AbstractMangroves are dense, spatially heterogeneous forests, and provide a wide range of goods and services. They protect the shore line from powerful waves, and provide habitat and nursery grounds for variety of animals. Mangroves are one of the most threatened habitats in the world, and are disappearing rapidly due to urban, industrial and commercial activities. These threats lead to an increasing demand for quantitative assessment for mangrove monitoring. Historically, mapping has been completed with aerial photos over small areas. Recent advances in remote sensing technology for data acquisition and processing have opened up a wealth of new options. The aim of this work was to push the boundary of data processing to extract metrics relevant to mangrove health mapping, monitoring, and management. This included mangrove species classification, delineating individual tree crowns, and continuous variable mapping of plant biomarkers. The first ever study classified mangrove species at Rapid Creek mangrove forest, Australia was achieved 89% overall accuracy compared to in-situ observations with a WorldView-2 satellite image and a support vector machine algorithm. The first successful method was developed to delineate individual tree crowns using object based image analysis techniques with 92% overall accuracy. One of the innovative prospects of this study was development of methods to continuous variable mapping of plant biomarkers: canopy chlorophyll, above ground biomass, and leaf area index using WorldView-2 images, field measurements, random forest and partial least squares regression algorithms. A relative risk model was developed to assess ecological risk associated with the Darwin Harbour mangroves. This model identified localized changes: cleared, stressed and dead mangroves due to natural or anthropogenic influences. In summary, this research presents unique applications of advanced remote sensing methods for analysing up-to-date spatial information on the current status of Rapid Creek mangroves, and they are a baseline for mangrove monitoring and management.
|Date of Award||2016|
|Supervisor||Karen Joyce (Supervisor), Stefan Maier (Supervisor) & Renee Elise Bartolo (Supervisor)|
A transition from traditional mangrove remote sensing to recent advances: mapping and monitoring
Heenkenda Mudalige, M. K. H. (Author). 2016
Student thesis: Doctor of Philosophy (PhD) - CDU