Tim Danaher

Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery

The detection of long term trends in woody vegetation in Queensland, Australia, from the Landsat-5 TM and Landsat-7 ETM+ sensors requires the automated prediction of overstorey foliage projective cover (FPC) from a large volume of Landsat imagery. This paper presents a comparison of parametric (Multiple Linear Regression, Generalized Linear Models) and machine learning (Random Forests, Support Vector Machines) regression models for predicting overstorey FPC from Landsat-5 TM and Landsat-7 ETM+ imagery. Estimates of overstorey FPC were derived from field measured stand basal area (RMSE 7.26%) for calibration of the regression models. Independent estimates of overstorey FPC were derived from field and airborne LiDAR (RMSE 5.34%) surveys for validation of model predictions. The airborne LiDAR-derived estimates of overstorey FPC enabled the bias and variance of model predictions to be quantified in regional areas. The results showed all the parametric and machine learning models had similar prediction errors (RMSE < 10%), but the machine learning models had less bias than the parametric models at greater than ~60% overstorey FPC. All models showed greater than 10% bias in plant communities with high herbaceous or understorey FPC. The results of this work indicate that use of overstorey FPC products derived from Landsat-5 TM or Landsat-7 ETM+ data in Queensland using any of the regression models requires the assumption of senescent or absent herbaceous foliage at the time of image acquisition.

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.

A Water Index for SPOT5 HRG Satellite Imagery, New South Wales, Australia, Determined by Linear Discriminant Analysis

A new water index for SPOT5 High Resolution Geometrical (HRG) imagery normalized to surface reflectance, called the linear discriminant analysis water index (LDAWI), was created using training data from New South Wales (NSW), Australia and the multivariate statistical method of linear discriminant analysis classification. The index uses all four image bands, and is better at separating water and non-water pixels than the two commonly used variations of the normalized difference water index (NDWI), which each only use two image bands. Compared across 2,400 validation pixels, from six images spanning four years, the LDAWI attained an overall accuracy of 98%, a producer’s accuracy for water of 100%, and a user’s accuracy for water of 97%. These accuracy measures increase to 99%, 100% and 98% if cloud shadow and topographic shadow masks are applied to the imagery. The NDWI achieved consistently lower accuracies, with the NDWI calculated from the green and shortwave infrared (IR) bands performing slightly better (91% overall accuracy) than the NDWI calculated from the green and near IR bands (89% overall accuracy). The LDAWI is now being routinely used on an archive of over 2,000 images from across NSW, as part of an operational environmental monitoring program.

Evaluation of Different Topographic Corrections for Landsat TM Data by Prediction of Foliage Projective Cover (FPC) in Topographically Complex Landscapes

The reflected radiance in topographically complex areas is severely affected by variations in topography; thus, topographic correction is considered a necessary pre-processing step when retrieving biophysical variables from these images. We assessed the performance of five topographic corrections: (i) C correction (C), (ii) Minnaert, (iii) Sun Canopy Sensor (SCS), (iv) SCS + C and (v) the Processing Scheme for Standardised Surface Reflectance (PSSSR) on the Landsat-5 Thematic Mapper (TM) reflectance in the context of prediction of Foliage Projective Cover (FPC) in hilly landscapes in north-eastern Australia. The performance of topographic corrections on the TM reflectance was assessed by (i) visual comparison and (ii) statistically comparing TM predicted FPC with ground measured FPC and LiDAR (Light Detection and Ranging)-derived FPC estimates. In the majority of cases, the PSSSR method performed best in terms of eliminating topographic effects, providing the best relationship and lowest residual error when comparing ground measured FPC and LiDAR FPC with TM predicted FPC. The Minnaert, C and SCS + C showed the poorest performance. Finally, the use of TM surface reflectance, which includes atmospheric correction and broad Bidirectional Reflectance Distribution Function (BRDF) effects, seemed to account for most topographic variation when predicting biophysical variables, such as FPC.

A regression model approach for mapping woody foliage projective cover using landsat imagery in Queensland, Australia

This paper describes the development of a regression model for predicting foliage projective cover (FPC) using an extensive set of over 2000 field observations for Queensland, Australia. The model includes Landsat TM and ETM+ imagery and a climatological ancillary variable, vapour pressure deficit. The resulting model was validated using independent site data and preliminary validation against FPC estimates from airbourne laser scanner data is presented. Results suggest the model is robust and performing well over a range of soil types and vegetation communities. This regression-based methodology is currently included in the process of monitoring annual woody vegetation change over Queensland and will form the basis of new products for monitoring longer term trends in FPC

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.

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.

Calibration of multiple Landsat sensors based on pseudo-invariant target sites in Western Queensland, Australia

The Statewide Landcover and Trees Study (SLATS) has used both Landsat-5 TM and -7 ETM+ imagery to monitor short-term woody vegetation changes throughout Queensland. In order to analyse long-term vegetation change, time-based trends that are an artifact of the sensor system must be removed. Although the calibration trends of TIM and ETM+ are well described, information on the calibration of the older Landsat-2 and -5 MSS sensors is relatively scarce. This paper describes the use of three pseudo-invariant target sites in western Queensland to achieve an operational calibration of TM and ETM+ data. Following the success of the current study, these targets will be used in the near future to achieve sensor specific calibrations for the Landsat-2 and -5 MSS SLATS data

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.