Neil Flood

AN EVALUATION OF THE TIME DEPENDENCE OF THE ANISOTROPY OF THE WATER DIFFUSION TENSOR IN ACUTE HUMAN ISCHEMIA

We have performed MRI examinations to determine the water diffusion tensor in the brain of six patients who were admitted to the hospital within 12 h after the onset of cerebral ischemic symptoms. The examinations have been carried out immediately after admission, and thereafter at varying intervals up to 90 days post admission. Maps of the trace of the diffusion tensor, the fractional anisotropy and the lattice index, as well as maps of cerebral blood perfusion parameters, were generated to quantitatively assess the character of the water diffusion tensor in the infarcted area. In patients with significant perfusion deficits and substantial lesion volume changes, four of six cases, our measurements show a monotonic and significant decrease in the diffusion anisotropy within the ischemic lesion as a function of time. We propose that retrospective analysis of this quantity, in combination with brain tissue segmentation and cerebral perfusion maps, may be used in future studies to assess the severity of the ischemic event.

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.

Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for both top-of-atmosphere and surface reflectance: a study in the Australian landscape

The new Landsat-8 Operational Land Imager (OLI) is intended to be broadly compatible with the previous Landsat-7 Enhanced Thematic Mapper Plus (ETM+). The spectral response of the OLI is slightly different to the ETM+, and so there may be slight differences in the reflectance measurements. Since the differences are a function not just of spectral responses, but also of the target pixels, there is a need to assess these differences in practice, using imagery from the area of interest. This paper presents a large scale study of the differences between ETM+ and OLI in the Australian landscape. The analysis is carried out in terms of both top-of-atmosphere and surface reflectance, and also in terms of biophysical parameters modelled from those respective reflectance spectra. The results show small differences between the sensors, which can be magnified by modelling to a biophysical parameter. It is also shown that a part of this difference appears to be systematic, and can be reliably removed by regression equations to predict ETM+ reflectance from OLI reflectance, before applying biophysical models. This is important when models have been fitted to historical field data coincident with ETM+ imagery. However, there will remain a small per-pixel difference which could be an unwanted source of variability.

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables

A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.

Seasonal Composite Landsat TM/ETM+ Images Using the Medoid (a Multi-Dimensional Median)

Multi-temporal satellite imagery can be composited over a season (or other time period) to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. For the purposes of vegetation monitoring, a commonly used technique is the Maximum NDVI Composite, used in conjunction with variety of other constraints. The current paper proposes an alternative based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values, and appears to be better at producing imagery which is representative of the time period. For each pixel, the medoid is always selected from the available dates, so the result is always a single observation for that pixel, thus preserving relationships between bands. The method is applied to Landsat TM/ETM+ imagery to create seasonal reflectance images (four per year), with the aim being a regular time series of reflectance values which captures the variability at seasonal time scales. Analysis of the seasonal reflectance values suggests that resulting temporal image composites are more representative of the time series than the maximum NDVI seasonal composite.

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.

Testing the local applicability of MODIS BRDF parameters for correcting Landsat TM imagery

Landsat Thematic Mapper (TM) imagery is widely used for large scale multi-temporal monitoring of the land surface. One source of variability in this imagery, which is not related to variation in the land surface, is the result of the bi-directional reflectance distribution function (BRDF). This causes variation in the measured reflectance as a function of the angular configuration of the sun and sensor. To remove this variation from the imagery, a correction can be applied for BRDF. Some authors have proposed the use of the BRDF parameterization derived for the Moderate Resolution Imaging Spectroradiometer (MODIS; NASA, Washington, DC, USA) instrument as a means of correction for BRDF in Landsat TM imagery. However, since the spatial scale of the two instruments is substantially different, it is not clear up to what extent the MODIS BRDF parameters are applicable to Landsat TM. This study provides a direct empirical test of this, over the eastern Australian landscape, and tests whether the MODIS BRDF parameters are applicable to characterize BRDF at a local scale of a Landsat TM pixel, or simply at a global scale. The results suggest that the MODIS BRDF parameters effectively characterize the BRDF of the Landsat imagery at a global average scale, but, at least in the Australian context, do not capture any further information about the BRDF of more local regions, or individual Landsat pixels.

Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007

A range of programs exist globally that use satellite imagery to derive estimates of vegetation-cover for developing vegetation-management policy, monitoring policy compliance and making natural-resource assessments. Consequently, the satellite imagery must have a high degree of geometric accuracy. It is common for the accuracy assessment to be performed using the root mean square error (RMSE) only. However the RMSE is a non-spatial measure and more rigorous accuracy assessment methods are required. Currently there is a lack of spatially explicit accuracy assessment methods reported in the literature that have been demonstrated to work within operational monitoring programs. This paper reports on the method used by the Statewide Landcover and Trees Study (SLATS) to georegister and assess the registration accuracy of Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) imagery in Queensland, Australia. A geometric baseline with high accuracy (a statewide mean RMSE of 4.53 m) was derived by registering Landsat-7 ETM+ panchromatic imagery acquired in 2002 to a database of over 1600 control points, collected on the ground using a differential global positioning system. Landsat-5 TM and Landsat-7 ETM+ imagery for 12 selected years from 1988 to 2007 was registered to the baseline in an automated procedure that used linear geometric correction models. The reliability of the geometric correction for each image was determined using the RMSE, calculated using independent check points, as an indicator of model fit; by analysing the spatial trends in the model residuals; and through visual assessment of the corrected imagery. The mean RMSE of the statewide coverage of images for all years was less than 12.5 m (0.5 pixels). Less than 1 percent of images had non-linear spatial trends in the model residuals and some image misregistration after applying a linear correction-model; in those cases a quadratic model was deemed necessary for correction. Further research in the development of automated spatially explicit accuracy assessment methods is required.

Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia

The new Sentinel-2 Multi Spectral Imager instrument has a set of bands with very similar spectral windows to the main bands of the Landsat Thematic Mapper family of instruments. While these should, in principle, give broadly comparable measurements, any differences are a function not only of the differences in the sensor responses, but also of the spectral characteristics of the target pixels. In order to test for and quantify differences between these sensors, a large set of coincident imagery was assembled for the Australian landscape. Comparisons were carried out in terms of surface reflectance, and also in terms of biophysical quantities estimated from the reflectances. Small but consistent differences were found, and suitable adjustment equations fitted to enable transformation of Sentinel-2A reflectance values to more closely match Landsat-7 or Landsat-8 values. This is useful if trying to take models and thresholds fitted from Landsat and use them with Sentinel-2. The fitted adjustment equations were also compared against those fitted globally for NASA’s Harmonized Landsat-8 Sentinel-2 product, and found to be substantially different, raising the possibility that such adjustments need to be fitted on a regional basis.

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l’Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008–2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.