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Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox

1
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, I-89122 Reggio Calabria, Italy
2
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal
3
Spin.Works S.A., Avenida da Igreja 42, 6º, 1700-239 Lisboa, Portugal
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1238; https://doi.org/10.3390/rs11101238
Received: 18 February 2019 / Revised: 20 May 2019 / Accepted: 21 May 2019 / Published: 24 May 2019
(This article belongs to the Special Issue UAV Applications in Forestry)
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Abstract

This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands. View Full-Text
Keywords: Geographic Object-Based Image Analysis (GEOBIA); Land cover classification; Unmanned Aerial Vehicles (UAVs); Orfeo ToolBox (OTB); cork oak woodlands; machine learning algorithms; Random Forest (RF); Support Vector Machines (SVM); Spectral separability; Accuracy assessment. Geographic Object-Based Image Analysis (GEOBIA); Land cover classification; Unmanned Aerial Vehicles (UAVs); Orfeo ToolBox (OTB); cork oak woodlands; machine learning algorithms; Random Forest (RF); Support Vector Machines (SVM); Spectral separability; Accuracy assessment.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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De Luca, G.; N. Silva, J.M.; Cerasoli, S.; Araújo, J.; Campos, J.; Di Fazio, S.; Modica, G. Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox. Remote Sens. 2019, 11, 1238.

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