Monitoring the Spatiotemporal Distribution of Invasive Aquatic Plants in the Guadiana River, Spain

Abstract

Monitoring the spatio-temporal distribution of invasive aquatic plants is a challenge in many regions worldwide. One of the most invasive species on earth is the water hyacinth. These plants are harmful to biodiversity and create negative impacts on society and economy. The Guadiana river (one of the most important ones in Spain) has suffered from this problem since the early 2000s. Several efforts have been made to mitigate it. However, invasive plants such as the water hyacinth are still present in seed banks at the bottom of the river and can germinate even more than a decade after. In this paper, we propose an automatic, based on remote sensing and deep learning techniques, to monitor the water hyacinth in the Guadiana river. Specifically, a multitemporal analysis was carried out during two years using images collected by ESA's Sentinel-2 satellite, analyzed with a convolutional neural network (CNN). We demonstrate that, with our strategy, the river can be monitored every few days and we are able to automatically detect the water hyacinth. Three experiments have been carried out to predict the presence of water hyacinth from a few scattered training samples, which represent invasive plants in different phenological stages and with different spectral responses.

Hydrocharis laevigata in Europe

“…Such is, above all, the result of eutrophication, but other causal factors include changes to hydrological regimes (i.e., reduced levels of water flow), decreasing degrees of water transparency, and global warming, which are simultaneously transforming natural aquatic environments [ 5 , 48 , 49 , 50 , 51 , 52 , 53 ]. These ecological shifts are harmful to native aquatic plant populations while promoting the establishment of invasive species such as H. laevigata , leading to profound transformations in European ecosystems [ 12 , 14 , 54 , 55 , 56 , 57 ].…”

A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor

“…Point cloud feature identification refers to the process of matching unknown features with known ones to classify features. In recent years, there has been a growing number of automatic classification methods that use deep learning for spatiotemporal data, such as remote sensing images, laser point clouds, SAR, and others [29][30][31][32][33][34]. These methods are mainly based on the supervised classification of statistical learning data, which requires learning sample data in advance to determine model parameters and then using the obtained model to classify sub-data.…”

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

“…In a subsequent work [33], we used CNNs as a baseline to monitor the spatio-temporal distribution of water hyacinth using sparse training samples collected from only four images (out of a total of 62 images available in the analyzed two-year time series) independently of the phenological stage. To study the dynamics of the spread of invasive plants over a two-year period, a methodology for mapping the most frequent areas of water hyacinth accumulation was developed.…”

“…1) CNN architecture: For aquatic weeds detection on preprocessed Sentinel-2 images, a CNN model was developed and trained with all Sentinel-2 spectral bands in [23], outperforming other traditional machine learning methods. The same CNN model was also used in our previous work [33], in which the training set was composed by samples collected from different Sentinel-2 images, acquired on different dates. In those works, the CNN model allowed us to detect water hyacinth at different phenological stages and also to analyze the spatio-temporal dynamics in a time series, determining the areas where the invasive plants were most frequently accumulated in the period analyzed.…”