9 results for Ashraf, Muhammad Salman

  • Enhancing spatial resolution of remotely sensed data for mapping freshwater environments

    Ashraf, Muhammad Salman (2011)

    Doctoral thesis
    University of Waikato

    Freshwater environments are important for ecosystem services and biodiversity. These environments are subject to many natural and anthropogenic changes, which influence their quality; therefore, regular monitoring is required for their effective management. High biotic heterogeneity, elongated land/water interaction zones, and logistic difficulties with access make field based monitoring on a large scale expensive, inconsistent and often impractical. Remote sensing (RS) is an established mapping tool that overcomes these barriers. However, complex and heterogeneous vegetation and spectral variability due to water make freshwater environments challenging to map using remote sensing technology. Satellite images available for New Zealand were reviewed, in terms of cost, and spectral and spatial resolution. Particularly promising image data sets for freshwater mapping include the QuickBird and SPOT-5. However, for mapping freshwater environments a combination of images is required to obtain high spatial, spectral, radiometric, and temporal resolution. Data fusion (DF) is a framework of data processing tools and algorithms that combines images to improve spectral and spatial qualities. A range of DF techniques were reviewed and tested for performance using panchromatic and multispectral QB images of a semi-aquatic environment, on the southern shores of Lake Taupo, New Zealand. In order to discuss the mechanics of different DF techniques a classification consisting of three groups was used - (i) spatially-centric (ii) spectrally-centric and (iii) hybrid. Subtract resolution merge (SRM) is a hybrid technique and this research demonstrated that for a semi aquatic QuickBird image it out performed Brovey transformation (BT), principal component substitution (PCS), local mean and variance matching (LMVM), and optimised high pass filter addition (OHPFA). However some limitations were identified with SRM, which included the requirement for predetermined band weights, and the over-representation of the spatial edges in the NIR bands due to their high spectral variance. This research developed three modifications to the SRM technique that addressed these limitations. These were tested on QuickBird (QB), SPOT-5, and Vexcel aerial digital images, as well as a scanned coloured aerial photograph. A visual qualitative assessment and a range of spectral and spatial quantitative metrics were used to evaluate these modifications. These included spectral correlation and root mean squared error (RMSE), Sobel filter based spatial edges RMSE, and unsupervised classification. The first modification addressed the issue of predetermined spectral weights and explored two alternative regression methods (Least Absolute Deviation, and Ordinary Least Squares) to derive image-specific band weights for use in SRM. Both methods were found equally effective; however, OLS was preferred as it was more efficient in processing band weights compared to LAD. The second modification used a pixel block averaging function on high resolution panchromatic images to derive spatial edges for data fusion. This eliminated the need for spectral band weights, minimised spectral infidelity, and enabled the fusion of multi-platform data. The third modification addressed the issue of over-represented spatial edges by introducing a sophisticated contrast and luminance index to develop a new normalising function. This improved the spatial representation of the NIR band, which is particularly important for mapping vegetation. A combination of the second and third modification of SRM was effective in simultaneously minimising the overall spectral infidelity and undesired spatial errors for the NIR band of the fused image. This new method has been labelled Contrast and Luminance Normalised (CLN) data fusion, and has been demonstrated to make a significant contribution in fusing multi-platform, multi-sensor, multi-resolution, and multi-temporal data. This contributes to improvements in the classification and monitoring of fresh water environments using remote sensing.

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  • Remote sensing of freshwater environments: Trial application on the lower Waikato River.

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2009-06)

    Report
    University of Waikato

    The overall goal of the study is to evaluate multi-sensor and multi-spectral satellite data to characterise different freshwater habitat zones of large rivers and lakes of the Waikato region. The earlier evaluation of different sources of remotely sensed data suggested the implementation of sub-pixel classification on the QuickBird satellite image of Tongariro River delta region. The current research is further expanding the same research on other freshwater environments within the Waikato region.

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  • Evaluating remote sensing data classification techniques for mapping freshwater habitats: Trial application in the Tongariro River delta, Lake Taupo

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2008-07)

    Report
    University of Waikato

    The overall goal of this study is to evaluate different classification techniques that can be applied to multi-source satellite remote sensing data to map different freshwater habitat zones. The Tongariro River delta at the southern side of the Lake Taupo was used as a test site to evaluate these techniques.

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  • Remote sensing of freshwater habitats for large rivers and lakes of the Waikato region using sub-pixel classification

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2007-09)

    Report
    University of Waikato

    The overall goal of this study was to evaluate the feasibility of multi-sensor and multi-spectral remote sensing data for mapping different freshwater habitat zones of large rivers and lakes of the Waikato region. The review of satellite sensors available in NZ for aquatic mapping has identified three data options, which have been purchased for a case study area (Tongariro river delta). These data options are Landsat-5 (30m resolution), ALOS (10m and 2.5m) and QuickBird-2 (2.4m and 0.6m). There is no perfect option available as each option is a compromise between spatial and spectral resolution and cost.

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  • Alternative solutions for determining the spectral band weights for the subtractive resolution merge technique

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2011)

    Journal article
    University of Waikato

    Data fusion using subtractive resolution merge (SRM) is limited because it currently requires fixed spectral band weights predetermined for particular sensors. This is problematic because there is an increasing availability of new and emerging sensors that have no predetermined band weights. There is also a need for fusion between sensors, which potentially requires a large number of sensor combinations and band weight calculations. This article demonstrates how the least sum of minimum absolute deviation (LAD, least absolute deviation) and ordinary least squares (OLS) regressions can calculate band weights for application in the SRM technique using QuickBird satellite and Vexcel aerial images. Both methods were effective in improving image details. The results of LAD and OLS are shown using qualitative and quantitative metrics and through unsupervised classification of freshwater habitat. OLS and LAD produce similar results; however, OLS is computationally simpler and easier to automate. The ability of the user to calculate their own scene specific band weights eliminates the dependence on predetermined sensor band weights. This research concludes that OLS band weight calculations should be integrated into the SRM technique to diversify its application.

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  • Introducing contrast and luminance normalisation to improve the quality of subtractive resolution merge technique

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2013)

    Journal article
    University of Waikato

    Subtractive resolution merge (SRM) is a contemporary image fusion method that produces highly preserved spatial and spectral resolution. This method is limited to dual sensor platforms with specific band ratios between the high-resolution panchromatic image (HRPI) and the low-resolution multispectral image (LRMI). An additional problem with SRM is that some bands are over- or under-represented due to the normalisation function applied. This article provides two modifications that resolve these limitations. SRM builds a synthetic low-resolution panchromatic image (LRPISYN) from the weighted sum of the LRMI bands. This image is modified by using a spatially resampled HRPI instead. The second modification is the use of a contrast and luminance index for the normalising function. These two modifications are tested on QuickBird images (multispectral and panchromatic), as well as fusing SPOT-5 (Satellite Pour l'Observation de la Terre) multispectral image and an aerial photograph. The results show improved quantitative metrics and unsupervised classification compared with the standard SRM technique and other contemporary image fusion methods. Both of these modifications are grouped into a patent pending technique that is called contrast and luminance normalised fusion.

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  • Satellite remote sensing for mapping vegetation in New Zealand freshwater environments: A review

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J.; Collier, Kevin J. (2010)

    Journal article
    University of Waikato

    Freshwater environments in New Zealand provide a range of ecosystem services and contain important biodiversity. Managing these environments effectively requires a comprehensive inventory of the resource and cost-effective tools for regular monitoring. The complex and extensive margins of natural water bodies make them difficult to sample comprehensively. Problems thus occur with extrapolating point-specific sampling to accurately represent the diversity of vegetation in large freshwater bodies. Mapping freshwater vegetation using satellite remote sensing can overcome problems associated with access, scale and distribution, but it requires high-resolution images that have appropriate spectral characteristics. This paper provides an overview of the optical satellite data characteristics required for mapping riparian, submerged and emergent vegetation associated with freshwater environments in New Zealand.

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  • Hindcasting water clarity from Landsat satellite images of unmonitored shallow lakes in the Waikato region, New Zealand

    Hicks, Brendan J.; Stichbury, Glen; Brabyn, Lars; Allan, Mathew Grant; Ashraf, Muhammad Salman (2013)

    Journal article
    University of Waikato

    Cost-effective monitoring is necessary for all investigations of lake ecosystem responses to perturbations and long-term change. Satellite imagery offers the opportunity to extend low-cost monitoring and to examine spatial and temporal variability in water clarity data. We have developed automated procedures using Landsat imagery to estimate total suspended sediments (TSS), turbidity (TURB) in nephlometric turbidity units (NTU) and Secchi disc transparency (SDT) in 34 shallow lakes in the Waikato region, New Zealand, over a 10-year time span. Fifty-three Landsat 7 Enhanced Thematic Mapper Plus images captured between January 2000 and March 2009 were used for the analysis, six of which were captured within 24 h of physical in situ measurements for each of 10 shallow lakes. This gave 32-36 usable data points for the regressions between surface reflectance signatures and in situ measurements, which yielded r (2) values ranging from 0.67 to 0.94 for the three water clarity variables. Using these regressions, a series of Arc Macro Language scripts were developed to automate image preparation and water clarity analysis. Minimum and maximum in situ measurements corresponding to the six images were 2 and 344 mg/L for TSS, 75 and 275 NTU for TURB, and 0.05 and 3.04 m for SDT. Remotely sensed water clarity estimates showed good agreement with temporal patterns and trends in monitored lakes and we have extended water clarity datasets to previously unmonitored lakes. High spatial variability of TSS and water clarity within some lakes was apparent, highlighting the importance of localised inputs and processes affecting lake clarity. Moreover, remote sensing can give a whole lake view of water quality, which is very difficult to achieve by in situ point measurements.

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  • Image data fusion for the remote sensing of freshwater environments

    Ashraf, Muhammad Salman; Brabyn, Lars; Hicks, Brendan J. (2011)

    Journal article
    University of Waikato

    Remote sensing based mapping of diverse and heterogeneous freshwater environments requires high-resolution images. Data fusion is a useful technique for producing a high-resolution multispectral image from the merging of a high-resolution panchromatic image with a low-resolution multispectral image. Given the increasing availability of images from different satellite sensors that have different spectral and spatial resolutions, data fusion techniques that combine the strengths of different images will be increasingly important to Geography for land-cover mapping. Different data fusion methods however, add spectral and spatial distortions to the resultant data depending on the geographical context; therefore a careful selection of the fusion method is required. This paper compares a technique called subtractive resolution merge, which has not previously been formally tested, with conventional techniques such as Brovey transformation, principal component substitution, local mean and variance matching, and optimised high pass filter addition. Data fusion techniques are grouped into spectral and spatial centric methods. Subtractive resolution merge belongs to a new class of data fusion techniques that uses a mix of both spatial and spectral centric approaches. The different data fusion techniques were applied to a QuickBird image of a semi-aquatic freshwater environment in New Zealand. The results were compared both qualitatively and quantitatively using spectral and spatial error metrics. This research concludes that subtractive resolution merge performed better than all the other techniques and will be a valuable technique for enhancing images for freshwater land-cover mapping.

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