Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China.

Urban ecological environment is the basis of citizens' survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECI), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECI, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECI was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECI fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECI in Hangzhou > Hefei ≈ Nanjing, spring ≈ autumn > summer ≫ winter. From 2009 to 2019, UECI has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECI in 2014-2019 is 4.3 times than that in 2009-2014. The correlation between UECI and economic index indicates that economic development has a positive impact on the urban ecological environment. The significant degradation of UECI in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.