Sam Gillingham

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.

The Remote Sensing and GIS Software Library (RSGISLib)

Key to the successful application of remotely sensed data to real world problems is software that is capable of performing commonly used functions efficiently over large datasets, whilst being adaptable to new techniques. This paper presents an open source software library that was developed through research undertaken at Aberystwyth University for environmental remote sensing, particularly in relation to vegetation science. The software was designed to fill the gaps within existing software packages and to provide a platform to ease the implementation of new and innovative algorithms and data processing techniques. Users interact with the software through an XML script, where XML tags and attributes are used to parameterise the available commands, which have now grown to more than 300. A key feature of the XML interface is that command options are easily recognisable to the user because of their logical and descriptive names. Through the XML interface, processing chains and batch processing are supported. More recently a Python binding has been added to RSGISLib allowing individual XML commands to be called as Python functions. To date the Python binding has over 100 available functions, mainly concentrating on image utilities, segmentation, calibration and raster GIS. The software has been released under a GPL3 license and makes use of a number of other open source software libraries (e.g., GDAL/OGR), a user guide and the source code are available at http://www.rsgislib.org. HighlightsAn open source software platform for the processing of remotely sensed and GIS datasets.Support for large scale processing on HPC systems.Scalable segmentation and image-to-image registration algorithms.Close links with other software to be used in combination rather than replacement.A platform for research to be made available to the community into the future.

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.

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.

The KEA image file format

There are a large number of image formats already in use within the remote sensing community but currently there is no format that provides the features of: compression, support for large file sizes, ground control points, raster attribute tables and inbuilt image pyramids. Therefore, a new image format, named KEA, after the New Zealand bird, has been proposed. The KEA format provides a full implementation of the GDAL data model and is implemented within a HDF5 file. A software library with a GDAL driver have been freely provided to the community allowing use through any GDAL based software. The new format has comparable performance with existing formats while producing smaller file sizes and is already within active use for a number of projects within Landcare Research, New Zealand, and the wider community.

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.

Accurate registration of optical satellite imagery with elevation models for topographic correction

It is necessary to remove the effects of topography from optical satellite imagery if automated techniques are to be used to infer surface properties. This is especially the case in mountainous terrain where variable slope normals cause variation in both illumination and reflectance of light. Digital elevation models (DEMs) are required to model slope normals and make topographic corrections. However, it is difficult to achieve accurate registration between ortho-rectified satellite images and DEMs. We show how this mis-registration, which can be spatially variable, may be accounted for with the use of a local correlation filter. The filter determines the offset between a DEM shade map and ortho-rectified satellite image for every pixel. Association of satellite image pixels with the ‘correct’ slope normal in topographic correction removes the majority of ghosting and high-frequency artefacts.

Comparing bright-target surface spectral-reflectance estimates obtained from IRS P6 LISS III to those obtained from Landsat 5 TM and Landsat 7 ETM+

Since 2008 there have been a limited number of Landsat 5 thematic mapper (TM) images acquired between April and October in Australia. Consequently, TM imagery may not be available at the desired time of year for some monitoring applications. IRS (Indian Remote Sensing) P6 LISS (Linear Imaging and Self Scanner) III imagery has been acquired over Australia since 2008 and represents an alternative, Landsat-like, data source to fill the Landsat 5 TM temporal gap. To be useful for the continuation of long-term monitoring, the LISS III imagery needs to provide similar surface-reflectance estimates to Landsat 5 TM. A time series of spatially averaged sensor-radiance estimates for 2008 was derived from Landsat 5 TM, Landsat 7 enhanced thematic mapper plus (ETM+) and IRS P6 LISS III imagery for two highly reflective, spectrally invariant, claypans in Queensland, Australia. The radiance values were converted to surface reflectance using the atmospheric transfer modelling code 6S. Adjustment factors, to account for the spectral band difference effects between sensors, were computed from field-measured reflectance spectra. The LISS III surface-reflectance estimates were found to be consistently lower than the Landsat estimates. The difference between the IRS P6 LISS III reflectances and the median Landsat 5 TM reflectances were approximately 20%, 22%, 12% and 3.5% for Landsat bands 2, 3, 4 and 5, respectively. Further research is required to determine whether updated calibration parameters for the LISS III sensor are required.

Alternatives to Landsat-5 Thematic Mapper for operational monitoring of vegetation cover: considerations for natural resource management agencies.

There is a serious concern as to whether the Landsat-5 Thematic Mapper (TM) will provide imagery up until the launch of the Landsat Data Continuity Mission (LDCM), which is expected in December 2012. The concern is due to fuel shortages and sporadic satellite and sensor problems. The Landsat-7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) malfunction in 2003 means that ETM+ imagery contains strips of missing data. Consequently there is concern that the highest quality Landsat imagery may not be available for monitoring for the years 2011 and 2012. Natural resource monitoring agencies, therefore, are faced with the serious challenge of determining a suitable alternative to Landsat-5 TM imagery should the need arise. While literature exists on possible alternatives, there is a lack of studies that provide guidelines to monitoring agencies on the issues to be explored when considering an alternative data source. We undertook a study that identified Landsat-7 ETM+, SPOT4 HRVIR, SPOT5 HRG, and IRS-P6 LISS-III as the most suitable alternatives for monitoring vegetation cover in Queensland and New South Wales (NSW) in eastern Australia. None of the sensors were found to be ideal candidates due to a combination of one or more of lower radiometric quality, increased data volumes, additional processing requirements, and higher purchasing costs. We found that the low cost and ease of access to Landsat-7 ETM+ made it a technically and economically viable option for annual monitoring of woody vegetation extent and change in eastern Australia. However, Landsat-7 ETM+ may not be an option in many areas of the world due to high cloud coverage. We used the experiences gained through this work to recommend a process that natural resource management agencies can use to explore the issues related to selecting an alternative to Landsat-5 TM for land cover monitoring.