Timely and objective assessment of the optimal season for the construction of remote sensing ecological index (RSEI) is of great significance for accurate and effective assessment of ecological environment quality. We manipulated RSEI in different seasons to monitor and evaluate seasonal ecological environment quality (EEQ) variations in the Beijing-Tianjin-Hebei (JJJ) region from 2001 to 2020. First, we evaluated the image quality, and the bad observations were interpolated. Second, the seasonal RSEI was constructed by MODIS, and we compared the eigenvalues contribution rate of PC1 in seasons. Third, we assessed the temporal and spatial variations in EEQ across the same season within distinct years. Third, Moran’s I was employed to evaluate the spatial autocorrelation of EEQ and the stability of mean and standard deviation of correlation between RSEI and four indicators of seasons was compared. The results showed that: 1)the PC1 component concentrates most of the characteristics of the four indicators, especially in summer (over 71%); 2) the Moran’ I in the summer of 2001, 2006, 2011, 2016, and 2020 are 0.909, 0.898, 0.917, 0.921, and 0.892, respectively, which indicated that the EEQ has a strong positive spatial correlation. 3) the correlation between the four indicators and summer RSEI showed high correlation in different years, and the standard deviation of the correlation between the four indicators and RSEI fluctuated most slightly in summer, which the std of NDVI, WET, LST, and, NDBSI were 0.005, 0.052, 0.026, and 0.017, respectively. 4) Only the RSEI in summer and the VCF show spatial distribution consistency in the long time series RSEI spatial distribution of four seasons. This study explored the spatiotemporal variations of EEQ in the JJJ region at a seasonal scale, which can provide a reference for selecting the optimum season for the ecological quality monitoring of urban agglomeration in the future.
Context Vegetation productivity is crucial for human production and livelihoods. Monitoring changes in NPP (Net Primary Productivity) is essential to evaluate regional ecological shifts and carbon sink capacity. Objectives Our objective is to explore the variations of NPP during 2001–2020 and propose a new idea to predict the actual NPP in 2030 under multiple climate scenarios, taking the Beijing-Tianjin-Hebei (BTH) region as an example. Methods This study utilized the PLUS (patch-generating land use simulation) and improved CASA (Carnegie-Ames-Stanford Approach) models, along with remote sensing and climate data, to estimate changes in NPP in the BTH region for the period 2001–2020 and predict NPP in 2030. Results The results indicate that, during the period of 2001–2020, the NPP in the research area maintained a spatial distribution pattern, with higher values in the northeastern forest area, a slightly higher value is found in the southeast of the city, while a lower value is found in the northwest and center, showing an overall gradual improvement trend. However, the NPP in the study area is predicted to decline in 2030 compared to 2020, albeit better than that in 2001–2015. Moreover, NPP will decline in 2030 under three future climate scenarios, and the NPP condition is optimal under the SSP 1-2.6 scenario. Conclusions NPP will decline in 2030 in the BTH region, it may be related to some current ecological policies. Comparing NPP development under three future climate scenarios, we find that a low-emission scenario, which represents a green development model, is more favorable for the development of NPP. This research sheds light on the variations of NPP in the BTH region and offers a scientific basis for relevant departments to formulate future policies.
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