Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features

Zhenjiang Wu,Sha Zhang,Tehseen Javed,Fan Deng,Guizhen Liu,Dan Liu,Da Zhang,Lan Xun,Mengfei Ji,Jiahua Zhang

doi:10.3390/rs13050835

Abstract

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

Highlights

Grassland covers about 40% of the Earth’s surface [1]
The results indicate that compared to using multispectral data only, the method that integrates multispectral data, principle component analysis (PCA) EVI2 time-series, and phenological features contributes to improving the classification accuracy of grassland communities
The ESTARFM algorithm was validated for its applicability on GF data to generate 16 m spatial resolution cloudless time-series data

Summary

Introduction

In China, grassland covers about 3.93 × 106 km , accounting for 41.7% of the total land area [2]. It is a renewable source for livestock production, helps ecological stability, and produces wealth for humans [3]. The field survey is a common approach for the classification of grassland. It is a laborious and time-consuming method [5,6,7]. RS-based unmanned aerial vehicles (UAV) have acquired hyperspectral or high-spatial resolution datasets which are well known, and precise methods for classification of grassland over the small area can be implemented [9,10,11,12]. A satellite-borne multispectral dataset is more suitable for the regional scale [13,14,15]

Objectives

Methods

Results

Discussion

Conclusion