Alex Lee

Retrieving forest biomass through integration of CASI and LiDAR data

To increase understanding of forest carbon cycles and stocks, estimates of total and component (e.g. leaf, branch and trunk) biomass at a range of scales are desirable. Focusing on mixed species forests in central south‐east Queensland, two different approaches to the retrieval of biomass from small footprint Light Detection and Ranging (LiDAR) and Compact Airborne Spectrographic Imager (CASI) hyperspectral data were developed and compared. In the first, stems were located using a LiDAR crown openness index, and each was associated with crowns delineated and identified to species using CASI data. The component biomass for individual trees was then estimated using LiDAR‐derived height and stem diameter as input to species‐specific allometric equations. When summed to give total above‐ground biomass (AGB) and aggregated to the plot level, these estimates showed a reasonable correspondence with ground (plot‐based) estimates (r 2 = 0.56, RSE = 25.3 Mg ha−1, n = 21) given the complex forest being assessed. In the second approach, a Jackknife linear regression utilizing six LiDAR strata heights and crown cover at the plot‐scale produced more robust estimates of AGB that showed a closer correspondence with plot‐scale ground data (r 2 = 0.90, RSE = 11.8 Mg ha−1, n = 31). AGB aggregated from the tree‐level and Jackknife regression plot‐based AGB estimates (for 270 plots—each of 0.25 ha) compared well for more mature homogeneous and open forests. However, at the tree level, AGB was overestimated in taller forests dominated by trees with large spreading crowns, and underestimated AGB where an understorey with a high density of stems occurred. The study demonstrated options for quantifying component biomass and AGB through integration of LiDAR and CASI data but highlighted the requirement for methods that give improved estimation of tree density (by size class distributions) and species occurrence in complex forests.

Enhanced Simulation of Radar Backscatter From Forests Using LiDAR and Optical Data

Focusing on a forest dominated by Poplar Box (Eucalyptus populnea) near Injune in Queensland, Australia, light detection and ranging (LiDAR) and optical remote sensing data are integrated with tree- and stand-level information to parameterize a coherent L-band synthetic aperture radar (SAR) imaging simulation that models microwave penetration and interaction with the canopy, understory, and ground. The approach used LiDAR data to generate a three-dimensional representation of the distribution of tree components (leaves and small branches) by species (based on 1-m3 voxels) and the ground surface. Tree trunks were mapped across a 7.5-ha forest stand using a LiDAR-derived height-scaled crown openness index. Primary and secondary branches were modeled as tapering cylinders and linked the canopy voxels to the LiDAR trunks. The dimensions of vegetation and soil components and their geometric and dielectric properties required by the model were calibrated with field-based measurements. Visual and numerical comparison between NASA JPL Airborne SAR data and the model simulation suggests the effective modeling of SAR imagery at L-band. The study provides a proof-of-concept approach for integrating LiDAR data in the parameterization of coherent SAR simulation models, and the model presents options for better understanding of the information content of SAR data in real forest situations

Use of airborne scanning lidar and large scale photography within a strategic forest inventory and monitoring framework

Australia has approximately 160 million hectares of forest and woodland, 70% of which is under private ownership. Increasing commitments in relation to climate change and sustainable forest management, particularly in the private sector, is creating an environment where rapid, cost-effective assessment and monitoring of forests is becoming critical. Using an example from Injune, central Queensland, this paper provides an overview of the potential use of both airborne scanning lidar and large scale photography as sampling tools. The capability of these sensors for extending and optimising ground-truth data and, in turn, maximising the potential of other forms of remote sensing through improved calibration and validation, is outlined.

Organic carbon partitioning in soil and litter in subtropical woodlands and open forests: a case study from the Brigalow Belt, Queensland

A woodland–open forest landscape within the Brigalow Belt South bioregion of Queensland, Australia, was surveyed for soil organic carbon, soil bulk density and soil-surface fine-litter carbon. Soil carbon stocks to 30 cm depth across 14 sites, spanning a range of soil and vegetation complexes, ranged from 10.7 to 61.8 t C/ha, with an overall mean of 36.2 t C/ha. Soil carbon stocks to 100 cm depth ranged from 19.4 to 150.5 t C/ha, with an overall mean of 72.9 t C/ha. The standing stock of fine litter ranged from 1.0 to 7.0 t C/ha, with a mean of 2.6 t C/ha, and soil bulk density averaged 1.4 g/cm3 at the soil surface, and 1.6 g/cm3 at 1 m depth. These results contribute to the currently sparse database of soil organic carbon and bulk density measurements in uncultivated soils within Australian open forests and woodlands. The estimates of total soil organic carbon stock calculated to 30 cm depth were further partitioned into resistant plant material (RPM), humus (HUM), and inert organic matter (IOM) pools using diffuse mid-infrared (MIR) analysis. Prediction of the HUM and RPM pools using the RothC soil carbon model agreed well with the MIR measurements, confirming the suitability of RothC for modelling soil organic carbon in these soils. Methods for quantifying soil organic carbon at landscape scales were also explored, and a new regression-based technique for estimating soil carbon stocks from simple field-measured soil attributes has been proposed. The results of this study are discussed with particular reference to the difficulties encountered in the collection of the data, their limitations, and opportunities for the further development of methods for quantifying soil organic carbon at landscape scales.

A New Dataset for Forest Height Across Australia: Pilot Project to Calibrate ICESat Laser Data with Airborne LiDAR

To better quantify and monitor the extent, structure and biomass of Australian forests, accurate cover and height information is required, yet only a small proportion of Australia’s forests have reliable height information. The use of airborne laser survey using Light Detection and Ranging (LiDAR) data has rapidly developed and has demonstrated its effectiveness and high accuracy for forests height measurement. However it is expensive and data is not yet widely available for many areas. A recent source of height data is now available from the NASA ICESat satellite. The ICESat laser pulses give approximately 70 m diameter footprints, spaced at 170-m intervals along the Earths surface. Tracks are spaced about 50 km wide, and since 2003 over 2 million points across Australia have been imaged. These could provide significant potential for improving vegetation structure assessment, and monitoring both natural and human induced change. A pilot project utilised three sites where coincident airborne LiDAR was available; in NE Victoria, south-central Queensland, and along the Brisbane River (Queensland). Ground elevation correspondence gave a mean difference < 2 m (ICESat higher than LiDAR), with woodlands recording a difference of 0.16 m. For forest structural attributes, ICESat gave reliable estimates (i.e. within 2 m for height and 10% for cover) in some cases, but the results were dependant on the density and height of the forest, and terrain slope within the footprint, thus making the extraction inconsistent between structural metrics. In sparser forests, ICESat tends to report foliage projective cover, whereas in dense forests, crown cover equivalent values are recorded. An apparent threshold of improved accuracy when cover was higher than 30% was observed. Further research is required to better define the thresholds where ICESat does not produce reliable results. Whilst ICESat appears to be unsuited to continental application for national reporting of both height and cover until further calibration across a greater range of forest environments is undertaken, however ongoing research efforts to improve the calibration are showing promise.

The role of LiDAR data in understanding the relation between forest structure and SAR imagery

As part of the 2000 PACRIM II Mission to Australia, polarimetric Synthetic Aperture Radar (SAR) data were acquired near Injune, central Queensland, Australia. The primary purpose of the acquisition was to better understand the role of SAR for retrieving biophysical properties of native forests through either empirical relationships or simulation modeling. In this paper, we outline the generation of a three-dimensional representation of the forest structure and component elements (leaves, branches and trunks) using field, airborne LiDAR and CASI data and aerial photography acquired at the same time as the AIRSAR. We then show how this representation formed the basis of the input to a coherent SAR imaging simulation that models microwave penetration and interaction with canopy and understorey components. A preliminary comparison between actual AIRSAR and simulated SAR data for a poplar box (Eucalyptus populnea) woodland suggests effective modelling of SAR backscatter.

Estimation of land cover and biomass change from remotely sensed data

Reports on progress in estimating land cover change, using aerial photography and satellite imagery and biomass change, based on field data, lidar and satellite imagery, in the Fitzroy catchment, Australia.

Remote sensing to support Australia's commitment to international agreements: a role for synthetic aperture radar

Using mixed species woodlands in central Queensland, Australia, as an example, this paper provides a broad overview of the potential role of Synthetic Aperture Radar (SAR) in the national accounting of carbon within the land use, land use change and forestry sector.