, W.; 6Haywood, A.; 7Mellor, A.
1Centre for Remote Sensing Email: simon.jones@rmit.edu.au
2Centre for Remote Sensing
3Centre for Remote Sensing
4Centre for Remote Sensing
5Centre for Remote Sensing
6Forest and Parks Division, Department of Sustainability and Environmen
7Centre for Remote Sensing
Abstract
This paper presents a methodology for the attribution and characterisation of forested landscapes. First we derive a set of woody vegetation data primitives (e.g. canopy cover, leaf area index, bole density, canopy profiles), which are then scaled up using multiple remote sensing data sources to characterise and extract landscape woody vegetation features. The advantage of this approach is that vegetation landscape features can be described using a variety of description systems which are composites of these data primitives. The proposed data primitives act as building blocks for the re-creation of past woody characterisation schemes as well as allowing for re-compilation to support present and future policy and management and decision making needs.Three research sites were attributed each representative of different Australian sclerophyll woody vegetated systems (Box Iron-bark forest; Mountain Ash forest; Mixed Species foothills forest). High resolution hyperspectral and full waveform LIDAR data was acquired over the three research sites. At the same time, land management agencies (Victorian Department of Sustainability and Environment) and researchers (RMIT, CRC for Spatial Information and CSIRO) conducted fieldwork to collect structural and functional measurements of vegetation, using traditional forest mensuration transects and plots, terrestrial lidar scanning and high temporal resolution in-situ autonomous laser (VegNet) scanners and dendrometers.Results are presented 1) inter-comparisons of LAI estimations made using ground based hemispherical photography, PCA LAI 2200, CI-110 and terrestrial and airborne laser scanners; 2) canopy height profiles derived from airborne LiDAR and validated using ground observations.