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Chinh C. Tran
Department of Natural Resources and Environmental Management, University of Hawaii, 1901 East-West Road, Honolulu, HI 96822, USA

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Journal article
Published: 12 February 2014 in Land
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The urban transition that has emerged over the past quarter century poses new challenges for mapping land cover/land use change (LCLUC). The growing archives of imagery from various earth-observing satellites have stimulated the development of innovative methods for change detection in long-term time series. We tested two different multi-temporal remote sensing datasets and techniques for mapping the urban transition. Using the Red River Delta of Vietnam as a case study, we compared supervised classification of dense time stacks of Landsat data with trend analyses of an annual series of night-time lights (NTL) data from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS). The results of each method were corroborated through qualitative and quantitative GIS analyses. We found that these two approaches can be used synergistically, combining the advantages of each to provide a fuller understanding of the urban transition at different spatial scales.

ACS Style

Miguel Castrence; Duong H. Nong; Chinh C. Tran; Luisa Young; Jefferson Fox. Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam. Land 2014, 3, 148 -166.

AMA Style

Miguel Castrence, Duong H. Nong, Chinh C. Tran, Luisa Young, Jefferson Fox. Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam. Land. 2014; 3 (1):148-166.

Chicago/Turabian Style

Miguel Castrence; Duong H. Nong; Chinh C. Tran; Luisa Young; Jefferson Fox. 2014. "Mapping Urban Transitions Using Multi-Temporal Landsat and DMSP-OLS Night-Time Lights Imagery of the Red River Delta in Vietnam." Land 3, no. 1: 148-166.

Journal article
Published: 28 November 2013 in ISPRS International Journal of Geo-Information
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Highly Pathogenic Avian Influenza (HPAI) subtype H5N1 poses severe threats to both animals and humans. Investigating where, when and why the disease occurs is important to help animal health authorities develop effective control policies. This study takes into account spatial and temporal occurrence of HPAI H5N1 in the Red River Delta of Vietnam. A two-stage procedure was used: (1) logistic regression modeling to identify and quantify factors influencing the occurrence of HPAI H5N1; and (2) a geostatistical approach to develop monthly predictive maps. The results demonstrated that higher average monthly temperatures and poultry density in combination with lower average monthly precipitation, humidity in low elevation areas, roughly from November to January and April to June, contribute to the higher occurrence of HPAI H5N1. Provinces near the Gulf of Tonkin, including Hai Phong, Hai Duong, Thai Binh, Nam Dinh and Ninh Binh are areas with higher probability of occurrence of HPAI H5N1.

ACS Style

Chinh C. Tran; Russell S. Yost; John F. Yanagida; Sumeet Saksena; Jefferson Fox; Nargis Sultana. Spatio-Temporal Occurrence Modeling of Highly Pathogenic Avian Influenza Subtype H5N1: A Case Study in the Red River Delta, Vietnam. ISPRS International Journal of Geo-Information 2013, 2, 1106 -1121.

AMA Style

Chinh C. Tran, Russell S. Yost, John F. Yanagida, Sumeet Saksena, Jefferson Fox, Nargis Sultana. Spatio-Temporal Occurrence Modeling of Highly Pathogenic Avian Influenza Subtype H5N1: A Case Study in the Red River Delta, Vietnam. ISPRS International Journal of Geo-Information. 2013; 2 (4):1106-1121.

Chicago/Turabian Style

Chinh C. Tran; Russell S. Yost; John F. Yanagida; Sumeet Saksena; Jefferson Fox; Nargis Sultana. 2013. "Spatio-Temporal Occurrence Modeling of Highly Pathogenic Avian Influenza Subtype H5N1: A Case Study in the Red River Delta, Vietnam." ISPRS International Journal of Geo-Information 2, no. 4: 1106-1121.