Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Abstract

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988–2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.