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利用遥感技术估算大型潮间带河口的底栖初级生产力。

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

机构信息

School of Science, University of Waikato, Hamilton 3260, New Zealand.

School of Science, University of Waikato, Hamilton 3260, New Zealand.

出版信息

Sci Total Environ. 2024 Jan 1;906:167389. doi: 10.1016/j.scitotenv.2023.167389. Epub 2023 Sep 27.

Abstract

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 = 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.

摘要

作为温带潮间带的两个主要初级生产者,海草和微型底栖生物(MPB)通过多种方式支持河口生态系统功能,包括稳定食物网和调节沉积物再悬浮等。在大范围内监测河口生产力可以为生态系统对环境胁迫(气候变化、污染和生境退化)的响应提供信息。在这里,我们使用一个案例研究来展示如何使用 Sentinel-2 数据通过将新的机器学习模型与基于文献的光合作用-辐射(P - I)关系相结合,来估算潮间带平原上整个河口的露出和淹没总初级生产力(GPP),从而估算整个河口的露出和淹没总初级生产力(GPP)。该模型由(1)随机森林的监督分类来划定海草和无植被区域,以及(2)人工神经网络(ANN)回归来预测海草覆盖率。我们的监督分类海草划定总体准确率为 0.96,而海草覆盖率的 ANN 回归提供了很高的预测精度(R = 0.71 和 RMSE = 0.11)。在 3 年的研究期间,估计的 GPP 显示海草对案例研究河口的潮间带底栖生产力的贡献略高于 MPB。该模型可用于预测海草和 MPB GPP 对海平面上升的响应,这表明未来的状态可能对浊度增加非常敏感。例如,到 2100 年,假设目前的浊度趋势,模型显示生产力随着海平面上升而急剧下降(海草损失高达 52-53%,MPB 损失 23-45%,这取决于海草是否向岸迁移)。然而,在浊度不变(且无海草迁移)的情况下,GPP 受海平面上升的负面影响最小(海草损失 3%,MPB 增加 29%)。因此,控制水浊度可能是维持当前 GPP 随着海平面上升的有效解决方案。

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