A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

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.

Assessing the potential of Sentinel-2 data for tracking invasive water hyacinth in a river branch

“…53,62,63 Moreover, the frequent revisiting rates of satellite sensors provides long-term records that are useful to support decisions on the application of control measures, since the observations make it possible to assess the effectiveness of those measures. 64,65 This information is valuable for governmental and environmental management agencies and decision makers, as well as for local communities.…”

“…21,27,59,63,[71][72][73] Particularly, multitemporal data allow that the normalized difference vegetation index (NDVI), which positively correlates with plants' health or vigor, i.e., plants exhibiting concentrated green pigments or active photosynthetic rates, is used to monitor infestations of water hyacinth; its signal relates to a prominent level of reflectance in the near-infrared (NIR) spectral band. 64,65 3 Materials and Methods…”

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

“…Here, the Canny Edge Filter was applied to the boundary between algae and seawater to improve the extraction accuracy of micro-U. prolifera scattered in the sea [50]. It should be noted that the distribution of the histogram appeared at the junction of the seawater and U. prolifera pixels.…”

Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing

“…Sentinel-2 has been used to distinguish between multiple emergent aquatic vegetation species (e.g., Spartina alterniflora, Phragmites australis) but the potential to spectrally discriminate between different types of FAV has yet to be determined [21,22]. Although Sentinel-2 has previously been used to map invasive FAV, such as water hyacinth, most study sites in the literature focus on relatively homogenous environments and community types, or target only one invasive species [23][24][25]. These prior studies also do not demonstrate the extent of Sentinel-2's utility across a range of environments, nor do they differentiate floating vegetation at the genus or species-level.…”