International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 659–666, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-659-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 659–666, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-659-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Aug 2020

12 Aug 2020

CLASSIFICATION OF UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS OF RIVERINE SPECIES USING MACHINE LEARNING ALGORITHMS: A CASE STUDY IN THE PALANCIA RIVER, SPAIN

J. P. Carbonell-Rivera, J. Estornell, L. A. Ruiz, J. Torralba, and P. Crespo-Peremarch J. P. Carbonell-Rivera et al.
  • Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain

Keywords: Point cloud classification, UAV, Structure from Motion, Random forest, Riverine species

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.