@article {bnh-5056, title = {Limitations of high resolution satellite stereo imagery for estimating canopy height in Australian tropical savannas}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {75}, year = {2019}, month = {03/2019}, pages = {12}, chapter = {83}, abstract = {

Obtaining reliable measures of tree canopy height across large areas is a central element of forest inventory and carbon accounting. Recent years have seen an increased emphasis on the use of active sensors like Radar and airborne LiDAR (light detection and scanning) systems to estimate various 3D characteristics of canopy and crown structure that can be used as predictors of biomass. However, airborne LiDAR data are expensive to acquire, and not often readily available across large remote landscapes. In this study, we evaluated the potential of stereo imagery from commercially available Very High Resolution (VHR) satellites as an alternative for estimating canopy height variables in Australian tropical savannas, using a semi-global dense matching (SGM) image-based technique. We assessed and compared the completeness and vertical accuracy of extracted canopy height models (CHMs) from GeoEye 1 and WorldView 1 VHR satellite stereo pairs and summarised the factors influencing image matching effectiveness and quality.

Our results showed that stereo dense matching using the SGM technique severely underestimates tree presence and canopy height. The highest tree detection rates were achieved by using the near-infrared (NIR) band of GE1 (8{\textendash}9\%). WV1-GE1 cross-satellite (mixed) models did not improve the quality of extracted canopy heights. We consider these poor detection rates and height retrievals to result from: i) the clumping crown structure of the dominant Eucalyptus spp.; ii) their vertically oriented leaves (affecting the bidirectional reflectance distribution function); iii) image band radiometry and iv) wind induced crown movement affecting stereo-pair point matching. Our detailed analyses suggest that current commercially available VHR satellite data (0.5 m resolution) are not well suited to estimating canopy height variables, and therefore above ground biomass (AGB), in Eucalyptus dominated north Australian tropical savanna woodlands.

}, keywords = {canopy height, Satellite, Savanna, Stereo}, doi = {10.1016/j.jag.2018.10.021}, url = {https://www.sciencedirect.com/science/article/pii/S0303243418308390}, author = {Grigorijs Goldbergs and Stefan Maier and Shaun R Levick and Andrew C. Edwards} } @article {bnh-4424, title = {Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas}, journal = {Remote Sensing}, volume = {10}, year = {2018}, month = {01/2018}, abstract = {
The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds{\textemdash}obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera{\textemdash}can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70\%) for dominant and co-dominant trees, but yielded low detection rates (\<35\%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.
}, keywords = {biomass, canopy height, low-cost UAS, segmentation, single tree detection, structure from motion}, doi = {10.3390/rs10020161 }, url = {http://www.mdpi.com/2072-4292/10/2/161/htm}, author = {Grigorijs Goldbergs and Stefan Maier and Shaun R Levick and Andrew C. Edwards} } @article {bnh-4335, title = {Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR}, journal = {Remote Sensing of Environment}, volume = {205}, year = {2018}, month = {02/2018}, pages = {141-150}, chapter = {141}, abstract = {

Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R2\ =\ 0.754, RMSE\ =\ 90\ kg), and that 63\% of individual trees crowns (ITC) could be accurately delineated with a\ canopy maximaapproach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4\ ha plots: R2\ =\ 0.86, RMSE\ =\ 3.4\ Mg\ ha-\ 1; and 1\ ha plots: R2\ =\ 0.83, RMSE\ =\ 4.0\ Mg\ ha-\ 1). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.

}, doi = {10.1016/j.rse.2017.11.010}, url = {https://www.sciencedirect.com/science/article/pii/S0034425717305357}, author = {Grigorijs Goldbergs and Shaun R Levick and Michael Lawes and Andrew C. Edwards} }