Construction and Restoration of Landscape Ecological Network in Urumqi City Based on Landscape Ecological Risk Assessment

The ecological protection and sustainable development of Urumqi have become an important part of the high-quality growth of the urban agglomeration on the northern slope of Tianshan Mountain. Under the impacts of multi-source factors, the ecological landscape pattern of Urumqi has changed due to it being in a fragile eco-environment, so an ecological network is desperately needed to enhance ecological security patterns. Taking Urumqi city as the study area, the ecological risk evaluation model and the minimum cumulative resistance model were integrated to analyze the spatial and temporal features of landscape ecological risk from 2000 to 2020, and the future land use simulation model was used to predict the ecological risk pattern of Urumqi in 2030, construct a landscape ecological network, and propose ecological security protection strategies. Since 2000, land use in Urumqi has undergone drastic changes: the built-up land area has increased significantly, the landscape has diversified, and landscape fragmentation has shown a decreasing trend from the main urban area as the core to the urban fringe. The high-risk landscape ecology shows a decreasing trend from east to west, mainly in the bare land areas with sparse vegetation, whereas the risk is relatively low in woodland, arable land, and built-up areas. The change of risk in the study area is mainly influenced by the typical defective factors of oasis cities such as urban expansion, land desertification, and sparse vegetation. The landscape ecological network is mainly located in the southwest, central, and east of the study area, whereas there is no corridor distribution in the north and southeast, which is mainly caused by the special geographical location and climatic conditions. The ecological network mainly consists of 10 ecological sources and 10 ecological corridors and proposes conservation strategies for the optimization of the landscape pattern and for the construction of the ecological security pattern in Urumqi, providing a guide for the improvement of ecological security

Research on the Change in Prediction of Water Production in Urban Agglomerations on the Northern Slopes of the Tianshan Mountains Based on the InVEST–PLUS Model

Assessing how land use change will affect water production ecosystem services is essential to developing sound water resource management and ecosystem conservation. The results of a coordination analysis of land-use intensity and water yield based on future land-use simulation projections are useful for future land-use planning. To effectively assess water production rates, the PLUS and InVEST models were used to dynamically assess the changes in water production occurring in the urban agglomeration on the northern slopes of the Tianshan Mountains from 2000 to 2030 under different scenarios of land-use change. The results show that the water-production rates in the study area from 2000 to 2020 were 517.26 × 106 m3, 582.28 × 106 m3, and 456 × 106 m3, showing an increasing and then decreasing trend, with the water production function decreasing from the foothills of the Tianshan Mountains to the north and south, with values of 509.10 × 106 m33, 510.90 × 106 m3, and 502.28 × 106 m3 being presented for the three scenarios in 2030. The rapid economic development scenario presents the lowest water yield values and the ecological conservation development scenario presents the highest water yield values. Changes in water production rates are closely related to changes in land use, which can be verified further by an analysis of the coordination between land- use intensity and water production. For this study area, the ecological conservation development scenario may be more in line with the future urban development pattern, and the results of the present study can provide some scientific references for land-use planning

Construction of Urban Thermal Environment Network Based on Land Surface Temperature Downscaling and Local Climate Zones

It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the perspective of influencing factors. However, there are few studies on mitigating LST from the standpoint of regional networks. This paper first combines Landsat 8 with Sentinel-2 imagery for LST downscaling based on the Google Earth engine as a way to match local climate zone (LCZ) with 17 classification types. Then, the thermal environment resistance surface is constructed according to LCZ, and the essential cold sources are identified using morphological spatial pattern analysis (MSPA) and circuit theory to form the thermal environment green corridor and obtain the pinch point and barrier point areas. The results show that (1) The downscaling of LST based on random forest (RF) for the Urumqi–Changji–Wujiaqu metropolitan area has an R2 of 0.860 and an RMSE of 3.23, with high downscaling accuracy. (2) High temperature (HT), medium temperature (MT), and low temperature (LT) have the largest proportions in the study area; HT dominates in Urumqi, LT in Changji, and MT in Wujiaqu. (3) The natural types (LCZ-D, LCZ-C, and LCZ-F) in the LCZ classification occupy a large area, and the building types are mainly concentrated in Urumqi; LCZ-D, LCZ-G, and LCZ-A contribute the most to the cooling of LST, and LCZ-F, LCZ-C, and LCZ-10 contribute the most to the warming of LST. (4) After identifying critical cold source patches according to MSPA to arrive at 253 green corridors, subsensitive corridors and sensitive corridors need to take certain measures to prevent corridor blockage; pinch point areas, as well as barrier point areas, need to be protected and repaired according to their respective characteristics. In summary, corresponding cooling measures to specific areas can improve the connectivity between cooling sources and slow down the temperature increase of the whole area. This study and experimental approach can provide new insights for urban planners and climate researchers

Spatiotemporal characteristics and prediction of carbon emissions/absorption from land use change in the urban agglomeration on the northern slope of the Tianshan Mountains

Changes in land use are a significantly contribute to carbon emissions and other environmental problems. Regional carbon emissions changes from land use have been affected by rapid urbanization. To dynamically assess land use change and the spatiotemporal characteristics of carbon emissions from 1990 to 2020, this study used the PLUS, grey back-propagation neural network, and related carbon emission accounting models as well as four distinct 2030 scenario simulations. According to the findings, the urbanization of the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) developed rapidly from 1990 to 2020 with a noticeable transfer of diverse types of land. In particular, the quantity of construction land and cropland belonging to carbon source land increased by 153.271% and 55.072% respectively. Simultaneously, carbon emissions showed a trend of continuous increase and demonstrated an S-shaped curve growth, with relatively rapid growth from 2000 to 2015, that has tended to be stable in recent years. If this carbon emission trend continues, an inflection point will appear around 2028. Land use simulations for 2030 reveal that the ecological security (ES) scenario, which slows the expansion of contribution land and cropland while increasing ecological land, is most likely to reduce the negative environmental consequences of urbanization. This is because higher potential peak carbon emissions per unit area that may be obtained with carbon sink land. Consequently, the ES scenario of the study region is comparable to the future urban agglomeration development model. The results provide a reference for territorial space planning and dual carbon target recommendations in the UANSTM