I am a researcher in global environmental change with expertise in land cover land use change and forest ecology. I have 6+ years experience working with big environmental datasets including remote sensing (urban and forestry LiDAR, optical satellite imagery) and atmospheric measurements (eddy covariance). I employ field sampling, remote sensing data, and spatial and statistical analysis (ArcGIS, R and Python) in my methodologies. I am particularly skilled in data processing design and optimization, management and integration of spatial datasets, as well as regression, cluster and time series analysis (detection of trends and anomalies).
land use land cover change; remote sensing; reproductive phenology; air quality; tropical ecology
Nguyen, HT, Hutyra, LR, Hardiman, BS & Raciti, SM 2016, 'Characterizing forest structure variations across an intact tropical peat dome using field samplings and airborne LiDAR', Ecological Applications, vol. 26, no. 2, pp. 587-601.View/Download from: Publisher's site
© 2016 by the Ecological Society of America. 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.
Ng, BJL, Hutyra, LR, Nguyen, H, Cobb, AR, Kai, FM, Harvey, C & Gandois, L 2015, 'Carbon fluxes from an urban tropical grassland', Environmental Pollution, vol. 203, pp. 227-234.View/Download from: Publisher's site
© 2014 Elsevier Ltd. All rights reserved. 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.
Nguyen, HT & Pearce, JM 2013, 'Automated quantification of solar photovoltaic potential in cities: Overview: A new method to determine a city's solar electric potential by analysis of a distribution feeder given the solar exposure and orientation of rooftops', International Review for Spatial Planning and Sustainable Development, vol. 1, no. 1, pp. 49-60.View/Download from: Publisher's site
© SPSD Press from 2010. Solar photovoltaic (PV) energy conversion offers a sustainable method of producing electricity to maintain and improve the standard of living within cities. Planning for large-scale adoption of PV in cities, however, provides a challenge to urban planners because of the distributed nature of PV. This paper develops a new methodology to determine a city's PV potential by analysis of solar PV generation potential by distribution feeder given the solar exposure and orientation of rooftops serviced by a specific feeder within the city. The methodology is applied to an example feeder, and then can be scaled to apply to the network of any city. The method comprises the following steps: (i) rooftop extraction from aerial photos; (ii) service parcel and territory matching based on geographical information system (GIS) data; (iii) simulation of the solar exposure of the customers connected to distribution feeders based on local meteorological conditions and the general roof orientation of the customers serviced by the feeder; and (iv) sensitivity analyses of electricity yield as a function of PV module efficiency. Experience from the case study such as trade-offs between time consumption and data quality is discussed to highlight a need for connectivity between demographic information, electrical engineering schematics and GIS. Finally conclusions are developed to provide final methodology with the most and useful information from the highest constrained sources and can be adapted anywhere in the world.
Nguyen, HT & Pearce, JM 2012, 'Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale', Solar Energy, vol. 86, no. 5, pp. 1245-1260.View/Download from: Publisher's site
Recently several algorithms have been developed to calculate the solar photovoltaic (PV) potential on the basis of 2.5D raster data that can capture urban morphology. This study provides a new algorithm that (i) incorporates both terrain and near surface shadowing effects on the beam component; (ii) scales down the diffuse components of global irradiation; and (iii) utilizes free and open source GRASS and the module r. sun in modeling irradiation. This algorithm is semi-automatic and easy to upgrade or correct (no hand drawn areas), open source, detailed and provides rules of thumb for PV system design at the municipal level. The workflow is pilot tested on LiDAR data for 100 buildings in downtown Kingston, Ontario. Shading behavior was considered and suitable roof sections for solar PV installations selected using a multi-criteria objective. At sub-meter resolution and small time steps the effect of occlusion from near object was determined. Annual daily horizontal irradiation values were refined at 0.55. m resolution and were shown to be lower than those obtained at 90. m by 30%. The robustness of r. sun as capable of working with different levels of surface complexity has been confirmed. Finally, the trade off of each computation option (spatial resolution, time step and shading effect) has been quantified at the meso scale, to assist planners in developing the appropriate computation protocols for their regions. © 2012 Elsevier Ltd.
Nguyen, HT, Pearce, JM, Harrap, R & Barber, G 2012, 'The application of LiDAR to assessment of rooftop solar photovoltaic deployment potential in a municipal district unit', Sensors, vol. 12, no. 4, pp. 4534-4558.View/Download from: Publisher's site
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. © 2012 by the authors; licensee MDPI, Basel, Switzerland.
Nguyen, HT & Pearce, JM 2010, 'Estimating potential photovoltaic yield with r.sun and the open source Geographical Resources Analysis Support System', Solar Energy, vol. 84, no. 5, pp. 831-843.View/Download from: Publisher's site
The package r.sun within the open source Geographical Resources Analysis Support System (GRASS) can be used to compute insolation including temporal and spatial variation of albedo and solar photovoltaic yield. A complete algorithm is presented covering the steps of data acquisition and preprocessing to post-simulation whereby candidate lands for incoming solar farms projects are identified. The optimal resolution to acquire reliable solar energy outputs to be integrated into PV system design software was determined to be 1 square km. A case study using the algorithm developed here was performed on a North American region encompassing fourteen counties in South-eastern Ontario. It was confirmed for the case study that Ontario has a large potential for solar electricity. This region is found to possess over 935,000 acres appropriate for solar farm development, which could provide 90 GW of PV. This is nearly 60% of Ontario's projected peak electricity demand in 2025. The algorithm developed and tested in this paper can be generalized to any region in the world in order to foster the most environmentally-responsible development of large-scale solar farms. © 2010 Elsevier Ltd. All rights reserved.
Wiginton, LK, Nguyen, HT & Pearce, JM 2010, 'Quantifying rooftop solar photovoltaic potential for regional renewable energy policy', Computers, Environment and Urban Systems, vol. 34, no. 4, pp. 345-357.View/Download from: Publisher's site
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 demonstrates techniques to merge the capabilities of geographic information systems and object-specific image recognition to determine the available rooftop area for PV deployment in an example large-scale region in south eastern Ontario. A 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. Limitations faced in terms of the capabilities of the software and determining the appropriate fraction of roof area available are discussed. Because this aspect of the analysis uses an integral approach, PV potential will not be georeferenced, but rather presented as an agglomerate value for use in regional policy making. A relationship across the region was found between total roof area and population of 70.0m2/capita±6.2%. With appropriate roof tops covered with commercial solar cells, the potential PV peak power output from the region considered is 5.74GW (157% of the region's peak power demands) and the potential annual energy production is 6909GWh (5% of Ontario's total annual demand). This suggests that 30% of Ontario's energy demand can be met with province-wide rooftop PV deployment. This new understanding of roof area distribution and potential PV outputs will guide energy policy formulation in Ontario and will inform future research in solar PV deployment and its geographical potential. © 2010 Elsevier Ltd.
Nguyen, HT & Pearce, JM 2011, 'Community-Scale Wind-Powered Desalination for Selected Coastal Mekong Provinces in Vietnam' in Advances in Global Change Research, pp. 371-398.View/Download from: Publisher's site
© 2011, Springer Science+Business Media B.V. 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.
Huete, A, Tran, NN, Nguyen, H, Xie, Q & Katelaris, C 2019, 'Forecasting Pollen Aerobiology with Modis EVI, Land Cover, and Phenology Using Machine Learning Tools', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE International Geoscience and Remote Sensing Symposium, IEEE, Yokohama, Japan, pp. 5429-5432.View/Download from: Publisher's site
Grass pollens are a major source of aeroallergens globally, inducing allergic asthma and hay fever in up to 500 million people worldwide. Pollen forecasting research and methods are site-dependent and tend to be empirically derived composites of expert knowledge and weather data. In this study we utilize satellite-based information of landscape conditions and phenology to better discern and predict grass pollen evolution. We employed machine learning approaches to formulate and better understand relationships between landscape phenology and seasonal flowering-induced pollen concentrations. We show that machine learning approaches significantly improved pollen prediction capabilities and provided key information to better attribute changes in pollen counts driven by shifting ecological landscapes from climate change drivers.
Nguyen, HT & Pearce, JM 2010, 'Renewable powered desalination in the coastal Mekong Delta', ASME 2010 4th International Conference on Energy Sustainability, ES 2010, pp. 935-946.View/Download from: Publisher's site
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 km 2 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. © 2010 by ASME.