Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Upscaling effects on infectious disease emergence risk emphasize the need for local planning in primary prevention within biodiversity hotspots
    (Springer Nature Limited, 2025-10-27) Muylaert RL; Wilkinson DA; Dwiyanti EI; Hayman DTS
    Zoonotic risk assessments are increasingly vital in the wake of recent epidemics. The microbial diversity of parasitic organisms correlates with host species richness, with regions of high biodiversity facing elevated risks of emerging zoonotic infections. While habitat loss and fragmentation reduce species diversity, anthropogenic encroachment, particularly in forested areas, amplifies human exposure to novel pathogens. This study integrates host habitat, biodiversity, human encroachment, and population at risk to estimate novel disease emergence and epidemic risk at multiple spatial scales. Using Java, Indonesia, as a case study, we demonstrate that degrading spatial resolution leads to information loss, with optimal resolutions typically below 2000 m, ideally around 500 m when native-resolution processing is unfeasible. Gravity models of epidemic spread highlight Jakarta and West Java as high-risk areas, with varying contributions from surrounding regions. Our spatial analysis underscores the influence of population centers on forest management and agroforestry practices. These findings offer valuable insights for guiding pandemic prevention research and improving pathogen- and driver-based risk monitoring strategies.
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    Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam
    (National Authority for Elsevier B V on behalf of Remote Sensing and Space Sciences, 2020-04-01) Ha TV; Tuohy M; Irwin M; Tuan PV
    Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning.