Use of new generation geospatial data and technology for low cost drought monitoring and SDG reporting solution : a thesis presented in partial fulfillment of the requirement for the degree of Master of Science in Computer Science at Massey University, Manawatū, New Zealand

Food security is dependent on ecosystems including forests, lakes and wetlands, which in turn depend on water availability and quality. The importance of water availability and monitoring drought has been highlighted in the Sustainable Development Goals (SDGs) within the 2030 agenda under indicator 15.3. In this context the UN member countries, which agreed to the SDGs, have an obligation to report their information to the UN. The objective of this research is to develop a methodology to monitor drought and help countries to report their ndings to UN in a cost-e ective manner. The Standard Precipitation Index (SPI) is a drought indicator which requires longterm precipitation data collected from weather stations as per World Meteorological Organization recommendation. However, weather stations cannot monitor large areas and many developing countries currently struggling with drought do not have access to a large number of weather-stations due to lack of funds and expertise. Therefore, alternative methodologies should be adopted to monitor SPI. In this research SPI values were calculated from available weather stations in Iran and New Zealand. By using Google Earth Engine (GEE), Sentinel-1 and Sentinel- 2 imagery and other complementary data to estimate SPI values. Two genetic algorithms were created, one which constructed additional features using indices calculated from Sentinel-2 imagery and the other data which was used for feature selection of the Sentinel-2 indices including the constructed features. Followed by the feature selection process two datasets were created which contained the Sentinel- 1 and Sentinel-2 data and other complementary information such as seasonal data and Shuttle Radar Topography Mission (SRTM) derived information. The Automated Machine Learning tool known as TPOT was used to create optimized machine learning pipelines using genetic programming. The resulting models yielded an average of 90 percent accuracy in 10-fold cross validation for the Sentinel- 1 dataset and an average of approximately 70 percent for the Sentinel-2 dataset. The nal model achieved a test accuracy of 80 percent in classifying short-term SPI (SPI- 1 and SPI-3) and an accuracy of 65 percent of SPI-6 by using the Sentinel-1 test dataset. However, the results generated by using Sentinel-2 dataset was lower than Sentinel-1 (45 percent for SPI-1 and 65 percent for SPI-6) with the exception of SPI-3 which had an accuracy of 85 percent. The research shows that it is possible to monitor short-term SPI adequately using cost free satellite imagery in particular Sentinel-1 imagery and machine learning. In addition, this methodology reduces the workload on statistical o ces of countries in reporting information to the SDG framework for SDG indicator 15.3. It emerged that Sentinel-1 imagery alone cannot be used to monitor SPI and therefore complementary data are required for the monitoring process. In addition the use of Sentinel-2 imagery did not result in accurate results for SPI-1 and SPI-6 but adequate results for SPI-3. Further research is required to investigate how the use of Sentinel-2 imagery with Sentinel-1 imagery impact the accuracy of the models

Stigmatized attitudes toward people living with HIV in Bangladesh: health care workers' perspectives.

This study was conducted among 526 health care workers (HCWs) in Bangladesh to identify the levels and correlates of stigmatized attitudes toward people living with HIV (PLHIV). HIV-related stigmatized attitudes were measured by a set of items that reflected avoidance attitude of HCWs in hypothetical situations. A multiple linear regression model identified the following correlates of stigma: higher age, high level of irrational fear about HIV and AIDS, being HCW other than a doctor, working in teaching hospital, and rating religion as very important in their life (R (2) = .502). The findings are important for both public health policy planners and human rights activists as high prevalence of stigmatized attitudes among HCWs influence the decision-making process of PLHIV and stop them from accessing voluntary counseling and testing, care, support, and treatment services

Digital Deposition of Ultrathin Pd Films on Well-Defined Pt(111) Electrodes via Surface-Limited Redox Replacement Reaction: An Electron Spectroscopy-Electrochemistry Study

In this study, ultrathin (submonolayer to eight-monolayer) Pd films were deposited one layer at a time on well-defined Pt(111) surfaces via a process known as surface-limited redox replacement reaction (SLR^3). In this digital-deposition method, one monolayer of a nonnoble metal (Cu) is deposited on a noble metal (Pt) by underpotential deposition (UPD). When the UPD adlayer is exposed to cations of less reactive metals (Pd^2 ), it is oxidatively stripped and reductively displaced by the more inert metal. The positive difference between the equilibrium potential of the noble metal in contact with its solvated cations and the equilibrium potential of the UPD adlayer is the driving force behind SLR^3. The Pd films were characterized by Auger electron spectroscopy (AES), low-energy electron diffraction (LEED), and electrochemistry. The LEED patterns indicated (1×1) surface structure of the deposited films. No residual Cu was detected by AES in the Pd films. The Pd ultrathin films on Pt(111) showed HUPD adsorption/desorption peaks which are not observed in bulk Pd. These peaks were observed even at 8 monolayer thick films. The interfacial structure and electrochemical properties of SLR^3-prepared films were compared with those prepared by controlledpotential deposition (CPD). There is a linear correlation between Cu deposition charge (i.e., Pd deposition charge) and I-catalyzed Pd dissolution charge. Electrochemical and LEED results suggest that SLR^3 prepared films are smooth (if not slightly smoother) compared to those prepared by CPD. SLR^3 thus appears to be capable of preparing atomically smooth ultrathin films on Pt(111) surfaces without any additional thermal or electrochemical annealing