Coastal habitats are highly sensitive to change and highly diverse. Degrading environmental conditions have led to a global decline in biodiversity through loss, modification and fragmentation of habitats, triggering an increased effort to conserve these ecosystems. Remote sensing is important tool for filling in critical information gaps for monitoring habitats, yet significant barriers exist for operational use within the ecological and conservation communities. Reporting on both extent and condition of habitats are critical to fulfil policy requirements, specifically the ECs Habitat’s Directive. This study focuses on the use of Very High Resolution (VHR) optical imagery for retrieving parameters to identifyassociations that can separate habitat boundaries for extent mapping down to species level for indicators of condition, with a focus on operational use. The Earth Observation Data for Habitat Monitoring (EODHaM) system was implemented using Worldview-2 data from two periods (July and September), in situ data and local ecological knowledge for two sites in Wales, Kenfig Burrows SAC and Castlemartin SSSI. The system utilises the Food and Agricultural Organisation’s (FAO) Land Cover Classification System (LCCS) but translations between land cover and habitat schemes are not straight forward and need special consideration that are likely to be site specific. Limitations within therule-based method of the EODHaM system were identified and therefore augmented with machine learning based classification algorithms creating a hybrid method of classification generating accurate (>80% overall accuracy) baseline maps with a more automated and repeatable method. Quantitative methods of validation traditionally used within the remote sensing community do not consider spatial aspects of maps. Therefore, qualitative assessments carried out in the field were used in addition to error matrices, overall accuracy and the kappa coefficient. This required input from ecologists and site specialists, enhancing communication and understanding between the different communities. Generating baseline maps required significant amount of training data and updating baselines through change detection methods is recommended for monitoring. An automated, novel map-to-image change detection was therefore implemented. Natural and anthropogenic changes were successfully detected from Worldview-2 and Sentinel-2 data at Kenfig Burrows. An innovative component of this research was the development of methods, which were demonstrated to be transferable between both sites and increased understanding between remote sensing scientist and ecologist. Through this approach, a more operational method for monitoring site specific habitats through satellite data is proposed, with direct benefits for conservation, environment and policy.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:752866 |
| Date | January 2017 |
| Creators | Jones, Gwawr Angharad |
| Contributors | Bunting, Peter ; Lamb, Henry |
| Publisher | Aberystwyth University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2160/cfb598d7-9bb7-44a7-8725-bcf13d81657b |
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