Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Xisheng Zhou,Yifan Cui,Yunqiang Liu,Longqian Chen,Yu Zhang,Ting Zhang,Long Li

doi:10.3390/f10060478

Abstract

Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.

Highlights

Urban forests are important carriers of urban ecosystems [1,2], which can improve the urban microclimate, maintain the surface water–heat exchange balance [3,4], mitigate rainstorm runoff [5,6], and provide a comfortable habitat for urban residents [7]
Accuracy assessment based on the validation data acquired from our fieldwork showed that the highest accuracy and Kappa coefficient were achieved by M4
Overall classification accuracy was improved by 1.45% when adding digital elevation model (DEM) to M1, and was further improved when textural features and vegetation abundances were added one by one

Summary

Introduction

Urban forests are important carriers of urban ecosystems [1,2], which can improve the urban microclimate, maintain the surface water–heat exchange balance [3,4], mitigate rainstorm runoff [5,6], and provide a comfortable habitat for urban residents [7]. Forests 2019, 10, 478 forestry studies [8]. It provides a basis for the estimation of above-ground biomass of urban vegetation [9,10]. Since it was first introduced by Jorgensen (1986) [11], urban forestry received increasing attention from scholars. The scope of urban forests was defined from a variety of research perspectives [12,13,14]. An urban forest can refer to all the trees in an urban area, including forest parks, and public and private woodlands [15], while Miller (1996) [16] and other researchers [17,18]

Objectives

Methods

Results

Conclusion