Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

Pham, Thi Hong Lien

Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

dc.contributor.advisor	Brabyn, Lars
dc.contributor.advisor	d'Hauteserre, Anne-Marie
dc.contributor.author	Pham, Thi Hong Lien
dc.date.accessioned	2018-03-28T01:48:58Z
dc.date.available	2018-03-28T01:48:58Z
dc.date.issued	2018
dc.date.updated	2018-03-11T22:30:35Z
dc.description.abstract	Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis techniques. Object-based image analysis (OBIA) and machine learning algorithms are recent advances, which this thesis evaluates. OBIA and machine learning algorithms are first tested using a combination of multiple datasets for identifying individual tree species. These datasets include Quickbird, LiDAR, and GIS derived terrain data. Improvements in tree species classification were obtained and the best data combination was terrain context (based on slope, elevation, and wetness), tree height, canopy shape, and branch density (based on LiDAR return intensity). The availability of a range of classifiers and different data pre-processing techniques adds to the complexity of image analysis. The combinations of these techniques result in a large number of potential outcomes and these need to be evaluated. Therefore, the second part of this research investigated and compared tree species classification performance for different methods (Naïve Bayes - NB , Logistic Regression - LR, Random Forest - RF, and Support Vector Machine - SVM), combined with various dimensionality reduction (DR) methods (Correlation-based feature selection filter, Information Gain, Wrapper methods, and Principal Component Analysis). When DR was used prior to classification, only the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy, and this was achieved without DR. The final part of this thesis demonstrates a new method using OBIA for mapping the biomass change of mangrove forests in Vietnam between 2000 and 2011 from SPOT images. First, three different mangrove associations were identified using two levels of image segmentation followed by a SVM classifier and a range of spectral, texture and GIS information for classification. The RF regression model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy.
dc.format.mimetype	application/pdf
dc.identifier.citation	Pham, T. H. L. (2018). Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms (Thesis, Doctor of Philosophy (PhD)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/11758	en
dc.identifier.uri	https://hdl.handle.net/10289/11758
dc.language.iso	en
dc.publisher	The University of Waikato
dc.rights	All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subject	object-based classification
dc.subject	dimensionality reduction
dc.subject	biomass change
dc.subject	mangrove
dc.subject	LiDAR
dc.subject	machine learning
dc.title	Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms
dc.type	Thesis
pubs.place-of-publication	Hamilton, New Zealand	en_NZ
thesis.degree.grantor	The University of Waikato
thesis.degree.level	Doctoral
thesis.degree.name	Doctor of Philosophy (PhD)
uow.author.twitter	@lienpham_2017