Testing Accuracy of Land Cover Classification Algorithms in the Qilian Mountains Based on GEE Cloud Platform

Dong Yang,Xufeng Wang,Zhao Zhang,Zain Nawaz,Yanpeng Yang

doi:10.3390/rs13245064

Abstract

The Qilian Mountains (QLM) are an important ecological barrier in western China. High-precision land cover data products are the basic data for accurately detecting and evaluating the ecological service functions of the QLM. In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google Earth Engine (GEE) cloud platform, the higher spatial resolution remote sensing images of Sentinel-1 and Sentinel-2; digital elevation data; and three remote sensing classification algorithms, including the support vector machine (SVM), the classification regression tree (CART), and the random forest (RF) algorithms, were used to perform supervised classification of Sentinel-2 images of the QLM. Furthermore, the results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy.

Highlights

Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Academic Editor: Janne Heiskanen
Three classification algorithms, including the support vector machine (SVM), classification regression tree (CART), and random forest (RF) algorithms were selected for analysis in this research, and the classifications were performed using the feature variable combinations with the best classification effect
Based on the Google Earth Engine and the various remote sensing satellite data provided by it, a feature space composed of feature variables such as spectral bands, spectral indices, terrain features, texture features, and radar features was constructed

Summary

Introduction

In order to study the land cover in the QLM and performance of different remote sensing classification algorithms for land cover mapping based on the Google. The results obtained from the classification process were compared and analyzed by using different remote sensing classification algorithms and feature-variable combinations. The results indicated that: (1) the accuracy of the classification results acquired by using different remote sensing classification algorithms were different, and the RF had the highest classification accuracy, followed by the CART and the SVM; (2) the different feature variable combinations had different effects on the overall accuracy (OA) of the classification results and the performance of the identification and classification of the different land cover types; and (3) compared with the existing land cover products for the QLM, the land cover maps obtained in this study had a higher spatial resolution and overall accuracy. Accurate land cover mapping and its changes are still facing many challenges due to land surface heterogeneity and spectral confusion, especially in higher-resolution mapping

Objectives

Methods

Results

Discussion

Conclusion