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

Remote Sensing

Jiahua Zhang¹  Da Zhang¹  Lan Xun¹  Guizhen Liu²  Sha Zhang³  Tehseen Javed³  Fan Deng⁴  Mengfei Ji⁴  Dan Liu⁴  Zhenjiang Wu⁴ 

[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;Agricultural Comprehensive Development Office, Dongsheng District, Ordos City 017000, China;Research Center for Remote Sensing Information and Digital Earth, College of Computer Science and Technology, Qingdao University, Qingdao 266071, China;School of Geoscience, Yangtze University, Wuhan 430100, China;

关键词: grassland community classification;    GaoFen satellite;    ESTARFM;    time-series;    regional-scale;   

DOI  :  10.3390/rs13050835

来源: DOAJ

【 摘 要 】

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$\mathrm{m}$) spatial and ( 2 day) 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$\mathrm{m}$ moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16$\mathrm{m}$ 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.

【 授权许可】

Unknown