Ha T. Nguyen

Quantifying Rooftop Solar Photovoltaic Potential for Regional Renewable Energy Policy

Solar photovoltaic (PV) technology has matured to become a technically viable large-scale source of sustainable energy. Understanding the rooftop PV potential is critical for utility planning, accommodating grid capacity, deploying financing schemes and formulating future adaptive energy policies. This paper merges the capabilities of geographic information systems and object-based image recognition to determine the available rooftop area for PV deployment in an example large-scale region in south eastern Ontario. An innovative five-step procedure has been developed for estimating total rooftop PV potential which involves geographical division of the region; sampling using the Feature Analyst extraction software; extrapolation using roof area-population relationships; reduction for shading, other uses and orientation; and conversion to power and energy outputs. A relationship across the region was found between roof area and population of 70.0 m2/capita ± 6.2%. For this region with appropriate roof tops covered with commercial solar cells the potential PV peak power output is 5.74 GW (157% of the region’s peak power demands) and the potential annual energy production is 6909 Gwh (5% of Ontario’s total annual demand). This suggests that 30% of Ontario’s demand can be met with province-wide rooftop PV deployment. This new understanding of roof area distribution and potential PV outputs has an immense significance to energy policy formulation in Ontario and the methodology developed here is transferable in other regions to assist in solar PV deployment.

The Application of LiDAR to Assessment of Rooftop Solar Photovoltaic Deployment Potential in a Municipal District Unit

A methodology is provided for the application of Light Detection and Ranging (LiDAR) to automated solar photovoltaic (PV) deployment analysis on the regional scale. Challenges in urban information extraction and management for solar PV deployment assessment are determined and quantitative solutions are offered. This paper provides the following contributions: (i) a methodology that is consistent with recommendations from existing literature advocating the integration of cross-disciplinary competences in remote sensing (RS), GIS, computer vision and urban environmental studies; (ii) a robust methodology that can work with low-resolution, incomprehensive data and reconstruct vegetation and building separately, but concurrently; (iii) recommendations for future generation of software. A case study is presented as an example of the methodology. Experience from the case study such as the trade-off between time consumption and data quality are discussed to highlight a need for connectivity between demographic information, electrical engineering schemes and GIS and a typical factor of solar useful roofs extracted per method. Finally, conclusions are developed to provide a final methodology to extract the most useful information from the lowest resolution and least comprehensive data to provide solar electric assessments over large areas, which can be adapted anywhere in the world.

Carbon fluxes from an urban tropical grassland.

Turfgrass covers a large fraction of the urbanized landscape, but the carbon exchange of urban lawns is poorly understood. We used eddy covariance and flux chambers in a grassland field manipulative experiment to quantify the carbon mass balance in a Singapore tropical turfgrass. We also assessed how management and variations in environmental factors influenced CO2 respiration. Standing aboveground turfgrass biomass was 80 gC m(-2), with a mean ecosystem respiration of 7.9 ± 1.1 μmol m(-2) s(-1). The contribution of autotrophic respiration was 49-76% of total ecosystem respiration. Both chamber and eddy covariance measurements suggest the system was in approximate carbon balance. While we did not observe a significant relationship between the respiration rates and soil temperature or moisture, daytime fluxes increased during the rainy interval, indicating strong overall moisture sensitivity. Turfgrass biomass is small, but given its abundance across the urban landscape, it significantly influences diurnal CO2 concentrations.

Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR

Tropical peat swamp forests (PSF) are one of the most carbon dense ecosystems on the globe and are experiencing substantial natural and anthropogenic disturbances. In this study, we combined direct field sampling and airborne LiDAR to empirically quantify forest structure and aboveground live biomass (AGB) across a large, intact tropical peat dome in Northwestern Borneo. Moving up a 4 m elevational gradient, we observed increasing stem density but decreasing canopy height, crown area, and crown roughness. These findings were consistent with hypotheses that nutrient and hydrological dynamics co-influence forest structure and stature of the canopy individuals, leading to reduced productivity towards the dome interior. Gap frequency as a function of gap size followed a power law distribution with a shape factor (λ) of 1.76 ± 0.06. Ground-based and dome-wide estimates of AGB were 217.7 ± 28.3 Mg C/ha and 222.4 ± 24.4 Mg C/ha, respectively, which were higher than previously reported AGB for PSF and tropical forests in general. However, dome-wide AGB estimates were based on height statistics, and we found the coefficient of variation on canopy height was only 0.08, three times less than stem diameter measurements, suggesting LiDAR height metrics may not be a robust predictor of AGB in tall tropical forests with dense canopies. Our structural characterization of this ecosystem advances the understanding of the ecology of intact tropical peat domes and factors that influence biomass density and landscape-scale spatial variation. This ecological understanding is essential to improve estimates of forest carbon density and its spatial distribution in PSF and to effectively model the effects of disturbance and deforestation in these carbon dense ecosystems.

Community-Scale Wind-Powered Desalination for Selected Coastal Mekong Provinces in Vietnam

Global climate destabilization is exacerbating water problems in Vietnam, most acutely in the South and Central regions where the majority of the inhabited area lies in the low elevation coastal zone. Off-grid community-scale reverse osmosis desalination powered by small wind turbines offers a solution to this problem for the coastal fringe of Vietnam’s Mekong Delta. Using a geographical information system (GIS) platform, a wind resources atlas developed by the Asia Sustainable and Alternative Energy, and projected rural population available from Columbia University’s Center for International Earth Science Information Network, this chapter explores this potential. The GIS analysis estimated that in the absence of all other water supply facilities, off-grid wind desalination could provide clean water to 5.4 million rural residents living in 18,900 km2 of the Mekong Delta coastal provinces at the rate of 60 l/person/day. In addition to providing clean water, the use of wind-powered desalination in the region would have educational benefits to combat poverty and unemployment and ease water-related conflicts, and it has been shown to improve environmental and agricultural sustainability. Thus this technology was found to represent a decentralized and community-based method to adapt to and mitigate climate change in the coastal fringe of the Mekong Delta.

Renewable Powered Desalination in the Coastal Mekong Delta

Global climate destabilization is exacerbating water problems in Vietnam, most acutely in the South and Central regions where most of the inhabited area lies in the low elevation coastal zone. Using a geographical information system (GIS) platform, a wind resources atlas developed by the Asia Sustainable and Alternative Energy Program and the projected rural population available from Columbia University’s Center for International Earth Science Information Network, this paper explores the potential for off-grid medium to small-scale reverse osmosis desalination powered by small wind turbines for the coastal fringe of Vietnam’s Mekong Delta. The analysis estimated that in the absence of all other water supply facilities, off-grid wind desalination could provide clean water to 5.4 million rural residents living in 18.9 thousand km2 of the Mekong Delta coastal provinces at the rate of 60 liters per capita per day. In addition to providing clean water, the use of wind powered desalination in the region would have educational benefits, combat poverty and unemployment, ease water-related conflicts, and has been shown to be improve environmental and agricultural sustainability. Thus this technology was found to represent a decentralized and community-based method to adapt to and mitigate climate change in the coastal fringe of the Mekong Delta.Copyright