Ecological environment quality assessment based on remote sensing data for land consolidation

• Published in: Journal of cleaner production 2019-12, Vol.239, p.118126, Article 118126
• Main Authors: Shan, Wei, Jin, Xiaobin, Ren, Jie, Wang, Yongcai, Xu, Zhigang, Fan, Yeting, Gu, Zhengming, Hong, Changqiao, Lin, Jinhuang, Zhou, Yinkang
• Format: Article
• Language: English
• Subjects: Dynamic monitoring; Ecological environmental quality; Land consolidation; Remote sensing; RSEI
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2019.118126

Ecological transformation is inevitable in development of land consolidation (LC) in China. An appropriate approach for assessing ecological environmental quality (EEQ) is fundamental for the analysis of LC efficiency. However, previous studies generally rely on statistical data or land use data, and the objectivity of these data and research periods are generally limited at the project scale. Based on the remote sensing data 6 years before and after LC, this study developed the RS-based theoretical research framework, provided a contrastive analysis between Remote Sensing Ecological Index (RSEI) and Ecological Index (EI) and expanded spatio-temporal pattern study to assess EEQ in a typical land consolidation project area (LCPA) of China, with the application of RSEI method. Results suggested that RSEI and EI showed strong comparability in the ecological sense, and RSEI can reflect the changes in EEQ in LCPA more effectively than EI. Wetness and greenness indicators had positive effects on EEQ promotion, while heat and dryness indicators had negative effects, and the effect of dryness indicator was more significant than that of other indicators. Before, during, and after LC, EEQ decreased first, increased later, and decreased overall, with the characteristics of degenerating during the construction period, improving during the restoration period, as well as degenerating in the overall process. LC caused persistent disturbance to EEQ, but there was a lag for restoration for more than 5 years. Prediction indicated that the selected indicators were the key indicators of EEQ, among which wetness and greenness were essential for EEQ improvement. This study provides certain theoretical guidance and method reference for the scientific, objective, direct, and long-term comprehensive monitoring and assessment of EEQ at the project scale. [Display omitted] •A RS-based theoretical framework is constructed to evaluate the EEQ with satellite data.•RSEI proves more efficient than EI to assess the changes of EEQ before, during and after LC.•EEQ presents a significantly spatial and temporal variation of pattern in LCPA.•There is lag of more than 5 years for restoration of the EEQ in LCPA.•In this LCPA, dryness indicator is the major factor that determines the EEQ.