An inland wetlands monitoring framework leveraging remote sensing and reanalysis-based datasets: a case study of 60 inland wetlands in south korea

Abstract

This research proposes a comprehensive and sustainable framework for wetland monitoring by evaluating the wetland environmental superiority index (WESI) of inland wetlands using a long short-term memory (LSTM) model. The WESI estimation method aims to establish a long-term and periodic monitoring system for extensive regions based on remote sensing and reanalysis data. To achieve this, exemplary wetland sites representing high-quality and vulnerable wetlands were selected, with a label of 1 assigned to high-quality wetlands and 0 to vulnerable wetlands. These exemplary wetland sites provide the target variables for training the LSTM-based WESI estimation model, while remote sensing and reanalysis datasets closely associated with the environmental characteristics of inland wetlands are utilized as input variables. In this study, a comprehensive database comprising 13 types of hydrometeorological, vegetation, topographic, and carbon-related remote sensing and reanalysis datasets was established. Additionally, 30 exemplary high-quality and 30 vulnerable inland wetlands—identified based on the field survey conducted by the National Institute of Ecology (NIE) in South Korea—were used to evaluate the applicability of the proposed framework. The WESI estimation method is expected to contribute to the establishment of a long-term, continuous monitoring system for inland wetlands by leveraging the stable data production capabilities of remote sensing and reanalysis data.

 

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (RS-2024-00334564) and in part by Korea Environmental Industry&Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment (MOE). (2022003640001)