Hong Kong's Mandatory Provident Fund system : a study of the evolution of governance and policy tools

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

Deep Charging Evaluation of Satellite Power and Communication System Components

No abstract availabl

Web-based adaptive testing system for C programming

With the ever increasing amount of workload that students have during their college, students requires more and more time to recap, revise and reinforce their knowledge of each subject. However, the current self-practice and self-assessment approaches seldom cater to a student’s learning capability or ability. To cope with this problem, Computerised Adaptive Testing (CAT) and Test Paper Generation (TPG) techniques have been used to eliminate some of these restrictions. In this project, the author focuses into two approaches to improve these two techniques. Currently, these two approaches do not allow a teacher to make detailed requirements for CAT based tests as well as test papers generated by TPG. The modifications proposed attempts to enable these two techniques to be more practical to the student as well as the teachers. Performance evaluation of these two proposed modifications was also completed and shown to be effective. Additionally, a complete web-based adaptive testing system using CAT and TPG techniques as well as the two proposed modifications was developed for the domain of C Programming Language. With the completion of the system, it was deployed for current students taking a related course to evaluate. The user evaluation has shown that the system was very useful to the students who are currently attending the course. The results have also been included in this report. The author hopes that through the use of the web-based adaptive testing system and its related techniques, more students will be able to improve upon with their studies by effectively reinforcing their knowledge of each subject through practices and test of questions that are in line with their ability.Bachelor of Engineering (Computer Science

Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in Mai Po, Hong Kong, China. LiDAR data were employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. A single red-edge VI derived from HSI data delivered the lowest RMSE of 0.12 and the highest R 2 adj of 0.79 for OLe model fitting. The synergetic use of LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R 2 adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. In addition to their penetration attributes, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation. </p

Mapping multi-layered mangroves from multispectral, hyperspectral, and LiDAR data

Understanding species distribution and canopy structure of mangrove forests is imperative for flora and fauna conservation in mangrove habitats. However, most mangrove studies focused on the top canopy layer without exploring the vertical structure of mangroves. This paper presents multi-layered mangrove mapping which considered both overstory and understory detection and species classification using multispectral WorldView-3 (WV-3) data, airborne hyperspectral images (HSI), and LiDAR point cloud. First, LiDAR returns were stratified into the overstory and understory by analyzing the profile of return height, which helped understand the vertical structure of the mangrove stands. Second, three classification algorithms Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were compared by applying WV-3, HSI, LiDAR data, and their combinations to map seven vegetative species. Feature selection was conducted to identify important features and the optimal feature size prior to classification tasks. The measured and estimated understory canopy heights reached a high correlation coefficient of 0.71, which demonstrated the effectiveness of using LiDAR data and the proposed procedure to stratify multi-layered canopies. The combined HSI and LiDAR data produced satisfactory results by the three classifiers with overall accuracy (OA) varying from 0.86 to 0.88. And the species was also accurately mapped by integrating WV-3 and LiDAR data using both RF and SVM algorithms with OA attaining between 0.84 and 0.86. The results of this study highlight that (1) LiDAR data provided superior information to map the vertical structure of multi-layered mangroves, which provided valuable information to classify single-layered and dual-layered Kandelia obovata with understory beneath; (2) the combination of spectral and LiDAR features improved mangrove species classification; (3) and species mapping results derived from combined datasets appeared to be more influential by LiDAR features when using RF and SVM, but spectral features played a more important role in CNN

Combining multi-temporal LiDAR and Sentinel-2 multispectral data for assessment of disturbances and recovery of mangrove forests

Disturbances such as tropical cyclones and insect pests in mangroves can cause defoliation, tree mortality, and other changes in ecosystem processes. Understanding the resistance and resilience of mangroves to disturbance is critical to developing strategies for conservation. However, most studies apply multi-temporal optical data which have limited power to detect structural changes, especially for forests with complex architectures. We combined multispectral Sentinel-2 (S2) images and airborne LiDAR datasets to assemble a comprehensive view of the effects of two disturbance events (a moth pest and a super-typhoon) on mangroves in Mai Po, Hong Kong. A series of normalized difference vegetation index (NDVI) estimates derived from S2 data indicated changes of greenness before and after the moth pest and typhoon events. An object-based stratification method was applied with LiDAR data to separate the overstory and understory to distinguish stratum changes. The results showed that moth larvae were more likely to encroach leafy mangroves of Avicennia marina. Double-layered and single-layered short mangroves have better resistance to typhoons than younger tall mangroves without understory beneath. NDVI recovered rapidly after three to six months post-disturbance but significant changes in canopy structures were found from the LiDAR data. Canopy gaps increased both in size and quantity in mature overstory areas, likely benefitting the growth of the understories beneath. Finally, the understory area grew resulting in a transition from single-layered to double-layered structures. The combination of multi-temporal LiDAR and multispectral data used here highlights the power of complementary remote sensing products in documenting mangrove ecosystem processes

Assessment of active LiDAR data and passive optical imagery for double-layered mangrove leaf area index estimation: a case study in Mai Po, Hong Kong

Remote sensing technology is a timely and cost-efficient method for leaf area index (LAI) estimation, especially for less accessible areas such as mangrove forests. Confounded by the poor penetrability of optical images, most previous studies focused on estimating the LAI of the main canopy, ignoring the impact of the understory. This study investigated the capability of multispectral Sentinel-2 (S2) imagery, airborne hyperspectral imagery (HSI), and airborne LiDAR data for overstory (OLe) and understory (ULe) LAI estimation of a multi-layered mangrove stand in the Mai Po, Hong Kong. The LAI of overstory and understory mangroves was measured using digital hemispherical photography (DHP) in the field to calculate the OLe and ULe. LiDAR data was employed to stratify the overstory and understory. Vegetation indices (VIs) and LiDAR metrics were generated as predictors to build regression models against the OLe and ULe with multiple parametric and non-parametric methods. The OLe model fitting results were typically better than ULe because of the dominant contribution of the overstory to the remotely sensed signal. Single red edge VI derived from HSI data delivered the low RMSE = 0.12 and high R2adj = 0.79 for OLe model fitting. The synergetic use LiDAR metrics and S2 VIs performed best for ULe model fitting with RMSE = 0.33, R2adj = 0.84. OLe estimation benefited from the high spatial and spectral resolution HSI that was found less confounded by the understory. Apart from penetration attribute, LiDAR data could separately describe the upper and lower canopy, which reduced the noise from other components, thereby improving the ULe estimation

Combining multi-temporal airborne LiDAR and Sentinel-2 multispectral data for assessment of disturbances and recovery of mangrove forests

Disturbances such as tropical cyclones and insect pests in mangroves can cause defoliation, tree mortality, and other changes in ecosystem processes. Understanding the resistance and resilience of mangroves to disturbance is critical to developing strategies for conservation. However, most studies apply multi-temporal optical data which have limited power to detect structural changes, especially for forests with complex architectures. We combined multispectral Sentinel-2 (S2) images and airborne LiDAR Scanning (ALS) datasets to assemble a comprehensive view of the effects of two disturbance events (a moth pest and a super-typhoon) on mangroves in Mai Po, Hong Kong. A series of normalized difference vegetation index (NDVI) estimates derived from S2 data indicated changes in greenness before and after the moth pest and typhoon events. An object-based stratification method was applied with ALS data to separate the overstory and understory to distinguish stratum changes. The results showed that moth larvae were more likely to encroach leafy mangroves of Avicennia marina. Double-layered and single-layered short mangroves have better resistance to typhoons than younger tall mangroves without understory beneath. NDVI recovered rapidly after three to six months post-disturbance but significant changes in canopy structures were found from the ALS data. Canopy gaps increased both in size and quantity in mature overstory areas, likely benefitting the growth of the understories beneath. Finally, the understory area grew resulting in a transition from single-layered to double-layered structures. The combination of multi-temporal LiDAR and multispectral data used here highlights the power of complementary remote sensing products in documenting mangrove ecosystem processes