Ecological quality assessment and monitoring using a time-series remote sensing-based ecological index (ts-RSEI)

Abstract

Ecological quality assessment is fundamental to revealing changes in ecological environments before the development of effective ecological conservation policies. The complex ecological environment can be assessed more reliably when multiple remote sensing indices are integrated, such as in the use of the prevalent Remote Sensing-based Ecological Index (RSEI). However, for effective ecological quality assessment, the requirement of acquisition time consistency for images has become an outstanding issue for broadening the application of the RSEI. In this study, we adjusted the RSEI to the Continuous Change Detection and Classification (CCDC) algorithm that predicts synthetic images instead of real images. Based on this algorithm, we mapped time-series RSEI (ts-RSEI), which provide comparable results for tracing the dynamics of ecological quality at any time. Our major findings are as follows: (1) The RSEI is very sensitive to the timespan of the image acquisition dates, with the Mean Absolute Difference (MAD) of 0.111 (19.2%) when the interval between dates exceeds one month. (2) The ts-RSEI from synthetic images is comparable to the RSEI from real images, with the MAD of 0.075 (10.5%), which is superior to that of two real images with the timespan of half-a-month. (3) For Hangzhou, the ecological quality was maintained for almost the past 35 years (the ts-RSEI changed from 0.679 to 0.705). However, special attention should be paid to the spatial polarization between natural (“better”) and human-dominated (“worse”) environments. The high temporal consistency and the capability of any- time mapping of the ts-RSEI are expected to be of value to policy makers and authorities in implementing effective ecological conservation measures.

Keywords

• remote sensing
• ecological quality assessment
• time-series
• rsei
• ccdc
• hangzhou