A Random Forest-Based Multi-Index Classification (RaFMIC) Approach to Mapping Three-Decadal Inundation Dynamics in Dryland Wetlands Using Google Earth Engine

Abstract

Australian inland riparian wetlands located east of the Great Dividing Range exhibit unique, hydroecological characteristics. These flood-dependent aquatic systems located in water-limited regions are declining rapidly due to the competitive demand for water for human activities, as well as climate change and variability. However, there exist very few reliable data to characterize inundation change conditions and quantify the impacts of the loss and deterioration of wetlands. A long-term time record of wetland inundation maps can provide a crucial baseline to monitor, assess, and assist the management and conservation of wetland ecosystems. This study presents a random forest-based multi-index classification algorithm (RaFMIC) on the Google Earth Engine (GEE) platform to efficiently construct a temporally dense, three-decadal time record of inundation maps of the southeast Australian riparian inland wetlands. The method was tested over the Macquarie Marshes located in the semiarid region of NSW, Australia. The results showed a good accuracy when compared against high-spatial resolution imagery. The total inundated area was consistent with precipitation and streamflow patterns, and the temporal dynamics of vegetation showed good agreement with the inundation maps. The inundation time record was analysed to generate inundation probability maps, which were in a good agreement with frequently flooded areas simulated by a hydrodynamic model and the distribution of flood-dependent vegetation species. The long-term, time-dense inundation maps derived from the RaFMIC method can provide key information to assess the condition and health of wetland ecosystems and have the potential to improve wetland inventory with spatially explicit water regime information. RaFMIC can be adapted over other dryland wetlands, as an effective semiautomated method of mapping long-term inundation dynamics.

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“…The identification of major vulnerability drivers highlights priority areas for policy interventions to balance socioeconomic needs and wetland conservation [80,81]. The future projections also underline the urgency for strategic resource allocation matching the locations likely to undergo maximum change [56,58]. Mainstreaming wetland protection considerations into regional planning while adopting localized adaptive measures tailored to zone-specific vulnerabilities can offer potential pathways [83].…”

“…This helps evaluate the models' performance and ensures their robustness. Key parameters will be optimized to enhance model accuracy and predictive capabilities [56].…”

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