Xerra Earth Observation Institute (Alexandra, New Zealand) | Website
Applied Research Topic
Potential Applications ► Water quality, including HAB detection, monitoring and forecasting
DescriptionNew Zealand coastal waters and large lakes are increasingly threatened by harmful algal blooms (HABs). These blooms affect both commercial and recreational activities, in addition to harming ecological function and ecosystem health. Monitoring the temporal and spatial patterns of HABs requires an understanding of dispersion and growth dynamics, and species distribution. The identification of potentially toxic versus non-toxic algae using synoptic radiometric methods, especially in optically complex waters, requires high spectral resolution data. This project aims to combine in situ observations of phytoplankton abundance and pigment analysis with relevant local and remote hyperspectral reflectance measurements to establish and support routine monitoring of HABs, as well as improve alert systems and inform the design of restoration activities.
SignificanceHABs have a negative impact on the health of natural aquatic resources in New Zealand's lakes and coastal waters, which traditional/indigenous, commercial and recreational groups depend on as a source of sustenance and commercial revenue.
Why PACESpace-borne hyperspectral observations are required to effectively monitor large, remote water bodies in and around New Zealand. Currently, there are no operational hyperspectral missions in orbit to provide this kind of data for our regions of interest. PACE's Ocean Color Instrument will provide this type of data and fulfill this need.
End User(s)Environment Canterbury
Bay of Plenty Regional Council
Waikato Regional Council
New Zealand Antarctic Science Platform
SAT Partner(s)Peter Gaube and Alison Chase
Ha, T.N., M. Manley-Harris, D.T. Pham and I. Hawes (2020). A Comparative Assessment of Ensemble-Based Machine Learning and Maximum Likelihood Methods for Mapping Seagrass Using Sentinel-2 Imagery in Tauranga Harbor, New Zealand, Remote Sensing, 12(3), 355.
Jiang, W., Knight, B.R., Cornelisen, C., Barter, P., and Kudela, R. (2017). Simplifying Regional Tuning of MODIS Algorithms for Monitoring Chlorophyll-a in Coastal Waters, Frontiers in Marine Science, 4, 151.
Lehmann, M.K., U. Nguyen, K. Muraoka and M.G. Allan (2019). Regional trends in remotely sensed water clarity over 18 years in the Rotorua Lakes, New Zealand, New Zealand Journal of Marine and Freshwater Research, 53(4), 513-535.
Lehmann, M.K., U. Nguyen, M. Allan and H. van der Woerd (2018). Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types, Remote Sensing 10(8), 1273.
Pahlevan, N., B. Smith, J. Schalles, C. Binding, Z. Cao, R. Ma, K. Alikas, K. Kangro, D. Gurlin, B. Matsushita, W. Moses, S. Greb, M.K. Lehmann, M. Ondrusek, N. Oppelt and R. Stumpf (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach, Remote Sensing of Environment, 240, 111604.