An automatic classification algorithm for submerged aquatic vegetation in shallow lakes using Landsat imagery

Research output: Contribution to journalArticle

Abstract

Submerged aquatic vegetation (SAV) is one of the main producers in inland lakes. Tracking the temporal and spatial changes in SAV is crucial for the identification of state changes in lacustrine ecosystems, such as changes in light, nutrients, and temperature. However, the available SAV classification algorithms based on remote sensing are highly dependent on field survey data and/or human interventions, prohibiting the extraction of large-scale and/or long-term patterns. Here, we developed an automatic SAV classification algorithm using Landsat imagery, where the thresholds of two key parameters (the floating algae index (FAI) and reflectance in the shortwave-infrared (SWIR) band) are automatically determined. The algorithm was applied to eight Landsat images of four Yangtze Plain lakes and obtained a mean producer accuracy of 82.9% when gauged against field-surveyed datasets. The algorithm was further employed to obtain long-term SAV areal data from Changdang Lake on the Yangtze Plain from 1984 to 2018, and the result was highly consistent with lake transparency data. Numerical simulations indicated that our developed algorithm is insensitive to the Chl-a concentration of the water column. Yet, it has a detection limit of ~0.35 m below the water surface, and such a limit changes with different fractions of vegetation coverage within a pixel. The automatic classification algorithm proposed in this study has the potential to obtain the temporal and spatial distribution patterns of SAV in other shallow lakes where SAV grows in lakes sharing similar hydrological characteristics as the lakes in the Yangtze Plain.

Details

Authors
Organisations
External organisations
  • Southern University of Science and Technology
  • University of Copenhagen
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Ecology

Keywords

  • Aquatic vegetation, SAV, Remote sensing, FAI, SWIR, Classification, Dynamic threshold, Landsat
Original languageEnglish
Article number112459
JournalRemote Sensing of Environment
Volume260
Publication statusPublished - 2021 Jul
Publication categoryResearch
Peer-reviewedYes