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Sushinsky J.R.,University of Queensland | Rhodes J.R.,University of Queensland | Possingham H.P.,University of Queensland | Gill T.K.,Joint Remote Sensing Research Program | And 2 more authors.
Global Change Biology | Year: 2013

Urbanization causes severe environmental degradation and continues to increase in scale and intensity around the world, but little is known about how we should design cities to minimize their ecological impact. With a sprawling style of urban development, low intensity impact is spread across a wide area, and with a compact form of development intense impact is concentrated over a small area; it remains unclear which of these development styles has a lower overall ecological impact. Here, we compare the consequences of compact and sprawling urban growth patterns on bird distributions across the city of Brisbane, Australia. We predicted the impact on bird populations of adding 84 642 houses to the city in either a compact or sprawling design using statistical models of bird distributions. We show that urban growth of any type reduces bird distributions overall, but compact development substantially slows these reductions at the city scale. Urban-sensitive species particularly benefited from compact development at the city scale because large green spaces were left intact, whereas the distributions of nonnative species expanded as a result of sprawling development. As well as minimizing ecological disruption, compact urban development maintains human access to public green spaces. However, backyards are smaller, which impacts opportunities for people to experience nature close to home. Our results suggest that cities built to minimize per capita ecological impact are characterized by high residential density, with large interstitial green spaces and small backyards, and that there are important trade-offs between maintaining city-wide species diversity and people's access to biodiversity in their own backyard.© 2012 Blackwell Publishing Ltd. Source


Johansen K.,Joint Remote Sensing Research Program | Johansen K.,University of Queensland | Johansen K.,Queensland Government | Phinn S.,Joint Remote Sensing Research Program | And 3 more authors.
Remote Sensing of Environment | Year: 2010

Riparian zones in Australia are exposed to increasing pressures because of disturbance from agricultural and urban expansion, weed invasion, and overgrazing. Accurate and cost-effective mapping of riparian environments is important for assessing riparian zone functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to compare the accuracy and costs of mapping riparian zone attributes from image data acquired by three different sensor types, i.e. Light Detection and Ranging (LiDAR) (0.5-2.4. m pixels), and multi-spectral QuickBird (2.4. m pixels) and SPOT-5 (10. m pixels). These attributes included streambed width, riparian zone width, plant projective cover, longitudinal continuity, vegetation overhang, and bank stability. The riparian zone attributes were mapped for a study area along Mimosa Creek in the Fitzroy Catchment, Central Queensland, Australia. Object-based image and regression analyses were used for mapping the riparian zone attributes. The validation of the LiDAR, QuickBird, and SPOT-5 derived maps of streambed width (R=0.99, 0.71, and 0.44 respectively) and riparian zone width (R=0.91, 0.87, and 0.74 respectively) against field derived measurements produced the highest accuracies for the LiDAR data and the lowest using the SPOT-5 image data. Cross-validation estimates of misclassification produced a root mean square error of 1.06, 1.35 and 1.51 from an ordinal scale from 0 to 4 of the bank stability score for the LiDAR, QuickBird and SPOT-5 image data, respectively. The validation and empirical modelling showed high correlations for all datasets for mapping plant projective cover (R>0.93). The SPOT-5 image data were unsuitable for assessment of riparian zone attributes at the spatial scale of Mimosa Creek and associated riparian zones. Cost estimates of image and field data acquisition and processing of the LiDAR, QuickBird, and SPOT-5 image data showed that discrete return LiDAR can be used for costs lower than those for QuickBird image data over large spatial extents (e.g. 26,000. km of streams). With the higher level of vegetation structural and landform information, mapping accuracies, geometric precision, and lower overall costs at large spatial extents, LiDAR data are a feasible means for assessment of riparian zone attributes. © 2010 Elsevier Inc. Source


Arroyo L.A.,Joint Remote Sensing Research Program | Arroyo L.A.,University of Queensland | Johansen K.,Joint Remote Sensing Research Program | Johansen K.,University of Queensland | And 4 more authors.
Forest Ecology and Management | Year: 2010

Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets. © 2009 Elsevier B.V. Source


Arroyo L.A.,Joint Remote Sensing Research Program | Arroyo L.A.,University of Queensland | Arroyo L.A.,Research Center del Fuego | Johansen K.,Joint Remote Sensing Research Program | And 3 more authors.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives | Year: 2010

Although geographic object based image analysis (GEOBIA) has been successfully applied to derive local maps (1-10s km2) from very high spatial resolution (VHR) image data (pixels > 1.0 x 1.0 m), its potential for automatically mapping large areas remains unknown. The aim of this study was to create and apply a GEOBIA method to automatically map land cover classes in subsets with different environmental and land cover characteristics from VHR image data. Airborne Vexcel Ultracam-D image data with four multi-spectral bands and 0.25 m pixels were captured for the study area, located 50 km from Melbourne, Victoria, Australia. Five subsets showing different environments and characteristics were selected for the study. Four of them were used to create a GEOBIA classification method for mapping land cover types. A step-wise approach was adopted, where individual steps of segmentation and classification were used to establish a contextual knowledge base. Thus, context features became useful for classifying land cover types. The following land cover types were mapped from the five subsets: woody vegetation, non-woody vegetation; water bodies, bare ground, urban features and agricultural areas. The overall accuracy of the four land cover maps used to develop the GEOBIA classification scheme was 77.5%. The classification accuracy was calculated using 100 validation sites per land cover class, visually identified from the Ultracam-D data. Finally, the effectiveness of replicating the GEOBIA classification scheme was tested against the independent fifth subset. This classification produced very similar results, with an overall accuracy of 74.8%, which indicates that the developed GEOBIA classification scheme may be automatically applied to other independent areas, and potentially for larger spatial extent mapping. Source


Johansen K.,Joint Remote Sensing Research Program | Johansen K.,University of Queensland | Johansen K.,Queensland Government | Arroyo L.A.,Joint Remote Sensing Research Program | And 7 more authors.
Ecological Indicators | Year: 2010

Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R2 = 0.99 for streambed width, R2 = 0.82 for riparian zone width, R2 = 0.89 for PPC, R2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia. © 2010 Elsevier Ltd. Source

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