The shadows cast by 180 individual Eucalyptus trees, of varying canopy condition, on undulating land in south-eastern Australia were used to infer their heights from 50-cm spatial resolution, multispectral aerial imagery (blue = 0.4-0.5 μm; green = 0.5-0.6 μm; red = 0.6-0.7 μm; near infrared = 0.7-1 μm). A geometrical shadow model was developed incorporating the local slope and aspect of the ground from a digital elevation model at each tree location. A method of deriving 'local tree time' to infer the solar elevation angle, in situations where the image acquisition time is not available, was also developed. Based on a measurement of the shadow length from the geometric centre of the tree crowns, and ignoring the role of the crown periphery in distorting the shadow shape, the tree heights were estimated with a root mean square error of ±5.6m (±27%) with some overestimated by as much as 50%. A geometric correction for shadow distortion assuming spherical crown geometry provided an improved estimate with a root mean square error of ±4.8m (±23%).

High spatial resolution images (~10 cm) are routinely available from airborne platforms. Few studies have examined the applicability of using such data to characterize land cover in "farmscapes" comprising open pasture and remnant vegetation communities of varying density. Very high spatial resolution remotely sensed imagery has been used to classify land cover classes on a ~5000 ha extensive grazing farm in Australia. This "farmscape" consisted of open pasture fields, scattered trees, and remnant vegetation (woodlands). The relative performances of object-based and pixel-based approaches to classification were tested for accuracy and applicability. Maximum likelihood classification (MLC) was used for pixel-based classification while the k-nearest neighbor (k-NN) technique was used for object-based classification. A range of image sampling scales was tested for image segmentation. At an optimal sampling scale, the pixel-based classification resulted in an overall accuracy of 77 percent, while the object-based classification achieved an overall accuracy of 86 percent. While both the object- and pixel-based classification techniques yielded higher quantitative accuracies, a "more realistic" land cover classification, with few errors due to intermixing of similar classes, was achieved using the object-based method.

Owing to its relevance to remotely-sensed imagery of landscapes, this paper investigates the ability to infer diameter at breast height (DBH) for five species of Australian native 'Eucalyptus' from measurements of tree height and crown projection area. In this study regression models were developed for both single trees and clusters from 2 to 27 stems (maximum density 536 stems per ha) of 'Eucalyptus bridgesiana', 'Eucalyptus caliginosa', 'Eucalyptus blakelyi', 'Eucalyptus viminalis', and 'Eucalyptus melliodora'. Crown projection area and tree height were strongly correlated for single trees, and the log-transformed crown projection area explained the most variance in DBH (R² = 0.68, mean prediction error ±16 cm). Including tree height as a descriptor did not significantly alter the model performance and is a viable alternative to using crown projection area. The total crown projection area of tree clusters explained only 34% of the variance in the total (sum of) the DBH within the clusters. However average crown projection area per stem of entire tree clusters explained 67% of the variance in the average (per stem) DBH of the constituent trees with a mean prediction error ±8 cm. Both the single tree and tree cluster models were statistically similar and a combined model to predict average stem DBH yielded R² = 0.71 with a mean prediction error (average DBH per stem) of ±13 cm within the range of 0.28-0.84 m. A single model to infer DBH for both single trees and clusters comprising up to 27 stems offers a pathway for using remote sensing to infer DBH provided a means of determining the number of stems within cluster boundaries is included.

High resolution remote sensing is a valuable tool for quantifying the distribution and density of trees with applications ranging from forest inventory, mapping urban parklands to understanding impacts on soil nutrient and carbon dynamics in farming land. The present study aims to compare the accuracy of different remote sensing techniques for delineating the tree cover in 50 cm resolution WorldView2 imagery of farmland. An image of farmland comprising pastures, remnant vegetation and woodland was initially classified into six classes, namely tree cover, bare soil, rock outcrop, natural pasture, degraded pasture and water body using different techniques. Pixel based classification based on all four available wavebands, were tested and an overall classification accuracy of 96.8% and 72.9 % were achieved for supervised and unsupervised techniques. Object based segmentation and subsequent classification yielded an improved overall classification accuracy of 98.3%. Addition of a fifth NDVI layer to the available wavebands did improve the accuracy but not significantly (98.1%, approx 1.3%). In addition to the improvements in overall classification accuracy, a visual inspections of results from the different methods indicated the object based method to yield a more 'realistic' result, avoiding the 'salt and pepper' effects apparent in the pixel-based methods. Overall, object based classification hence is considered more suitable for tree cover extraction from high resolution images.

Studies estimating canopy volume are mostly based on laborious and time-consuming field measurements; hence, there is a need for easier and convenient means of estimation. Accordingly, this study investigated the use of remotely sensed data (WorldView-2 and LiDAR) for estimating tree height, canopy height and crown diameter, which were then used to infer the canopy volume of remnant eucalypt trees at the Newholme/Kirby 'SMART' farm in north-east New South Wales. A regression model was developed with field measurements, which was then applied to remote-sensing-based measurements. LiDAR estimates of tree dimensions were generally lower than the field measurements (e.g., 6.5% for tree height) although some of the parameters (such as tree height) may also be overestimated by the clinometer/rangefinder protocols used. The WorldView-2 results showed both crown projected area and crown diameter to be strongly correlated to canopy volume, and that crown diameter yielded better results (Root Mean Square Error RMSE 31%) than crown projected area (RMSE 42%). Although the better performance of LiDAR in the vertical dimension cannot be dismissed, as suggested by results obtained from this study and also similar studies conducted with LiDAR data for tree parameter measurements, the high price and complexity associated with the acquisition and processing of LiDAR datasets mean that the technology is beyond the reach of many applications. Therefore, given the need for easier and convenient means of tree parameters estimation, this study filled a gap and successfully used 2D multispectralWorldView-2 data for 3D canopy volume estimation with satisfactory results compared to LiDAR-based estimation. The result obtained from this study highlights the usefulness of high resolution data for canopy volume estimations at different locations as a possible alternative to existing methods.

Rapid decline and death of rural Eucalypts trees of all ages and species have been reported in the farmscapes of regional Australia due to various environmental and farming management related factors. The identification of existing farm tree species is important for long term management strategies to provide ecosystem stability in the region. This study explored the feasibility of structural attributes of LiDAR and spectral and spatial characteristics of high resolution remote sensing data to identify and map Eucalyptus tree species. An object based image segmentation and rule-based classification algorithm were developed to delineate tree boundaries and species classification. The integration of two datasets improved the classification accuracy (65%) against their separate classification (52% and 41%, respectively). The identification of tree species will help in getting first-hand information on existing farm trees, which may be used in assessing tree condition in time series related to management practices and complex dieback problem.