Jonathan P. Dash

Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773–0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1–21 pls/m2) or the height threshold (0–2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches.

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

Phenotyping Whole Forests Will Help to Track Genetic Performance

Phenotyping is the accurate and precise physical description of organisms. Accurate and quantitative phenotyping underpins the delivery of benefits from genetic improvement programs in agriculture. In forest trees, phenotyping at an equivalent precision has been impossible because trees and forests are large, long-lived, and highly variable. These facts have restricted the delivery of genetic gains in forestry compared to other agricultural sectors. We describe a landscape-scale phenotyping platform that integrates remote sensing, spatial information systems, and genomics to facilitate the delivery of greater gains enabling forestry to catch up with other sectors. Combining remote sensing at a range of spatial and temporal scales with genomics will ultimately impact on tree breeding globally.

Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities

Abstract Fine-scale characterisation of forest stands using very high-density aerial lidar data holds considerable potential for improving the accuracy of area-based forest inventories. To realise these gains, new methods of characterising dense aerial point clouds are required. This research presents one potential approach using voxel-based metrics often associated with the analysis of terrestrial lidar data. This was accomplished by comparing predictions of forest inventory attributes made using voxel-based metrics, more standard lidar metrics and a combination of both classes of metrics. A high-density lidar dataset was acquired using a helicopter-mounted RIEGL VUX-1UAV laser scanner to produce point clouds with a minimum density of 280 pulses m−2. Data were obtained from 73 plots presenting a wide range of stand conditions located within two adjacent plantations of Pinus radiata D.Don in south-eastern New South Wales. Random forests regression models were developed to predict top height, basal area, stand density and total stem volume. To assess the interaction between metric type and pulse density, the point clouds were thinned to 18 pulse densities ranging from 1 to 280 pulses m−2 before fitting models using the metrics generated from data at each target density. Data thinning had little effect on the predictive accuracy of models for any of the four forest attributes predicted from either voxel-based, standard lidar metrics or their combination. Averaged across all pulse densities, models created for top height, basal area, stand density and total stem volume from standard lidar metrics had R2 of 0.72, 0.44, 0.34 and 0.53 with normalised RMSE (RMSE expressed as a percentage of the mean for each dimension) of 6,6, 25.2, 60.1 and 25.5% respectively. Use of voxel-based metrics resulted in substantial gains in model precision for all dimensions, apart from top height, with R2 increasing by 0.04, 0.23, 0.24, and 0.22 and nRMSE averaging 6.1, 19.6, 48.6, and 18.7%, respectively, for top height, basal area, stand density, and total stem volume. The precision of models that used both types of lidar metrics was very similar to the precision of models that used only voxel-based metrics. These results demonstrate the considerable potential of voxel-based metrics for improving the accuracy of forest measurement. The gains from voxelised-metrics were not dependent on very high pulse densities and could be achieved at densities typical of conventional lidar surveys undertaken using fixed-wing aircraft.