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Title: Reedbed mapping using remotely sensed data
Author: Onojeghuo, Alex Okiemute
ISNI:       0000 0004 2746 1878
Awarding Body: Lancaster University
Current Institution: Lancaster University
Date of Award: 2010
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In the UK reedbeds dominated by Phragmites australis have been identified as a priority habitat for most regional Biodiversity Partnerships. Information on the current distribution and quality of reedbed sites across the UK is lacking, yet such information is vital in developing suitable management plans for the conservation and expansion of this threatened habitat. The focus of this thesis is to develop a suitable methodology for accurately mapping the distribution and assessing the biophysical properties of reedbed habitats using remotely sensed data. Three study sites situated in the North West region of the UK were used: Leighton Moss nature reserve in Lancashire, and the River Leven and Esthwaite Water situated in Cumbria. The remotely sensed data used in this study included high-resolution satellite and airborne imagery and ground-based spectral data. Results of the first analytical chapter (i.e. chapter 3) demonstrated the potential of using high resolution QuickBird multi spectral satellite imagery to derive accurate maps of reedbeds through appropriate analysis of image texture, careful selection of input bands, spatial degradation of input bands, selection of a suitable classification algorithm and post-classification refinement using terrain data. Results of the second analytical chapter (chapter 4) demonstrated the benefits of using multi-seasonal images over single-date images and the effectiveness of incorporating spectral bands with textural measures. Through careful selection of appropriate classification technique, the input image datasets could be used to generate optimal reedbed maps. The results of the multi-seasonal reedbed mapping experiment conducted using QuickBird imagery was the basis for the field spectrometry experiment. The study aimed at monitoring and understanding variations in the spectral reflectance and biophysical properties of reedbeds canopies throughout the seasonal phenological cycle and to identify the optimal spectral indices for quantifying biophysical properties (chapter five ). The results of the experiment indicated that the narrow- band derived Difference Vegetation Index (DV I) and Renormalised Difference Vegetation Index (RDVI) provided the most accurate e'~~iIi1~tes of the leaf area index (LAl) for reedbed canopies (r = 0.77 and 0.72 respectively). Having observed the limitations of accurately deriving canopy heights from experiments conducted in chapter 5, the potential for quantifying canopy biophysical properties from light detection and radar (LiDAR) data (elevation and intensity) was investigated in chapter 6. The study demonstrated some of the potential and limitations of using LiDAR data for characterising reedbed canopies. A canopy height model (CHM) was generated by subtracting the Ordnance Survey (OS) derived digital terrain model (DTM) from the LiDAR- derived digital surface model (DSM). The density of first return points was high for reedbeds and these were able to generate an accurate CHM, when validated against field measurements. LiDAR intensity data displayed specular reflection along the centre of the flight line over reedbeds and water bodies, but not for other land cover/vegetation types. The LiDAR intensity data showed potential for containing considerable information on reedbed canopy structure and pattern that is valuable from an ecological perspective. Results of the final analytical chapter (chapter 7) demonstrated the value in combining appropriately compressed hyperspectral imagery with LiDAR data for the effective mapping of reedbed habitats. The most effective image compression technique was the spectrally segmented principal component analysis (SSPCA), which had the optimal combination of reedbed accuracy and processing efficiency. A substantial improvement in the accuracy of reedbed delineation was achieved when a mask generated by applying a 3m threshold to the LiDAR- derived CHM was used to filter the reedbed map derived from the optimal SSPCA image dataset. Based on the fmdings of chapter 5 and 6, the hyperspectral and LiDAR data was used to derive LAI and canopy height (CH) maps of reedbeds respectively, two vital biophysical measures needed in estimating the quality of reedbed canopies. Hence, this study is a step forward in utilizing spectral, spatial and structural data contained in remotely sensed data for the mapping of reedbed quantity and quality. This research has demonstrated the potential of using remotely sensed data, complemented with adequate ground based information for mapping the spatial extent and quality of reedbed canopies in three specific sites across the North " West region in the UK. Based on the success with a specific habitat type, suggestions are made to further expand these techniques to explore fine scale mapping of more habitats using remotely sensed data of high spatial resolution. Hence, two major studies are recommended for future work, namely (1) updating the Phase 1 habitat survey map using remote sensing techniques, and (2) the integration of high spatial resolution satellite imagery (hyperspectral or QuickBird) and LiDAR data for vegetation mapping and deriving biophysical measures.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available