基于指标自动筛选的新疆开孔河流域生态健康评价

生态健康评价对了解区域生态健康状况和促进区域可持续发展具有重要意义,如何自动筛选出能反映生态系统特性的重要指标,是生态健康定量评估的关键问题。基于压力-状态-响应(PSR,Press-State-Response)框架和生态等级网络框架(EHN,Ecological Hierarchy Network),通过文献调研和因果分析建立要素层与指标层之间的交叉联系,构建了生态健康评价"网状"指标体系;在保证指标体系完备性基础上,通过结合主成分分析和熵权法的候选指标权重的客观计算,基于目标优化理论构建了评价指标的自动筛选模型,并基于中选指标计算了新疆开孔河流域2001—2017年生态健康指数(EHCI,Ecological Health Comprehensive Indexes),分析其空间分异和时间变化特征。结果表明:利用所建立的评价指标自动筛选模型,开孔河流域生态健康评价指标由31个候选指标自动筛选出了17个中选指标,用54.8%的指标表达了85.98%的信息,中选的17个指标在干旱/半干旱区域有关文献中应用较多,使用频次比例都在20%以上,其中归一化植被指数(NDVI,Normalized Difference Vegetation Index)、年降水量和植被覆盖度(FVC,Fractional Vegetation Coverage)3个指标的使用频次百分比均超过了50%,说明指标自动筛选模型的合理性;开孔河流域空间分布差异显著,总体上西北高、东南低,东南部和中部绿洲区外围生态健康状况较差,西北部河谷地带和中部两大绿洲区生态健康状况较好;17年来,流域生态质量整体趋于改善,显著改善区域占10.26%,远高于显著退化的1.61%,显著改善区域以孔雀河绿洲最为明显。开孔河流域生态健康的总体好转趋势说明区域生态综合治理取得一定成效。

Ecological health assessment of Kaikong River Basin based on automatic screening of indicators in Xinjiang

Ecological health assessment is of great significance for understanding regionally ecological health status and promoting regional sustainable development. How to automatically screen out important indicators that reflect the characteristics of ecosystems is a key issue for quantitative assessment of ecological health. Based on the pressure-state-response (PSR) framework and Ecological Hierarchy Network (EHN), this paper establishes a cross-link between the feature layer and indicator layer through literature research and causal analysis, and builds a "network" indicator system for ecological health assessment. On the basis of ensuring the completeness of the indicator system, by combining the objective calculation of candidate components weights with principal component analysis and entropy weight method, an automatic screening model of evaluation indicators is constructed based on the target optimization theory. The ecological health comprehensive indexes (EHCI) of 2001-2017 in the Kaikong River Basin of Xinjiang were calculated based on the selected indicators, then spatial differentiation and time variation characteristics of EHCI were analyzed. The results showed that, using the established indicator automatic screening model, the eco-health evaluation index of the Kaikong River Basin automatically selected 17 indicators from 31 candidate indicators, and expressed 85.98% of the information with 54.8% of the indicators. The selected indicators have been widely used in the relevant literature, and the use frequency of the selected indicators was all above 20%. The frequency percentages of normalized difference vegetation index (NDVI), annual precipitation and fractional vegetation coverage (FVC) were all over 50%, indicating the rationality of the indicator automatic screening model. The EHCI''s spatial distribution in the Kaikong river basin was significantly different, generally higher in the northwest and lower in the southeast. The ecological health in the southeastern and central oasis areas was poor, and the northwest valley and the two oasis regions in the central region was good. In the past 17 years, the overall ecological quality of the river basin has improved. The area of significant improvement was 10.26%, mainly distributing in the Peacock River Oasis, far higher than the 1.61% of significant degradation. The overall ecological health improvement in the Kaikong river basin indicates that regionally ecological comprehensive management has achieved good outcomes.