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Object-based image analysis of ultra-fine spatial resolution imagery acquired over a saltmarsh environment by an Unmanned Aircraft ASystem (UAS)


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Kelcey, JM (2014) Object-based image analysis of ultra-fine spatial resolution imagery acquired over a saltmarsh environment by an Unmanned Aircraft ASystem (UAS). PhD thesis, University of Tasmania.

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Unmanned Aircraft Systems (UAS) are an emerging technology in the field of remote
sensing. Two fundamental differences of UAS when compared with traditional aerial
remote sensing platforms are the operational altitude and payload capacity. The lower
operational altitude of UAS generates ultra-fine spatial resolution data (< 10 cm). The
small size of most UAS platforms allows scientific research groups to transport and operate
the platform within small focused study areas. However, a small size also places physical
limitations on UAS sensor payload carrying capacity. This requires a compromise between
sensor functionality, cost, and weight. Sensor feature reduction or miniaturisation achieves
this compromise but at the cost of data quality. This thesis advances UAS remote sensing
through an exploration of the development, scale analysis and application of ultra-fine
spatial resolution UAS data.
Two sites of remnant cold temperate saltmarsh vegetation in Tasmania were selected
to assess UAS remote sensing. Frequent salt water spray and tidal inundation within
saltmarsh create a saline gradient that limits the establishment of larger canopy species.
This has resulted in the dominance of salt and water-logging tolerant herbaceous and
small woody shrub species. Despite the harsh environmental conditions, the combination
of land wash-off and tidal inundation both readily supply and redistribute nutrients,
creating one of the most environmentally productive environments. Measuring the finescale
vegetation distribution and productivity of cold temperate saltmarsh vegetation
requires the ultra-fine spatial resolution data of UAS.
In this study, a sensor correction methodology was designed and implemented to reduce
the effects of noise and distortion in the 6-band multispectral miniature multiple camera
array (mini-MCA) produced by Tetracam. This methodology includes techniques for
sensor noise reduction using dark offset subtraction, vignetting correction through
field look-up tables, and lens distortion correction by implementing the Brown-Conrady model. The sensor correction framework is demonstrated through a real-world application
on UAS-derived saltmarsh data. Chapter 2 demonstrates that sensor noise and distortions
can be satisfactorily corrected in 6-band Tetracam mini-MCA data acquired from a small
multirotor UAS.
Once image data are constructed, the next challenge lies in deconstructing the complex
ultra-fine spatial resolution UAS data to derive meaningful information. The increased
resolving power of UAS data provides spatial measurements of image features at scales
previously too small to distinguish. This results in increased spatial complexity as finescale
structural variation becomes measurable. A key challenge is to disassemble and
simplify this fine-scale variation for the extraction of information. This is achieved through
two frameworks that provide a meaningful spatial generalisation using image texture
models and geospatial object-based image analysis (GEOBIA).
Image texture is defined as the replications, symmetries and patterns in tonal structure.
Image texture models are used to quantify the tonal structure in a local neighbourhood
into a single, statistical measure. The large number of available texture models and
parameters, as well as the dependence of texture on image scale and context, complicates
the optimal selection of image texture measures. In Chapter 3, a texture selection
methodology is introduced to provide a rapid, broad assessment of image texture.
The texture selection framework is illustrated using a 6-band multispectral dataset of a
saltmarsh site. Four texture models are investigated: a simple first-order kernel, the greylevel
co-occurrence matrix (GLCM), local binary pattern operator (LBP), and wavelets.
Using image subsets, 693 texture measures are extracted from seven vegetation and nonvegetation
groundcover classes. A random forest ensemble classifier was used to quantify
the relative class-specific importance of individual texture measures. A correlation threshold
was used to remove highly correlated, less important measures before forward inclusion
was used to identify the minimum optimal number of texture measures. The number of
required texture measures was linked with class spectral variation, with spectrally complicated classes requiring more measures. The performance of the measures was tested
across the entire image, with a recorded improvement of 17.2% in overall classification
accuracy with the inclusion of selected texture measures.
GEOBIA extends traditional pixel-based analysis through the segmentation of imagery
into meaningful objects. The results of the initial segmentation determine the units of
analysis, and their accuracy is therefore paramount to the entire analysis. As with texture,
image segmentation is dependent upon image structure and content. In Chapter 4,
a methodology is presented utilising image subsets to identify class-specific relative scales
of image segmentation through identifying under- and over-segmentation. Reference objects
were used to compare image segmentation results against a meaningful real-world
abstraction. Under-segmentation was tested using spatial area metrics, and was quanti
fied on a class-by-class basis whenever a subset recorded 100% omission in labelling.
Over-segmentation was identified by extracting the statistical properties of objects and
then testing the separability using a random forest model. The insuficient spatial generalisation
of over-segmentation resulted in reduced class separability. Furthermore, spatial
accuracy was limited by classification accuracy, as the need of spatial generalisation to
achieve class separability required suitably large objects. It was found that this dependence
upon objects for spatial generalisation could be reduced through the incorporation
of texture measures.
Chapter 5 explores the scale potential of ultra-fine spatial resolution data. Field-level
biomass modelling relies upon the construction of allometric models for the rapid estimation
of biomass based upon easily measurable plant characteristics. Allometric modelling
is regarded as the most accurate approach for estimating plant biomass, but its extension
to remotely sensed data has been limited by data resolution. Coarser data resolution
may limit or exclude the ability to measure the parameters required of plant allometric
biomass models. The potential of ultra-fine resolution UAS data to measure allometric
parameters is presented in Chapter 5, which is focused on fine-scale shrub biomass. Field derived allometric relationships are used to deconstruct shrub structure through image
segmentation. Allometric parameters derived from the shrub components are then used
to estimate biomass.
This thesis demonstrates a methodology to develop and analyse UAS remotely sensed
data, illustrating the scale potential of ultra-fine spatial resolution data. The increased
complexity of fine-scale variability is a recognised problem associated with the improved
resolving power of image data. This variability is a central challenge for UAS remote
sensing and the analysis of the ultra-fine data scale it generates. By developing a clear
methodology to construct and meaningfully disassemble ultra-fine resolution UAS data,
this thesis provides a foundation which provides broader access to the novel scale niche
that UAS measurements fill.

Item Type: Thesis (PhD)
Keywords: UAS, GEOBIA, saltmarsh, remote sensing, GIS, spatial
Copyright Information:

Copyright 2014 the author(s)

Date Deposited: 07 Sep 2015 03:13
Last Modified: 11 Mar 2016 05:52
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