Remote sensing provides an ideal tool for on-going vegetation monitoring programs. Plat- form characteristics determine the spatial scale of sampled data. Traditional aerial or satellite approaches have excluded fine-scale sampling (<10 cm), limiting remote sensing to broader monitoring scales. The monitoring of fine-scale phenomena changes within small, vulnerable communities requires a platform capable of acquiring data at a match- ing sample scale. The advent of low-cost UAS introduces a new paradigm in platform accessibility and data sampling scale.
Low-cost UAS ( <$10,0002 ) enable small research groups to develop a niche remote sensing platform. The accessibility of UAS is dependent upon robust frameworks for constructing and analysing UAS data. Relative to larger, more expensive platforms, the UAS is limited in its payload and power capacity, and therefore requires significant sensor miniaturisation. Sensor miniaturisation is achieved by the removal of space consuming circuitry for onboard data processing and decreasing the size of sensor elements. This decrease in size of sensor elements results in an increase sensitivity to sensor noise. This miniaturisation impacts on data quality, with collected data requiring robust preprocess- ing and corrections prior to analysis.
Analysis of ultra-fine spatial resolution UAS data may require meaningful spatial gen- eralisation to achieve an appropriate scale for analysis. Texture models and GEOBIA techniques for spatial generalisation are well established. The sample scale may be shifted through the local neighbourhood of a texture model or the similarity threshold of a GEO- BIA segmentation. While the scale of a single data source is fixed, variation between data collection regimes creates variation in scale between data sources. An optimal texture lo- cal neighbourhood or similarity threshold is relative to the fixed scale, and is therefore not transferable between different data sources. The fundamental problem that this
thesis aims to address is how to identify the optimal analysis scale and how to identify the optimal texture and segmentation parameters for individual vegetation communities.
1.3
Objectives
The overarching aim of this thesis is to develop and apply novel remote sensing techniques to UAS acquired data for the generation of spatial information suitable for fine-scale saltmarsh mapping and monitoring.
The thesis outlines three analysis frameworks:
1. Radiometric sensor correction framework for a lightweight 6-band multispectral UAS sensor;
2. Spatial generalisation framework to identify optimal class-specific scales of analysis utilising texture model and GEOBIA segmentation parameters; and
3. Biomass estimation framework based upon field-derived above-ground biomass (AGB) allometric models and UAS-derived vegetation structure.
The specific objectives of this research are:
1.3.1 Objective 1
Assess sensor artefacts of a 6-band multispectral UAS sensor and identify physical and electrical sources of data collection errors. Assess existing approaches to the correction of sensor error. Incorporate suitable correction methods into a rigorous framework that is transferable between sensor systems.
Assess sources of sensor error within the TetraCam Miniature Multiple Camera Array (mini-MCA).
Develop an automated framework to pre-process raw Mini-MCA data into radio- metrically corrected, aligned multispectral images.
Demonstrate the data quality improvement of the corrective framework using real- world saltmarsh data.
1.3.2 Objective 2
Assess the performance of the UAS ultra-fine resolution sample scale for image classifica- tion. Assess existing image texture modelling and GEOBIA segmentation methodologies for spatial generalisation. Develop image texture modelling and GEOBIA segmentation frameworks to spatially generalise ultra-fine UAS spatial resolution to an optimal class- specific analysis scale.
Assess the effect of the fine-scale sampling of UAS data on the classification of saltmarsh data.
Research and develop a texture framework using IDL that compares texture models and measures to identify relevant class-specific scales of analysis.
Research and develop a GEOBIA framework using the OGR/GDAL libraries within the Python programming language to assess segmentation parameters and identify class-specific scales of analysis.
Assess the performance of both the texture and GEOBIA frameworks for the clas- sification of UAS saltmarsh data.
Explore the potential complimentary nature of texture and GEOBIA approaches to spatial generalisation.
1.3.3 Objective 3
Explore the UAS capacity to generate AGB estimations of fine spatial scale vegetation. Develop methodology to extract structural and AGB allometric parameters from fine-scale UAS samples. Assess the direct substitution of field-based shrub allometric parameter measurements with UAS-derived measurements.
Research and develop structural and AGB field-based shrub allometric models util- ising destructive sampling.
Identify and develop framework that utilises structure-from-motion and GEOBIA to extract suitable allometric parameters from fine-scale UAS data.
Assess the transfer of parameter measurement from field to UAS observation.
1.4
Thesis Structure
This thesis is presented as a collection of related scientific articles. The structure of the thesis follows a UAS remote sensing workflow, starting from data pre-processing, through to image analysis techniques and finally the application of UAS remote sensing. Chapter 2 is focused upon Objective 1 and details the development of a sensor correction framework for the mini-MCA. Chapters 3 and 4 cover Objective 2. Chapter 3 is the development of a texture framework for the assessment and identification of class-specific texture metrics. Chapter 4 focuses upon a GEOBIA framework that identifies class- specific segmentation parameters. Chapter 4 builds upon Chapter 3 by incorporating the identified texture metrics into GEOBIA to assess the complementary nature of image texture and GEOBIA. Chapter 5 extends the value of UAS measurements for monitoring, by comparing field and UAS observations for the calculation of AGB biomass. Chapter 6 provides concluding remarks on the overall achievements of the thesis, limitations, and suggests future directions for UAS research.
Sensor Correction of a 6-Band
Multispectral Imaging Sensor for UAS
Remote Sensing
The focus of Chapter 2 is upon the development of a radiometric sensor correction frame- work. The chapter provides an assessment of a 6-band multispectral sensor by identi- fying physical and electrical sources of data collection error, and the development of a framework to correct sensor error. The work comprising this chapter is published in the peer-reviewedJournal of Remote Sensing (Kelcey and Lucieer, 2012).