Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia

In the present global situation, when everywhere ecology is degraded due to the extreme exhaustion of natural resources. Therefore spatiotemporal ecological vulnerability analysis is necessary for the current situation for sustainable development with protection of fragile eco-environment. Remote sensing is a unique tool to provide complete and continuous land surface information at different scales, which can use for eco-environment analysis. A methodology constructed on the principal component analysis (PCA) to identify satellite remote sensing ecological index (RSEI) for ecological vulnerability analysis and distribution based on four land surface parameters (dryness, greenness, temperature and moisture) by using Landsat TM/ETM+/OLI/TIRS data in the Samara region Russia. The results were verified by the following four methods: location-based, categorization based, correlation-based and city center to outwards distance-based comparisons. Results indicate that ecological condition was improved from 2010 to 2015 as RSEI increased from 0.79 to 0.98 and from 2015 to 2020 the ecological condition was degraded as RSEI decreased from 0.98 to 0.82 but overall it was improved in this decade. RSEI distribution curve shows moderate to good and excellent ecological conditions and degraded ecological condition was basically characterized by high human interference and socioeconomic activities in the study area. Such a technique is a baseline for highly accurate ecological conditions mapping, monitoring and can use for decision making, management and sustainable development.

Automated summary

The article introduces a technique for ecological vulnerability analysis based on remote sensing data and PCA method, analyzing the changes of four land surface parameters in the Samara region of Russia to reveal the trends in ecological conditions from 2010 to 2020. It suggests that this technology could be utilized for ecological conditions mapping, monitoring, decision-making, and management.