A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data

Peter Scarth,Peter Bunting,John Armston,Richard Lucas

doi:10.3390/rs11020147

Abstract

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.

Highlights

A large number of studies have focused on quantifying the height of woody vegetation at regional to global scales from remote sensing data, with these based primarily on radar interferometry (e.g., the Shuttle Radar Topographic Mission (SRTM) from 2000) and Tandem-X or spaceborne LiDAR
Wall-to-wall mapping of vegetation height was achieved by [7], who used the eCognition software to generate a nested segmentation of the landscape based on the SRTM National Elevation Dataset (NED)-derived slope, the SRTM-NED difference and the National Land Cover Database (NLCD) of canopy density
The orthorectification utilized a digital elevation model (DEM) simulated from SRTM data and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) strips acquired during periods of relatively low surface moisture were used to generate the mosaic as proposed by Lucas et al [21]

Summary

Introduction

A large number of studies have focused on quantifying the height of woody vegetation at regional to global scales from remote sensing data, with these based primarily on radar interferometry (e.g., the Shuttle Radar Topographic Mission (SRTM) from 2000) and Tandem-X (from 2010 onwards) or spaceborne LiDAR (namely, the ICESat/GLAS from 2003–2008). Wall-to-wall mapping of vegetation height was achieved by [7], who used the eCognition software to generate a nested segmentation of the landscape based on the SRTM National Elevation Dataset (NED)-derived slope, the SRTM-NED difference and the National Land Cover Database (NLCD) of canopy density. They extrapolated associated airborne Laser Vegetation Imaging Sensor (LVIS) metrics across segments based on an inverse weighted distance. Ørka et al [8] generated a wall-to-wall coverage of canopy cover by extrapolating estimates from airborne LiDAR data using a random forest, non-parametric classification of Landsat-derived Normalized Difference Vegetation Index (NDVI) data, Tasseled Cap transformed brightness and wetness, and elevation and slope data

Methods

Results

Discussion

Conclusion