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Spatial Dynamics of Nearshore Marine Habitats From Low Altitude Remote Sensing for Conservation and Planning

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Thesis(10.76 MB)

Date

2022

Authors

Chand, Subhash

Supervisor

Bollard, Barbara
Shears, Nick
Orams, Mark

Item type

Thesis

Degree name

Doctor of Philosophy

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Volume Title

Publisher

Auckland University of Technology

Abstract

Biogenic habitats such as wild oyster reefs and seagrass meadows support biodiversity and essential ecological services. However, these biogenic habitats are susceptible to change from anthropogenic and environmental impacts. Hence, they are the subjects of significant conservation and planning research. Mapping and monitoring the spatial dynamics of the nearshore marine environment is challenging due to its dynamic nature, where many processes operate simultaneously over varying temporal and spatial scales within tidal variations. To address this challenge, this study aimed to develop the scientific understanding and novel applications of proximal low altitude remote sensing of nearshore marine environments using visible (VIS) and near-infrared (VIS+NIR) sensors from 50m altitude. The research examined the application of remotely piloted aircraft system (RPAS) technology in two nearshore marine environments, a rocky intertidal reef (Meola reef) and a mudflat (Cox’s Bay) in Waitemata Harbour, North Island of New Zealand. Over time remotely sensed products derived from airborne and spaceborne platforms were highly influential in mitigating this challenge, but limitations with these technologies persist. Aerial mapping and monitoring wild oyster reefs and seagrass meadows require high spectral and spatial resolution imagery to be successfully delineated and classified. Hence, the advent of proximal low altitude remote sensing technology such as lightweight RPAS has been a step-change in mapping and monitoring research. RPAS technology is accessible and can reliably collect high-resolution aerial datasets in various locations at user-defined periods with repeated surveys and high accuracy. In this research, the comparison between RPAS aerial data collection and standard field observations highlighted that the aerial perspective provided by the RPAS allowed for a more precise spatial assessment of wild oyster reefs and seagrass meadows. In particular, the application of RPAS at 50m provided a ground sampling distance of 1.3cm/pixel (VIS sensor) and 3.5cm/pixel (VIS+NIR sensor), which improved the detection and classification of wild oyster reefs and seagrass meadows. Most studies on wild oyster reefs used RPAS within the VIS electromagnetic spectrum along temperate intertidal rocky reefs. Therefore, there was an opportunity to demonstrate the potential of an RPAS with a VIS+NIR sensor and structure from motion photogrammetry technique to identify and characterise wild oyster reefs in a temperate intertidal estuary (Chapter 3). The findings showed that additional spectral bands (RedEdge and NIR) enhanced feature detection and increased the potential to delineate oyster reefs within a heterogeneous marine ecosystem in this study. A rule-based classification technique was used to detect and classify oyster reefs based on their spectral characteristics following segmentation and achieved an overall accuracy of 83.9% and a Kappa coefficient of 69.8%. The findings from this study also established that RPAS as a survey tool is optimum to target marine tidal and metrological conditions and could be ideal for monitoring and locating the distribution of predatory borer snails from low altitudes. Researchers in New Zealand have established that seagrass meadows, a valuable resource, are under pressure from human activities and climate change and are at risk of declining. Although progress has been made locally to understand the marine environment, there are still gaps in temporal data consistency, limiting the full potential to understand drivers of change. Therefore, there was a research opportunity to bridge these gaps by developing new scale-appropriate techniques for rapid assessment and monitoring changes in the seagrass ecosystems (Chapter 4). This study demonstrated the potential of an RPAS with a VIS+NIR sensor for low altitude mapping and high-resolution spatial assessment of intertidal seagrass meadow and modified a spectral index. The results from object-based image analysis (OBIA) and the maximum likelihood classification technique achieved an overall accuracy of 95% and a Kappa coefficient of 81%. The findings from this study showed that researchers could gain valuable insights to observe local changes and identify drivers of change. Results have established that RPAS with a VIS+NIR sensor could consistently fill the multi-temporal data gap with repeated surveys. Marine managers can use the methodology from this study to quickly identify the drivers of change and prevent this crucial resource from reaching its tipping point. Furthermore, researchers found that the RPAS VIS sensor limited the spectral and textural separability between oyster reefs and sediment. Researchers also established that broad spectral resolution from many multispectral satellite sensors restricted the detection of wild oyster reefs. Hence there was a research opportunity to explore VIS and VIS+NIR sensors for spatial assessments, monitoring, and mapping of wild oyster reefs from proximal low altitude remote sensing (Chapter 5). The results from this study showed that wild oyster reefs in the VIS+NIR imagery achieved an overall classification accuracy of 85% compared to 70% from the VIS imagery. The findings showed that spectral resolution was more critical than the spatial resolution that correctly detected and classified oyster reefs in this study. The findings also established that the remote sensing technique used for ecology and conservation offers scale-appropriate spatial assessment, monitoring, and mapping of benthic habitats in challenging and inaccessible temperate marine environments. Moreover, seagrass decline also affects associated species and their vital linkage with the adjacent habitats, igniting a broader degradation with long-lasting impacts on other habitats and biodiversity dependent on seagrass within an ecosystem. Researchers have established that the possibility of identifying subtle fine-scale seasonal change goes undetected and undocumented. While different RPAS mapping and monitoring techniques have been applied for seagrass research, there is a gap in simultaneously testing VIS and VIS+NIR domains to detect fine-scale seasonal seagrass change in a dynamic nearshore marine environment. This gap gave rise to a research opportunity that tested the performance of VIS and VIS+NIR sensors to detect fine-scale time-series seagrass seasonal change in a dynamic nearshore marine environment using spectral indices and supervised machine learning classification technique (Chapter 6). No attempts were made to identify and quantify the abundance and distribution of marine macrofauna benthic activity from proximal low altitude remotely sensed drone imagery. Hence, this research also tested whether macrofauna benthic activity abundance and distribution amongst seagrass meadows can be determined from proximal low altitude remotely sensed drone aerial imagery. The VIS imagery and support vector machine (SVM) classification results produced an average overall class accuracy of 93% and an average Kappa coefficient of 0.90, and VIS+NIR sensors had an average overall class accuracy of 95% and an average Kappa coefficient of 0.93. These accuracies established that the spectral indices were fundamental in this study to identify and classify seagrass density. The other important finding revealed that seagrass-associated macrofauna benthic activity showed increased or decreased abundance and distribution with seasonal seagrass variability from drone high spatial resolution orthomosaic. These findings are essential for seagrass conservation because managers can quickly detect fine-scale seasonal changes and take mitigation actions before the decline of this keystone species affects the entire ecosystem. Also, proximal low-altitude, remotely sensed time-series seasonal data provided valuable contributions for documenting spatial ecological seasonal change in this dynamic marine environment. Collectively, this research improved the understanding of proximal low altitude remote sensing in a dynamic nearshore marine environment and its competency to supplement in-situ and other remotely sensed datasets for conservation and planning. In addition, this research identified the limitations and strengths of its application for monitoring, mapping, and understanding the spatial dynamics of the nearshore marine environments.

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https://hdl.handle.net/10292/15262

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