Terrestrial Ecosystem Research Network New South Wales (TERN NSW)

Project Member(s): Huete, A., Eamus, D.

Funding or Partner Organisation: Investment NSW (NSW RESEARCH ATTRACTION AND ACCELERATION PROGRAM)

Start year: 2016

Summary: The TERN NSW team will work together on developing a ‘NSW Ecosystem Change’ report (eBook style) which will focus on how Government (and Industry) can use the data and infrastructure made available through TERN to understand “Environmental Change” and potentially other grand challenges. It is important to note that this report is not intended to be a replacement for the “State of the Environment” reporting, rather a demonstration of how TERN’s approach to research infrastructure could lead to new innovations in this process, from our unique perspective.

Publications:

Song, G, Wang, J, Liddell, M, Morellato, P, Lee, CKF, Yang, D, Alberton, B, Detto, M, Ma, X, Zhao, Y, Yeung, HCH, Zhang, H, Ng, M, Nelson, BW, Huete, A & Wu, J 2023, 'Tropical leaf phenology characterization by using an ecologically-constrained deep learning model with PlanetScope satellites'.
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Tarin, T, Nolan, RH, Medlyn, BE, Cleverly, J & Eamus, D 2020, 'Water‐use efficiency in a semi‐arid woodland with high rainfall variability', Global Change Biology, vol. 26, no. 2, pp. 496-508.
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Barraza, V, Grings, F, Franco, M, Douna, V, Entekhabi, D, Restrepo-Coupe, N, Huete, A, Gassmann, M & Roitberg, E 2019, 'Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia', Agricultural and Forest Meteorology, vol. 268, pp. 341-353.
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Watson, CJ, Restrepo-Coupe, N & Huete, AR 2019, 'Multi-Scale Phenology of Temperate Grasslands: Improving Monitoring and Management With Near-Surface Phenocams', Frontiers in Environmental Science, vol. 7, no. FEB.
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Thenkabail, PS, Lyon, JG & Huete, A 2018, 'Fifty Years of Advances in Hyperspectral Remote Sensing of Agriculture and Vegetation—Summary, Insights, and Highlights of Volume II: Hyperspectral Indices and Image Classifications for Agriculture and Vegetation' in Hyperspectral Indices and Image Classifications for Agriculture and Vegetation, pp. 251-286.

Lin, S, Li, J, Liu, Q, Huete, A & Li, L 2018, 'Effects of Forest Canopy Vertical Stratification on the Estimation of Gross Primary Production by Remote Sensing', Remote Sensing, vol. 10, no. 9, pp. 1329-1329.
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Teluguntla, P, Thenkabail, PS, Oliphant, A, Xiong, J, Gumma, MK, Congalton, RG, Yadav, K & Huete, A 2018, 'A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 144, pp. 325-340.
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Thenkabail, PS, Lyon, JG & Huete, A 2018, 'Editors', Hyperspectral Indices and Image Classifications for Agriculture and Vegetation, pp. xxv-xxviii.

Li, L, Wang, Y-P, Beringer, J, Shi, H, Cleverly, J, Cheng, L, Eamus, D, Huete, A, Hutley, L, Lu, X, Piao, S, Zhang, L, Zhang, Y & Yu, Q 2017, 'Responses of LAI to rainfall explain contrasting sensitivities to carbon uptake between forest and non-forest ecosystems in Australia', Scientific Reports, vol. 7, no. 1.
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Keywords: TERN, Remote Sensing, Phenology, NSW, GDE, Environment

FOR Codes: Environmental Monitoring, Natural Resource Management, Ecosystem Assessment and Management at Regional or Larger Scales, Ecosystem Assessment and Management of Forest and Woodlands Environments, Pollution and contamination, Assessment and management of Antarctic and Southern Ocean ecosystems, Assessment and management of freshwater ecosystems