The effect of urbanization on the urban thermal environment (UTE) has attracted increasing research attention because its significant effects on local weather and climate, and serious consequences for people. However, systematic study of the relationship between urbanization and UTE has been undertaken only to a limited extent. Using quantitative thermal remote sensing and multi-buffer ring method and multiple spatial scales method, here we analyze Landsat TM/ETM+ images of Zhengzhou in Central China acquired on four different dates in 2017 to investigate the spatiotemporal variations, trends, and driving force in the land surface temperature (LST). Our results showed that LST generally increased with urbanization intensity. This trend was extremely obvious in spring and summer, weak in winter, and slightly downward in autumn. Moreover, PLAND (e.g., percentage of impervious surface in a landscape) has the most significant effect on urban LST, and generally increases as the spatial scale becomes larger. In conclusion, the study recommends that urban planning in Zhengzhou should prioritize PLAND, especially at large spatial scales. These results provide a scientific reference for urban planners who are committed to the sustainable development of Zhengzhou City.
The degradation and loss of global urban habitat and biodiversity have been extensively studied as a global issue. Urban heat islands caused by abnormal land surface temperature (LST) have been shown to be the main reason for this problem. With the accelerated urbanization process and the increasing possibility of abnormal temperatures in Zhengzhou, China, more and more creatures cannot adapt and survive in urban habitats, including humans; therefore, Zhengzhou was selected as the study area. The purpose of this study is to explore the response of urban habitat quality to LST, which provides a basis for the scientific protection of urban habitat and biodiversity in Zhengzhou from the perspective of alleviating heat island effect. We used the InVEST-Habitat Quality model to calculate the urban habitat quality, combined with GIS spatial statistics and bivariate spatial autocorrelation analysis, to explore the response of habitat quality to LST. The results show the following: (1) From 2000 to 2015, the mean value of urban habitat quality gradually decreased from 0.361 to 0.304, showing a downward trend as a whole. (2) There was an obvious gradient effect between habitat quality and LST. Habitat quality’s high values were distributed in the central and northern built-up area and low values were distributed in the high-altitude western forest habitat and northern water habitat. However, the distribution of LST gradient values were opposite to the habitat quality to a great extent. (3) There were four agglomeration types between LST and habitat quality at specific spatial locations: the high-high type was scattered mainly in the western part of the study area and in the northern region; the high-low type was mainly distributed in the densely populated and actively constructed central areas; the low-low type was mainly distributed in the urban-rural intersections and small and medium-sized rural settlements; and the low-high type was mainly distributed in the western mountainous hills and the northern waters.
Urban ecosystem dysfunction, habitat fragmentation, and biodiversity loss caused by rapid urbanization have threatened sustainable urban development. Urban habitat quality is one of the important indicators for assessing the urban ecological environment. Therefore, it is of great practical significance to carry out a study on the driving mechanism of urban habitat quality and integrate the results into urban planning. In this study, taking Zhengzhou, China, as an example, the InVEST model was used to analyze the spatial differentiation characteristics of urban habitat quality and Geodetector software was adopted to explore the driving mechanism of habitat quality at different grid-scales. The results show the following: (1) LUCC, altitude, slope, surface roughness, relief amplitude, population, nighttime light, and NDVI are the dominant factors affecting the spatial differentiation of habitat quality. Among them, the impacts of slope, surface roughness, population, nighttime light, and NDVI on habitat quality are highly sensitive to varying grid-scales. At the grid-scale of 1000 to 1250 m, the impacts of the dominant factors on habitat quality is closer to the mean level of multiple scales. (2) The impact of each factor on the spatial distribution of habitat quality is different, and the difference between most factors has always been significant regardless of the variation of grid-scales. The superimposed impact of two factors on the spatial distribution of habitat quality is greater than the impact of the single factor. (3) Combined with the research results and the local conditions of Zhengzhou, we put forward some directions of habitat protection around adjusting urban land use structure, applying nature-based solutions and establishing a systematic thinking model for multi-level urban habitat sustainability.
During urbanization in developing countries, fragmentation of green infrastructure due to increasing populations and the expansion of construction land leads to an extremely serious imbalance between the supply and demand for urban ecosystem services. In this study, the central city of Zhengzhou, a central city in central China, was selected as the study area and the excessive demand for six ecosystem services, namely, air purification, flood regulation, heat regulation, hydrological regulation, CO2 sequestration and recreational services, was quantitatively evaluated. The entropy method was used to calculate the weights of various ecosystem services, and spatial overlay analysis was performed to obtain the comprehensive ecosystem service excessive demand. Finally, bivariate spatial autocorrelation analysis was used to explore the response of population density to comprehensive excessive demand for ESs. The results of this study indicate that: (1) The most prevalent need is for more CO2 regulation service throughout the study area. (2) Except for hydrological regulation service, the spatial distribution of the remaining highly excessive ecosystem service demands are mostly concentrated in old neighborhoods. (3) Of the six excessively demanded economic services, rainwater regulation obtained the greatest weight, reflecting the poor urban infrastructure configuration for countering the rapidly increasing threat of flooding caused by climate change in the city. (4) The comprehensive ecosystem service excessive demand results show that there are eight priority green infrastructure implementation blocks in the central city of Zhengzhou. (5) There were three agglomeration types between population density and comprehensive excessive demand for ESs: high-high type, low-high type and low-low type. The spatial distribution characteristics of population density and comprehensive ES demand are positively correlated. The results of this study could help to provide information for decision making when delineating the priority areas and types of green infrastructure implementation in developing cities.
Exploring protected area (PA) siting from a biodiversity perspective is critical in mitigating human impacts on ecosystems. This paper used the MaxEnt model to predict the geographic distribution patterns of wetland species in Zhengzhou and the environmental factors affecting species’ habitat selection. Environmental variables were screened by correlation analysis to avoid affecting the prediction results due to overfitting of the model. The AUC value of the training set of the model ROC curve was above 0.8, and the prediction accuracy was high. The prediction results showed that the only nature reserve in Zhengzhou, Yellow River Wetland Nature Reserve, currently covers only 10.25% of the total area of the high suitability areas for plants and 17.54% of the high suitability habitat areas for waterfowl in the whole area of Zhengzhou. The potential suitability areas of wetland species outside the reserve can provide a basis for site selection for wetland conservation planning in Zhengzhou. It was found that the geographic distribution of wetland species in Zhengzhou is constrained by the distribution of water bodies, bioclimatic variables, land cover, and population density.
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