Mapping rubber plantations in Xishuangbanna, southwest China based on the re-normalization of two Landsat-based vegetation–moisture indices and meteorological data

Abstract

Information on where rubber plantations are located and when they were established is essential for understanding changes in the regional carbon cycle, biodiversity, hydrology and ecosystem services. Here, we proposed a simple and modified phenology-based method to map rubber plantations and evaluate the effectiveness of this method in Xishuangbanna in southwest China, the second largest area of natural rubber cultivation. Our phenological algorithm is supported by local meteorological data and involves the re-normalization of two Landsat-8 Operational Land Imager-derived vegetation moisture indices (i.e. the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI)). We then applied air temperature data (daily records from 1981 to 2015) and periodic in situ observations of rubber plantations (weekly records from 2017 to 2018) to determine the phenological stages of rubber tree growth with the goal of selecting the most effective Landsat images. The re-normalization algorithm was able to highlight the temporal differences in the vegetation canopy and moisture content of rubber plantations, because rubber trees in Xishuangbanna display unique defoliation-foliation signals during the dry season. The resultant map of rubber plantations showed a high overall accuracy of 92.3% and a Kappa coefficient of 0.846. The developed phenological re-normalization method with meteorological data greatly enriches remote sensing-based approaches for mapping rubber plantations.

Keywords:

• Rubber plantations
• phenology
• Landsat
• Normalized Difference Vegetation Index
• Normalized Difference Moisture Index
• Xishuangbanna

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the China Postdoctoral Science Foundation (2019M660777), the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20010203), the National Natural Science Foundation of China (41971242), and the Program for BINGWEI Excellent Young Talents of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (2018RC201).