Classification of Uav-Based Photogrammetric Point Clouds of Riverine Species Using Machine Learning Algorithms: A Case Study in the Palancia River, Spain

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.

Methodological Proposal for the Identification of Marginal Lands With Remote Sensing-Derived Products and Ancillary Data

“…Recall corresponds with the fraction of ML validation samples classified as positive, among the total number of positive ML. While Fmeasure is the harmonic mean of the model's precision and recall (Carbonell-Rivera et al 2020).…”

Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data

“…This approach allows the production of several digital surface models (DSMs), digital terrain models (DTMs) [11], orthophotos [12], and vegetation indices. Thus, fluvial environment monitoring is often possible, as these outcomes enable not only the detection of different environments (such as flooded or aquatic areas as well as vegetation or sandy/rocky regions) [8] but also the classification of different types of soils, vegetation detection [13], and feature extraction, such as tree height, canopy area and diameter [14], and individual tree counting [15].…”

“…Usually, the features classification in fluvial scenes is based on the exploitation of the satellite images [51] or of orthophotos obtained from photogrammetric image processing [52,53]. This technique can be more advantageous when, due to certain conditions (for example, dense vegetation and the presence of wind during image acquisition), the three-dimensional point cloud, generated during the photogrammetric process, is highly noisy; on the contrary, the direct analysis of the point cloud preserves spatial and threedimensional information of the scene, allowing one to delimit more accurately the different features and to obtain additional structural information [13].…”

Classification of Mediterranean Shrub Species from UAV Point Clouds

“…Summary of vegetation indices with their respective equations and references; ρ is defined as the digital number of the point for a given band. (ρ nir − ρ rb )/(ρ nir + ρ rb ), ρ rb = ρ red − [ρ blue − ρ red /2] blue − ρ red )]/[(ρ nir + 6•ρ red − 7.5•ρ blue + nir + 1 − [(2•ρ nir + 1) 2 − 8•(ρ nir − ρ red )] (ρ nir − ρ red )/(ρ nir + ρ red ) [45]NBRDI (Normalised Blue-Red Difference Index) (ρ red − ρ blue )/(ρ red + ρ blue )[46] NGBDI (Normalised Green-Blue Difference Index) (ρ green − ρ blue )/(ρ green + ρ blue )[47] NGRDI (Normalised Green-Red Difference Index) (ρ green − ρ red )/(ρ green + ρ red )…”