Urban built-up area information is required in various applications of land use planning and management. However, urban built-up area extraction from moderate spatial resolution Landsat time-series data is challenging because of significant intra-urban heterogeneity and spectral confusion between other landcover types. This paper proposes a technique to extract urban built-up area from time-series Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imageries and determines urban area changes between 1984 to 2015 of Adama Municipal Area of Ethiopia. The study selected three indices, the Enhanced Built-Up and Bareness Index (EBBI), Soil Adjusted Vegetation Index (SAVI) and Modified Normalized Difference Water Index (MNDWI), to represent three major urban land-use classes: built-up and barren/bare land, open waterbody, and vegetation, respectively. The built-up area was extracted by taking the difference between EBBI, SAVI and MNDWI to remove the vegetation and water noises, and the resulted index image was spectrally segmented to separate built-up area from the non-urban built-up lands. The derived index was used to map built-up area for 1984, 1995, 2005 and 2015 periods. The expansion of the built-up area has been revealed as a major change in the area when city area expanded substantially by 293% between 1984 to 2015 periods. The advantage of the method was to use almost the entire spectral range of Landsat imageries which cause less spectral confusion between land cover classes and hence resulted in higher accuracies compared to other indices. The method was effective and simple to implement, and can be used for built-up extraction in other areas.

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.

AN ANALYSIS OF time series Landsat imagery acquired over the Bundaberg region between 2010 and 2015 identified variations in annual crop vigour trends, as determined by greenness normalised difference vegetation index (GNDVI). On average, early to mid-April was identified as the crucial period where crops achieved their maximum vigour and as such indicated when single image captures should be acquired for future regional yield forecasting. Additionally, the regional crop GNDVI averaged from Landsat images between February to April, produced a higher coefficient of determination to final yield (R2 = 0.91) than the average crop GNDVI value from a single mid-season SPOT5 image capture (R2 = 0.52). This result indicates that the time series method may be more appropriate for future regional yield forecasting. For improved prediction accuracies at the individual crop level, a univariate model using only crop GNDVI values (SPOT5) and corresponding yield (t/ha) produced a higher prediction accuracy for the 2014 Bundaberg harvest than a multivariate model that included additional historic spectral and crop attribute data. For Condong, a multivariate model improved the prediction accuracy of individual crops harvested in 2014 by 41.8% for one-year-old cane (Y1), and 46.2% for two-year-old cane (Y2). For the non-invasive measure of foliar nitrogen (N%), the specific wavelengths 615 nm, 737 nm and 933 nm (Airborne hyperspectral), and 634 nm, 750 nm and 880 nm (ground based field spectroscopy) were found to be the most significant. These results were supported by satellite imagery (Worldview-2 and Worldview-3) acquired over three replicated field trials in Mackay (2014 and 2015) and Tully (2015), where the vegetation index (VI) REN2NDVIWV, a ratio of the rededge band (705-745 nm) and the Near-IR2 band (860-1040 nm), produced a higher correlation to nitrogen concentration (%) than NDVI.