Ecological environment quality assessment of Xishuangbanna rubber plantations expansion (1995–2018) based on multi-temporal Landsat imagery and RSEI

Abstract

The specific impact of ecological environment quality at a regional scale due to the rubber plantations expansion is still unclear in Xishuangbanna, Yunnan province, China. First, we used a pixel and phenology-based multiple normalization approach to map rubber plantations over six time periods during 1995–2018 with the available high-quality Landsat imagery. Second, a pixel-based optimized algorithm was developed to reduce the commission and omission errors of the rubber plantations maps and we mapped the rubber plantations expansion area. Third, a remote sensing-based ecological index (RSEI) was employed to assess the rubber plantations expansion area ecological environment quality. RSEI value decreased with the lowest value in 2007 (0.511) and increased with the highest value in 2016 (0.727). This study demonstrated that the ecological environment quality degraded from 1995 to 2007 with an increase of rubber plantations expansion, but improved gradually from 2007 to 2018 with the slow growth of rubber.

Keywords:

• Rubber plantations expansion
• RSEI
• ecological grading
• multi-temporal Landsat imagery

Acknowledgments

We thank Chi-wei Xiao for his rubber mapping algorithm. We thank the anonymous reviewers for their constructive comments on earlier version of the manuscript.

Disclosure statement

The authors declare no conflicts of interest.

Author contributions

Y. X. and W.-H.X. designed the research and wrote the paper; S.-D. H and C. W. conducted the data analyses; W.-H. X. and L.-G. W. contributed to the algorithm; Y. X. and F. D. designed and proposed the main structure of this study; W.-L. Q. and S.-D. H. wrote the draft manuscript; N. L. contributed to the manuscript revision.

Additional information

Funding

This research was supported in part by research grants from the National Natural Science Foundation of China (31860181, 31860182, 31760181, 61702442, 32060320); Major Science and Technology Project of Yunnan Province (202002AD080002, 2019ZE005); Applied Basic Research Project of Yunnan Province (FB105); Scientific Research Fund project of Yunnan Education Department (2020Y0378, 2021Y218); the Scientific Research Foundation for Ph.D., Southwest Forestry University (111821).