Tony Gill

How should we grow cities to minimize their biodiversity impacts

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 peoples access to biodiversity in their own backyard.

Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia

Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM) sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS) is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR) product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1) assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC) estimates using ground-measured values; and (2) comparing the LITS-generated normalized difference vegetation index (NDVI) and MODIS NDVI (MOD13Q1) time series. The predicted image-derived FPC products (value ranges from 0 to 100%) had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used) difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf) forest environment and also demonstrated that the approach can be used to form a time series of Landsat TM images to study vegetation phenology over a number of years.

An operational scheme for deriving standardised surface reflectance from landsat TM/ETM+ and SPOT HRG imagery for eastern Australia

Operational monitoring of vegetation and land surface change over large areas can make good use of satellite sensors that measure radiance reflected from the Earth’s surface. Monitoring programs use multiple images for complete spatial coverage over time. Accurate retrievals of vegetation cover and vegetation change estimates can be hampered by variation, in both space and time, in the measured radiance, caused by atmospheric conditions, topography, sensor location, and sun elevation. In order to obtain estimates of cover that are comparable between images, and to retrieve accurate estimates of change, these sources of variation must be removed. In this paper we present a preprocessing scheme for minimising atmospheric, topographic and bi-directional reflectance effects on Landsat-5 TM, Landsat-7 ETM+ and SPOT-5 HRG imagery. The approach involves atmospheric correction to compute surface-leaving radiance, and bi-directional reflectance modelling to remove the effects of topography and angular variation in reflectance. The bi-directional reflectance model has been parameterised for eastern Australia, but the general approach is more widely applicable. The result is surface reflectance standardised to a fixed viewing and illumination geometry. The method can be applied to the entire record for these instruments, without intervention, which is of increasing importance with the increased availability of long term image archives. Validation shows that the corrections improve the estimation of reflectance at any given angular configuration, thus allowing the removal from the reflectance signal of much variation due to factors independent of the land surface. The method has been used to process over 45,000 Landsat-5 TM and Landsat-7 ETM+ scenes and 2,500 SPOT-5 scenes, over eastern Australia, and is now in use in operational monitoring programs.

Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12 × 10     km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product ( r 2 = 0.92 ) from the Queensland Department of Environment and Resource Management.

Estimating tree-cover change in Australia: challenges of using the MODIS vegetation index product

Time series of the vegetation index product MOD13Q1 from the Moderate Resolution Imagery Spectroradiometer (MODIS) were assessed for estimating tree foliage projective cover (FPC) and cover change from 2000 to 2006. The MOD13Q1 product consists of the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). There were four challenges in using the MOD13Q1 product to derive tree FPC: assessing the impact of the sensors varying view geometry on the vegetation index values; decoupling tree and grass cover contributions to the vegetation index signal; devising a method to relate the temporally composited vegetation index pixels to Lidar estimates of tree FPC for calibration; and estimating the accuracy of the FPC and FPC change measurements using independently derived Lidar, Landsat and MODIS cover estimates. The results show that, for complex canopies, the varying view geometry influenced the vegetation indices. The EVI was more sensitive to the view angle than the NDVI, indicating that it is sensitive to vegetation structure. An existing time series method successfully extracted the evergreen vegetation index signal while simultaneously minimizing the impact of varying view geometry. The vegetation indices were better suited to monitoring tree cover change than deriving accurate single‐date estimates of cover at regional to continental scales. The EVI was more suited to monitoring change in high‐biomass regions (cover >50%) where the NDVI begins to saturate.

Limitations of the dense dark vegetation method for aerosol retrieval under Australian conditions

The use of dense dark vegetation (DDV) for atmospheric aerosol correction of Landsat imagery is investigated for Australian conditions. Aerosol optical depth (AOD) measurements from sun photometers are used as a reference data set and compared against estimates of AOD derived from Landsat imagery using the DDV method. The DDV method makes assumptions that the vegetation is sufficiently dark and the ratio between bottom-of-atmosphere reflectances at different wavelengths is constant. These assumptions were tested using Landsat-5 Thematic Mapper (TM) imagery corrected with AOD measured by field-based sun photometers on the AErosol RObotic NETwork (AERONET) network. The assumptions were found to be correct only for one of the three locations studied. In other locations, the spatial and temporal variability of the vegetation and its relative brightness makes the method unsuitable.

Improvements to ASTER-Derived Fractional Estimates of Bare Ground in a Savanna Rangeland

Accurate estimates of ground cover and its inverse, bare ground (BG), derived from satellite imagery are required for monitoring rangeland-health indicators over large areas. This paper shows how accurate estimates of BG were obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image data over a savanna rangeland in north-east Australia. The normalized difference vegetation index and the lignin and cellulose absorption index were used to extract spectral-reflectance signatures of three scene endmembers from the image data: BG, nonphotosynthetic vegetation, and photo- synthetic vegetation. The endmember signatures were used with the Monte Carlo spectral mixture analysis (MCSMA) algorithm to derive image estimates of BG that were compared with field measurements. The results showed that the accuracies of the BG estimates were improved, compared to those obtained in a previous ASTER study that used only two endmembers in the unmixing procedure (root mean square error (RMSE) was improved from >0.1 to ~0.05). The results are an improvement on previous work that used Landsat and IKONOS satellite- multispectral imagery, compared favorably with estimates derived from airborne hyperspectral imagery, and can be used with existing rangeland monitoring methods. We conclude that the end- member extraction method is simple and widely applicable and can be used with MCSMA to obtain accurate estimates of BG from ASTER imagery. However, the use of this approach for estimating BG from satellite imagery depends on the future development of satellite-hyperspectral or ASTER-like sensors.

Estimates of bare ground and vegetation cover from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave-infrared reflectance imagery

The high level of success of estimating photosynthetic vegetation from multispectral satellite sensors at regional scales has not been repeated for non-photosynthetic vegetation and bare ground. Therefore regional scale estimates of total vegetation from multispectral sensors are largely underestimated with implications for a wide range of agricultural and environmental applications. Recent research using simulated data showed that the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) had the potential to provide reliable estimates of bare ground and total vegetation. This study built on that research and found that estimates of bare ground retrieved from ASTER short-wave infrared imagery using linear spectral unmixing correlated well with field measurements (RMSE < 0.1, r 2 > 0.7). Image endmember libraries required for spectral unmixing were extracted from the image data using a combination of field knowledge and the lignin and cellulose absorption index. The most reliable results were found by applying a sum-constraint to the unmixing models and tying the signatures at wavebands that corresponded to cellulose or clay-hydroxyl absorption features. The results of this research show that ASTER can improve the estimates of total vegetation extracted from satellite imagery for environmental studies at regional scales.

Assessing viewing and illumination geometry effects on the MODIS vegetation index MOD13Q1 time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Australia

Time series analysis of satellite data can be used to monitor temporal dynamics of forested environments, thus providing spatial data for a range of forest science, monitoring and management issues. The moderate resolution imaging spectroradiometer (MODIS) vegetation index (MOD13Q1) product has potential for monitoring forest dynamics and disturbances. However, the suitability of the product to accurately measure temporal changes due to phenology and disturbances is questionable as the effects of variable solar and viewing geometry have not been removed from these data. This study aimed to examine the impact that viewing and illumination geometry differences had on MOD13Q1 vegetation index values, and their subsequent ability to map changes arising from phenology and disturbances in a number of forest communities in Queensland, Australia. MOD13Q1 normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were compared to normalized NDVI and EVI (NDVInormalized and EVInormalized), which were derived from the reflectance modelled from a bidirectional reflectance distribution function (BRDF)/albedo parameters product (MCD43A1) using fixed viewing and illumination geometry. Time series plots of the vegetation index values from a number of pixels representing different forest types and known disturbances showed that the NDVInormalized time series was more effective at capturing the changes in vegetation than the NDVI. MOD13Q1 NDVI showed higher seasonal amplitude, but was less accurate at capturing phenology and disturbances compared to the NDVInormalized. The EVI was less affected by variable viewing and illumination geometry in terms of amplitude, but was affected in terms of time shift in periodicities providing erroneous information on phenology. More studies in different environments with appropriate vegetation phenology reference data will be needed to confirm these observations.

X3D programmable shaders

Programmable shading in real time has been an ultimate goal of graphical applications since the introduction of RenderMan™. The research described in this paper focuses on an extension to X3D to allow for real time programmable shading. We describe a new Programmable Shaders component for X3D in detail as well as a set of tools that can be used for authoring.This paper reflects the work undertaken by the Web3D Consortiums X3D Programmable Shades Working Group and incorporates the most recent results of the groups discussion. The design has been implemented and demonstrated in prototype form in two X3D browsers.