An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

Martina Deur,Ivan Balenović,Mateo Gašparović

doi:10.3390/rs13101868

Abstract

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

Highlights

Over the last few decades, significant technological development of optical sensors has increased the possibility of remote sensing applications in many disciplines, including forestry
By using open-source software, we confirmed the significant potential of pansharpened very high resolution (VHR) WV-3 imagery for tree species classification in areas of mixed deciduous forest stands
Reference polygons were generated for three tree species classes: Alnus glutinosa, Carpinus betulus, and Quercus robur as well as bare land, low vegetation, and shadow

Summary

Introduction

Over the last few decades, significant technological development of optical sensors has increased the possibility of remote sensing applications in many disciplines, including forestry. The application of remote sensing in forestry on both local and regional scales decreases the need for difficult, expensive, and slow field surveys and at the same time increases the quantitative and qualitative value of the information obtained [1]. Very high resolution (VHR) satellite imagery (e.g., PlanetScope, SkySat, WorldView) provides a large number of more detailed information and presents an effective tool for individual tree species classification [11,12].

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Conclusion