Changes and net ecosystem productivity of terrestrial ecosystems and their influencing factors in China from 2000 to 2019

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Evaluation of typical ecosystem services in Dabie Mountain area and its application in improving residents' well-being

Research on ecosystem services and residents' well-being in old revolutionary base areas is an important task for China's ecological civilization construction and rural revitalization. Taking Jinzhai County, the core area of Dabie Mountains, China, as an example, based on InVEST model, the methods of spatial autocorrelation and coupling coordinated development degree, the spatiotemporal evolution, spatial heterogeneity and coupling association patterns of ecosystem services and multidimensional well-being in the study area from 2005 to 2020 were discussed. The major results are: In the past 15 years, in the core area of the Dabie Mountains, ecosystem services such as food supply, soil retention and water yield showed an upward trend, carbon sequestration and biodiversity maintenance showed a downward trend. The comprehensive index of multidimensional well-being in the core area of Dabie Mountain increased by 27.23% and the spatial difference in multidimensional well-being is gradually narrowing. By the analysis of coupling coordination, the number of units with the type of coupling disharmony between ecosystem services and multidimensional well-being in the study area decreased significantly from 56.85% in 2005 to 26.81% in 2020, respectively. The analysis of geographical detection showed that the habitat quality factor was the dominant controlling factor of coupling coordination spatial difference. By bivariate spatial autocorrelation analysis, in the past 15 years, the number of units with the "high ecology-high well-being" synergy type increased from 5.44% to 13.31%. The results can provide a reference for accurate identification, optimal regulation and synergistic improvement between ecosystem services and relative poverty in the Dabie Mountain area.