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

authors: Pham, Thi Hong Lien

subjects: object-based classification

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.

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

year: 2018

title: Deoxygenation impacts of lake weed

authors: Gibbs, Max M.

abstract: This talk came about from a series of questions that were asked at a TAG meeting in May 2016. These included:
1) What are the consequences of invasive weed growth?
2) What is the succession of species invasion and will it get worse?
3) What are the risks associated with these invasive weeds spreading?
4) What are native aquatic species of the littoral zone and their values?
5) What are the impacts of the associated water clarity improvements on weed?
6) What are possible control measures/options/experience/typical costs and
challenges?
7) What are the current BOPRC monitoring programmes?
(i) Lakes SPI; what does this mean? Incursion monitoring?
(ii) How do we compare with other regions? Can we improve?
8) What are the agencies involved and their current actions?

text: Deoxygenation impacts of lake weed This talk came about from a series of questions that were asked at a TAG meeting in May 2016. These included:
1) What are the consequences of invasive weed growth?
2) What is the succession of species invasion and will it get worse?
3) What are the risks associated with these invasive weeds spreading?
4) What are native aquatic species of the littoral zone and their values?
5) What are the impacts of the associated water clarity improvements on weed?
6) What are possible control measures/options/experience/typical costs and
challenges?
7) What are the current BOPRC monitoring programmes?
(i) Lakes SPI; what does this mean? Incursion monitoring?
(ii) How do we compare with other regions? Can we improve?
8) What are the agencies involved and their current actions?

year: 2017

title: Hoea te waka reo e 'Āti Rangi ē! He Kohinga Kōrero

authors: Fairhall, Tīahomarama Te Haana

subjects: Māori

abstract: He taonga tuku iho Te Reo Māori nō tāukiuki, nō tuawhakarere, nō onamata rānō, he kākano i ruia mai i Rangiātea. He taonga Te Reo Māori i raro i ngā tikanga o Te Tiriti o Waitangi, ā, nō te tau 1987 ka whakamanahia hai reo tūturu o Aotearoa. Ahakoa he reo tuku iho ki tērā whakatupuranga, ki tērā whakatupuranga, kai te tāmate te reo. He huhua noa ngā rautaki e whakarauora ai i te reo i roto i ngā kura, i ngā whānau, i ngā hapū, i ngā iwi anō hoki. 

Ko te tino o te tuhinga roa nei he āta titiro ki ngā momo rautaki a ngā uri e whakarauora nei i tō tātou reo ki roto o Ngāti Rangiwewehi kia kore ai ā mātou tamariki, ā mātou mokopuna e haere reo kore. Nō roto mai i te tuhinga nei ka āta whakatewhatewha te iwi o Ngāti Rangiwewehi o mua, kia mārama ai ki ōna āhua, ki ōna whenua, ki āna kōrero, ki ōna tāngata, ki ōna pūkōrero, ki tōna ahurei. Ka mutu, ka āta titiro, ka āta tātari, ka āta wetewete i ngā raraunga kōrero i kapo mai i ngā uiuinga, i kapo mai i ngā pātaitai, i kapo mai i ngā pukapuka kōrero ngā momo rautaki e whakarauora ai te reo o ngā uri o Ngāti Rangiwewehi. Hai āpitihanga, ka āta whakatakoto hoki i ngā piki me ngā heke o te reo ā tatauranga nei, ka kitea te ahunga mai i te wā i a koroua mā, i a kuia mā, ā, moroki noa nei.

Kai roto i ngā upoko o te tuhingaroa nei te tino ia o ngā kōrero, ā, e ono ngā upoko e whakatinana ai tēnei rangahau. Kai te upoko tuatahi e tino kōrerohia ana te iwi o Ngāti Rangiwewehi. Kātahi rā ka uru atu ki ngā whakamahukitanga o ngā mātāpono rangahau kai te upoko tuarua. Ka whakaara mai te upoko tuarua i ngā kaponga ariā me ngā rautaki reo hai tuarā mō te tuhingaroa nei. Kai te upoko tuawhā me te tuarima ngā pitopito kōrero, ngā putanga o te pākiki me ngā uiuinga. Ka mutu mai ki te upoko tuaono, ki te kupu whakamutunga o te tuhingaroa.

The Māori language is a taonga protected under the terms of Te Tiriti o Waitangi. An official language of New Zealand, since 1987. However, although a language passed down from generation to generation, the future of the language is not secure. Efforts are endless in providing many strategies of revitalization and ensuring that Te Reo Māori survives within kura, whare wānanga, whānau, hapū and iwi. The purpose of this research is to gather information pertaining to the tribe of Ngāti Rangiwewehi. This study will explore Ngāti Rangiwewehi in a traditional context, their traditional qualities and will also examine the contributions of the descendants in preserving Te Reo Māori. Furthermore, this study will also examine the ups and downs of the language and how Ngāti Rangiwewehi rates statisticaly in the national census under Te Reo Māori. This information will be gathered to examine strategy examples of Te reo that could benefit both present and future generations. As part of my research I will be interviewing key informants and conducting a survey. There are six chapters to this thesis. Chapter one is based on Ngāti Rangiwewehi as a iwi, past and present. Chapter two is based on the research methodology. Chapter three is the literature review, while Chapter four is the questionnaire. Chapter five are the interviews and lastly Chapter 6, the conclusion.

text: Hoea te waka reo e 'Āti Rangi ē! He Kohinga Kōrero He taonga tuku iho Te Reo Māori nō tāukiuki, nō tuawhakarere, nō onamata rānō, he kākano i ruia mai i Rangiātea. He taonga Te Reo Māori i raro i ngā tikanga o Te Tiriti o Waitangi, ā, nō te tau 1987 ka whakamanahia hai reo tūturu o Aotearoa. Ahakoa he reo tuku iho ki tērā whakatupuranga, ki tērā whakatupuranga, kai te tāmate te reo. He huhua noa ngā rautaki e whakarauora ai i te reo i roto i ngā kura, i ngā whānau, i ngā hapū, i ngā iwi anō hoki. 

Ko te tino o te tuhinga roa nei he āta titiro ki ngā momo rautaki a ngā uri e whakarauora nei i tō tātou reo ki roto o Ngāti Rangiwewehi kia kore ai ā mātou tamariki, ā mātou mokopuna e haere reo kore. Nō roto mai i te tuhinga nei ka āta whakatewhatewha te iwi o Ngāti Rangiwewehi o mua, kia mārama ai ki ōna āhua, ki ōna whenua, ki āna kōrero, ki ōna tāngata, ki ōna pūkōrero, ki tōna ahurei. Ka mutu, ka āta titiro, ka āta tātari, ka āta wetewete i ngā raraunga kōrero i kapo mai i ngā uiuinga, i kapo mai i ngā pātaitai, i kapo mai i ngā pukapuka kōrero ngā momo rautaki e whakarauora ai te reo o ngā uri o Ngāti Rangiwewehi. Hai āpitihanga, ka āta whakatakoto hoki i ngā piki me ngā heke o te reo ā tatauranga nei, ka kitea te ahunga mai i te wā i a koroua mā, i a kuia mā, ā, moroki noa nei.

Kai roto i ngā upoko o te tuhingaroa nei te tino ia o ngā kōrero, ā, e ono ngā upoko e whakatinana ai tēnei rangahau. Kai te upoko tuatahi e tino kōrerohia ana te iwi o Ngāti Rangiwewehi. Kātahi rā ka uru atu ki ngā whakamahukitanga o ngā mātāpono rangahau kai te upoko tuarua. Ka whakaara mai te upoko tuarua i ngā kaponga ariā me ngā rautaki reo hai tuarā mō te tuhingaroa nei. Kai te upoko tuawhā me te tuarima ngā pitopito kōrero, ngā putanga o te pākiki me ngā uiuinga. Ka mutu mai ki te upoko tuaono, ki te kupu whakamutunga o te tuhingaroa.

The Māori language is a taonga protected under the terms of Te Tiriti o Waitangi. An official language of New Zealand, since 1987. However, although a language passed down from generation to generation, the future of the language is not secure. Efforts are endless in providing many strategies of revitalization and ensuring that Te Reo Māori survives within kura, whare wānanga, whānau, hapū and iwi. The purpose of this research is to gather information pertaining to the tribe of Ngāti Rangiwewehi. This study will explore Ngāti Rangiwewehi in a traditional context, their traditional qualities and will also examine the contributions of the descendants in preserving Te Reo Māori. Furthermore, this study will also examine the ups and downs of the language and how Ngāti Rangiwewehi rates statisticaly in the national census under Te Reo Māori. This information will be gathered to examine strategy examples of Te reo that could benefit both present and future generations. As part of my research I will be interviewing key informants and conducting a survey. There are six chapters to this thesis. Chapter one is based on Ngāti Rangiwewehi as a iwi, past and present. Chapter two is based on the research methodology. Chapter three is the literature review, while Chapter four is the questionnaire. Chapter five are the interviews and lastly Chapter 6, the conclusion.

year: 2015