Scaling up benthic primary productivity estimates in a large intertidal estuary using remote sensing.

Autor: Shao Z; School of Science, University of Waikato, Hamilton 3260, New Zealand. Electronic address: zs94@students.waikato.ac.nz., Bryan KR; School of Science, University of Waikato, Hamilton 3260, New Zealand., Lehmann MK; School of Science, University of Waikato, Hamilton 3260, New Zealand; Xerra Earth Observation Institute, Alexandra 9320, New Zealand., Flowers GJL; School of Science, University of Waikato, Hamilton 3260, New Zealand., Pilditch CA; School of Science, University of Waikato, Hamilton 3260, New Zealand.
Jazyk: angličtina
Zdroj: The Science of the total environment [Sci Total Environ] 2024 Jan 01; Vol. 906, pp. 167389. Date of Electronic Publication: 2023 Sep 27.
DOI: 10.1016/j.scitotenv.2023.167389
Abstrakt: As two main primary producers in temperate intertidal regions, seagrass and microphytobenthos (MPB) support estuarine ecosystem functions in multiple ways including stabilizing food webs and regulating sediment resuspension among others. Monitoring estuary productivity at large scales can inform ecosystem scale responses to environmental stressors (climate change, pollution and habitat degradation). Here we use a case study to show how Sentinel-2 data can be used to estimate estuary-wide emerged and submerged gross primary productivity (GPP) on intertidal flats by coupling a new machine learning model to map seagrass and unvegetated habitats with literature-derived photosynthesis-irradiance (P - I) relationships. The model consisted of (1) supervised classification with random forest to delineate seagrass and unvegetated areas and (2) artificial neural network (ANN) regression to predict % seagrass coverage. Our seagrass delineation by supervised classification had an overall accuracy of 0.96, while the ANN regression on seagrass coverage provided high predictive accuracy (R 2  = 0.71 and RMSE = 0.11). The estimated GPP showed seagrass contributed slightly more to intertidal benthic productivity than MPB in the case-study estuary over the 3-year study period. This model can be used to predict the response of seagrass and MPB GPP to sea level rise, which shows that the future state may be very sensitive to increased turbidity. For example, by the year 2100, the model shows a sharp decline in productivity with sea level rise, assuming current turbidity trends, (loss of up to 52-53 % for seagrass and 23-45 % for MPB, a function of whether shoreward migration of seagrass is incorporated). However, GPP under conditions of unchanging turbidity (and no seagrass migration), exhibits minimal negative impact of sea level rise (loss of 3 % for seagrass and increase of 29 % for MPB). Therefore, controlling water turbidity might be an efficient solution to maintaining the current GPP as sea level rises.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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