Abstract
Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.
Highlights
Biological invasions are responsible for some of the most devastating impacts on the world’s ecosystems
We have introduced a hierarchical classification approach that enables the near-instantaneous monitoring of water and aquatic vegetation on a frequent basis
Whilst we have shown encouraging results for the MultiResUnet-based detection of aquatic vegetation within a single heterogenous study area, the computational infrastructure required for the operational convolutional neural network (CNN)-based mapping of aquatic vegetation across large extents remains a challenge
Summary
Biological invasions are responsible for some of the most devastating impacts on the world’s ecosystems. Freshwater ecosystems are among the worst affected, with invasive alien aquatic plants (IAAPs) posing serious threats, to freshwater biodiversity [1], and to the important ecosystem services it provides [2]. Once an IAAP species has established in a landscape, it can be difficult if not impossible, to stop or slow down the invasion [3]. Regular monitoring of IAAP species is needed to promote targeted, feasible, and effective management [4,5,6,7]. There is an urgent need for techniques that enable consistent, frequent, and accurate monitoring of IAAP species [8,9]. Semi-automated satellite image analysis techniques using open-access satellite imagery offer an ideal basis for an IAAP monitoring programme. Recent advances in the capture and processing of Earth Observation (EO) data have made these monitoring requirements more achievable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
