摘要
高速城市化进程对地区生态环境产生胁迫影响,及时评估生态质量变化对城市生态管理和规划具有重要意义。遥感生态指数(RSEI)是一种客观、快速和简便的生态质量评价技术,已被广泛应用于生态学领域,但在进行大范围、长时间评估时往往面临云遮挡的问题。本文基于Google Earth Engine(GEE)云平台,利用其海量的MODIS遥感图像,采用中值合成法逐年计算了成渝城市群的绿度、湿度、干度和热度遥感指标,并利用主成分分析法构建RSEI,评价了该区近20年生态质量的时空变化。结果表明:(1)近20年成渝城市群RSEI呈缓慢上升趋势,于2012年后RSEI开始呈现稳定态势,川中丘陵区和成都平原的生态环境质量降低明显,而渝东北地区的生态环境质量改善明显;(2)成渝城市群生态环境变化存在明显的空间自相关,Moran’s I指数达0.825,RSEI能够较好地表征研究区的生态质量,且绿度和湿度与其呈正相关,热度和干度与其呈负相关,绿度是生态环境变化的主导因素;(3)基于GEE云计算的图像处理可以较好地改善因多云多雨导致的遥感数据缺失问题,同时能极大提高影像处理效率,扩展了RSEI在大范围、长时间序列生态评估中的应用。研究结果可以为快速城市化背景下生态保护和土地管理提供理论依据。
Rapid urbanization has a stressful effect on regional ecological environment.Timely monitoring of ecolo-gical quality changes is thus crucial for urban ecological management and planning.The remote sensing-based ecological index(RSEI)is an objective,quick,and easy tool for monitoring and evaluating ecological quality,which has been frequently used in ecological research.However,large-scale and long-term monitoring is frequently confronted with the problem of cloud blockage.Based on Google Earth Engine(GEE)cloud platform and its massive MODIS remote sensing images,we calculated remote sensing indices of greenness,humidity,dryness,and heat of Chengdu-Chongqing Urban Agglomeration(CCUA)annually by using the median synthesis method,and constructed the RSEI using the principal component analysis to evaluate the temporal and spatial variations of ecological quality in this area in the past 20 years.The results showed that:(1)The RSEI of the CCUA showed a slow upward trend and remained stable since 2012.The ecological quality in hilly area of central Sichuan and Chengdu Plain had been deteriorated dramatically,and were significantly improved in mountainous areas of the northeastern Chongqing;(2)The changes in the CCUA’s ecological quality showed clear spatial autocorrelation.The Moran’s I index was 0.825.Greenness and wetness indices were positively correlated with RSEI,while heat and dryness indices were negatively correlated with it,and the greenness was the primary driver for the changes in ecological quality of the CCUA.(3)Image processing based on GEE cloud computing may considerably enhance image processing efficiency,and address the problem of remote sensing data missing due to overcast and wet conditions.It also has the potential to broaden the scope of RSEI’s use in large-scale and long-term sequence ecological monitoring.Our findings can be used as a theoretical basis for environmental conservation and land management under the context of rapid urbanization.
作者
吴小波
范晓雨
刘晓敬
肖林
马启民
贺宁
郜思卓
乔雨亭
WU Xiaobo;FAN Xiaoyu;LIU Xiaojing;XIAO Lin;MA Qimin;HE Ning;GAO Sizhuo;QIAO Yuting(College of Resources,Sichuan Agricultural University,Chengdu 611130,China;College of Forestry,Sichuan Agricultural University,Chengdu 611130,China;School of Resources and Environment,Chengdu University of Information Technology,Chengdu 610103,China)
出处
《生态学杂志》
CAS
CSCD
北大核心
2023年第3期759-768,共10页
Chinese Journal of Ecology
基金
国家重点研发计划(2021YFE0102000)
大学生创新训练项目“基于深度学习的四川丘陵山区耕地提取研究”资助。
