Abstract

Abstract. The management of riverine areas is fundamental due to their great environmental importance. The fast changes that occur in these areas due to river mechanics and human pressure makes it necessary to obtain data with high temporal and spatial resolution. This study proposes a workflow to map riverine species using Unmanned Aerial Vehicle (UAV) imagery. Based on RGB point clouds, our work derived simple geometric and spectral metrics to classify an area of the public hydraulic domain of the river Palancia (Spain) in five different classes: Tamarix gallica L. (French tamarisk), Pinus halepensis Miller (Aleppo pine), Arundo donax L. (giant reed), other riverine species and ground. A total of six Machine Learning (ML) methods were evaluated: Decision Trees, Extra Trees, Multilayer Perceptron, K-Nearest Neighbors, Random Forest and Ridge. The method chosen to carry out the classification was Random Forest, which obtained a mean score cross-validation close to 0.8. Subsequently, an object-based reclassification was done to improve this result, obtaining an overall accuracy of 83.6%, and individually a producer’s accuracy of 73.8% for giant reed, 87.7% for Aleppo pine, 82.8% for French tamarisk, 93.5% for ground and 80.1% for other riverine species. Results were promising, proving the feasibility of using this cost-effective method for periodic monitoring of riverine species. In addition, the proposed workflow is easily transferable to other tasks beyond riverine species classification (e.g., green areas detection, land cover classification) opening new opportunities in the use of UAVs equipped with consumer cameras for environmental applications.

Highlights

  • Riverine areas play one of the most important functions of watersheds, influencing the transfer of energy, nutrients and sediments between aquatic and terrestrial systems, as well as being the habitat of a wide variety of animal and plant species, having a great landscape and educational interest (Gutiérrez and Alonso, 2013)

  • The geometric error of the point cloud was calculated by obtaining the Root Mean Square Error (RMSE) between the Ground Control Points (GCPs) and the position of the computed 3D point

  • In order to analyse the proposed methodology, intermediate results were obtained after carrying out the classification by points using the different machine learning methods

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Summary

Introduction

Riverine areas play one of the most important functions of watersheds, influencing the transfer of energy, nutrients and sediments between aquatic and terrestrial systems, as well as being the habitat of a wide variety of animal and plant species, having a great landscape and educational interest (Gutiérrez and Alonso, 2013). Is the excessive accumulation of riverine species in the riverbed, which on certain occasions can cause flooding out of the riverbeds. In this aspect, the management plans of the riverine areas of the Spanish Mediterranean basin are conditioned by the periods of heavy rains, considering a fundamental factor which is the risk of flooding (Arizpe et al, 2008). Management planning for riverine species is affected by the rapid changes that river dynamics cause in their structure, as well as by the need to have accurate three-dimensional information for the study of river mechanics (Stella et al, 2013)

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