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

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.

Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

“…Trisasongko et al (2017) [64] compared different algorithms to classify vegetation at tropical landscapes and pointed out that among the tested classifiers the tree-based models presented a higher accuracy using all possible data configurations. The current research also corroborates De Luca et al (2019) [65] research, which compared RF and Support Vector Machine algorithm to classify structurally complex Mediterranean forest (cork oak woodlands), having found better performance to RF models; although, for their research the kappa values were superior (0.928 to 0.973), probably due the quality of input data (images from unmanned aerial vehicles-UAVs). One of the advantages in the use of remote sensing data and data mining techniques to map vegetation types is the possibility of improvements on vegetation maps, through better-fitted models, more field data, or the availability of sensors with better spectral and spatial resolution, as exemplified by the approach presented in this study.…”

Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

“…In this work the error is approximately the same as in [12,13,[15][16][17], but was tested in geographically separated areas, with different kinds of vegetation and during two full years, representing a more uncontrolled environment. In [10], the composites were generated in specific seasons of the year, avoiding the phenology effects. The proposed ANN was trained to detect only a specific kind of change, without discriminating changes as in [15][16][17].…”

“…This new high spatial resolution allowed the analysis of the FB conditions, which in current work, was to detect when maintenance operations are performed. It is common to divide remote sensing applications into two groups: land cover classification [7][8][9][10] and change detection [11][12][13][14][15][16][17]. The proposed methodology fits into the second group.…”

OIC-MCE: A Practical Land Cover Mapping Approach for Limited Samples Based on Multiple Classifier Ensemble and Iterative Classification

“…In order to avoid the "Salt and Pepper Noise" often existing in pixel-based classification results, and to take fully into consideration spectral, shape, and texture features, the object-oriented classification method was adopted [44][45][46]. The multi-resolution segmentation algorithm, adopted by the eCognition Developer 8.7, was adopted in this study.…”