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High resolution multispectral image and machine learning algorithms for saltmarsh classification and biomass modelling
thesisposted on 2022-03-28, 15:47 authored by Sikdar Mohammad Marnes Rasel
Saltmarsh is one of the important vegetation of wetlands. Due to a range of pressures, it has been declared as an Endangered Ecological Community (EEC) in Australia. Therefore, monitoring and mapping of the distribution of saltmarshes species are important to wetland management, conservation and distribution. This rigorous task requires intensive fieldwork and collection of ancillary data that are time-consuming. Remote Sensing offers a practical and economic means of plant sciences classification and biomass modelling. However, selecting suitable remote sensing systems and their data are important for mapping saltmarshes. Hyperspectral remote sensing can be used to monitor this endangered community. However, there are some crucial limitations of hyperspectral data that have been found in the current study area and discussed in the introduction. To overcome these limitations, Worldview-2 with its higher spatial (1.84 m) resolution is seen as a trade-off between the advantages of multispectral resolution satellite data and hyperspectral data. In this thesis, imagery acquired by three different sensors were used to compare the performance of machine learning classification methods and biomass regression models. Maximum Likelihood Classifier (MLC) and two advanced algorithms, Random Forest (RF) and Support Vector Machine (SVM) were used for classification. These two algorithms were also tested to develop a biomass model for Sporobolus virginicus species using multispectral Worldview-2 data. Reflectance and NDVI based vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. Proportional reduction of sample size (100 % to 33%) was focused to test the potentiality of both algorithms. RF showed significant changes in overall accuracy when the sample size was reduced from 100 % to 33%. Conversely, there were no significant changes in the accuracy for SVM when the sample size equally dropped from 100 % to 33%. When biomass model for RF (R2 =0.72, RMSE = 0.166 kg/m2) and SVR (R2 = 0.66, RMSE = 0.200 kg/m2) were compared, there was a significant (p =< 0.0001) difference was observed. Further research is crucial to explore the sensitivity of this method with the spatial autocorrelation of the training samples for random forest application in saltmarsh monitoring.
Table of ContentsChapter 1. General introduction -- Chapter 2. Endmember identification from EO-1 Hyperion and a comparison between hyperspectral and multispectral images for a saltmarsh community in the Hunter Wetland National Park -- Chapter 3. Spectral separability and spatial sampling unit analysis of saltmarsh species class using Worldview-2 and Landsat 8 OLI data -- Chapter 4. Scope for saltmarsh classification and biomass estimation using Support Vector Machine (SVM) and Random Forest (RF) : a review -- Chapter 5. Support vector machine (SVM) classifier with small training samples for wetland saltmarsh environment -- Chapter 6. An evaluation of equal training sample allocation for a saltmarsh environment using Random Forest (RF) and Support Vector Machine (SVM) classification algorithms -- Chapter 7. Saltmarshes biomass modelling using Random Forest (RF) and Support Vector Machine (SVM) regressions from multispectral data -- Chapter 8. Synthesis: High spatial resolution multispectral image and machine learning algorithms for saltmarsh classification and biomass modelling -- Appendix -- Bibliography.
NotesBibliography: pages 168-184 Empiriical thesis.
Awarding InstitutionMacquarie University
Degree TypeThesis PhD
DegreePhD, Macquarie University, Faculty of Science and Engineering, Department of Environmental Sciences
Department, Centre or SchoolDepartment of Environmental Sciences
Year of Award2017
Principal SupervisorMichael Chang
Additional Supervisor 1Neil Saintilan
Additional Supervisor 2Timothy John Ralph
RightsCopyright Sikdar Mohammad Marnes Rasel 2017. Copyright disclaimer: http://mq.edu.au/library/copyright
JurisdictionNew South Wales
Extent1 online resource (xviii, 184 pages) colour illustrations, maps
Former Identifiersmq:71409 http://hdl.handle.net/1959.14/1274054