Yinyin Dou

Rapid urban growth has transformed the three-dimensional structure and land cover composition of Chinese cities, yet long-term local climate zone (LCZ) changes remain insufficiently resolved at fine spatial scales. We mapped all 17 standard LCZ classes for 105 major Chinese cities in 2000, 2005, 2010, 2015, 2020, and 2025 at a 30 m resolution. A unified random forest framework implemented on Google Earth Engine integrated Landsat imagery with spectral, topographic, building, vegetation, and ancillary land cover information. Overall accuracy ranged from 83.88% to 88.71% across the six mapping years. The mapped urban area increased from 2.14 × 10⁴ to 5.86 × 10⁴ km², with the strongest relative growth shifting from eastern toward central and western China. Although the built-class area more than doubled, its share declined from 68.61% to 53.27%, while the proportion of vegetated-class area increased from 20.47% to 39.51%. LCZ D, LCZ 9, and LCZ A contributed most to the overall increase, whereas LCZ 8 declined in proportional importance. All moisture regions shifted toward more balanced built–vegetated configurations. Dryland cities, however, retained more bare surfaces and less woody vegetation than humid and transitional cities. These maps reveal both a common national trajectory and climate-conditioned regional differentiation, providing a basis for urban-climate assessment and climate-sensitive planning.

High-resolution mapping of near-surface air temperature (Tair) is essential for understanding urban microclimates and developing effective heat-mitigation strategies. However, existing retrieval methods often fail to capture fine-scale thermal gradients because they overlook the complex interaction between localized surface properties and regional atmospheric processes. Here we developed a novel 30-m resolution urban near-surface air temperature retrieval (UNAT-30) model based on a geophysical process-oriented framework that integrates horizontal advection with local driving factors. By optimizing the mixing coefficient (f), we achieved high-precision Tair against 50 ground-based meteorological stations in Beijing megacity during 2009–2019. Our results demonstrate that UNAT-30 model outperforms existing methods, yielding a mean root mean square error (RMSE) of 0.91°C and an R2 of 0.95. Spatiotemporal analysis reveals pronounced thermal heterogeneity, specifically highlighting substantial thermal contrasts between densely built residential zones and adjacent blue-green spaces—gradients that are frequently underestimated by traditional interpolation or course-resolution products. The UNAT-30 model is computationally efficient and transferable, providing a robust scientific basis for local governments to implement targeted urban heat mitigation strategies.

High-resolution mapping of near-surface air temperature (Tair) is essential for understanding urban microclimates and developing effective heat-mitigation strategies. However, existing retrieval methods often fail to capture fine-scale thermal gradients because they overlook the complex interaction between localized surface properties and regional atmospheric processes. Here we developed a novel 30-m resolution urban near-surface air temperature retrieval (UNAT-30) model based on a geophysical process-oriented framework that integrates horizontal advection with local driving factors. By optimizing the mixing coefficient (f), we achieved high-precision Tair against 50 ground-based meteorological stations in Beijing megacity during 2009–2019. Our results demonstrate that UNAT-30 model outperforms existing methods, yielding a mean root mean square error (RMSE) of 0.91°C and an R2 of 0.95. Spatiotemporal analysis reveals pronounced thermal heterogeneity, specifically highlighting substantial thermal contrasts between densely built residential zones and adjacent blue-green spaces—gradients that are frequently underestimated by traditional interpolation or course-resolution products. The UNAT-30 model is computationally efficient and transferable, providing a robust scientific basis for local governments to implement targeted urban heat mitigation strategies.

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions.

Context: Expansion of urban green space (UGS) enhances greenery and its benefits in cities. However, the impacts of such expansions on regional landscape sustainability and their spatiotemporal traits across the national scale remain unclear. Objectives: This study uncovers a trade-off between agricultural ecosystem services and urban human well-being—the loss of grain production due to UGS expansion consuming cropland—in China from 2000 to 2020. It proposes a safety boundary for UGS expansion to minimize the costs of grain production while addressing the escalating demand for UGS exposure. Methods: UGS expansion and cropland loss are quantified using land cover products, with statistical data contributing to the established relationship between grain loss and UGS coverage. UGS expansion scenarios for 2040 are simulated through bottom-up modeling. Results: Quadrupled UGS area growth contributed approximately one-third to urban expansion during 2000–2020 in China. Despite improvements in per capita UGS area implying enhanced UGS exposure during rapid urbanization, UGS expansion resulted in a national grain production loss of 4.2 ± 1.4 million t from 2000 to 2020, accelerating post-2010. This loss is equivalent to the annual food intake of 9.3 million Chinese people. We propose a 30.87% UGS coverage target in China by 2040 to preserve UGS access while minimizing grain loss under future urbanization. Conclusions: Acknowledging the critical role of UGS expansion in the issue of cropland loss to urbanization is imperative. Our results offer insights into ensuring human well-being and ecosystem services through landscape and urban planning.

Examining the ecology quality of the Pan-Third Pole region has implications for global environmental change and sustainable development. However, spatio-temporal evolution of ecological quality in this region is still lacking. In this study, 65 countries of the Pan-Third Pole region were selected. A comprehensive evaluation index system of ecological quality was constructed using a dataset containing remote sensing, ecological environment and socio-economic data to spatially quantify the ecological quality, as well as its change from 2000 to 2020. The results displayed that the average ecological quality of the Pan-Third Pole region was at a moderate level of 0.53. Spatially, the excellent ecological quality regions were mainly concentrated in East Asian countries, while the severe quality regions were located in the Middle East. From 2000–2020, areas with improved ecological changes accounted for 38.48% of the total area, and 10.66% of the total area experienced a decline; specifically, European countries had a large proportion of improved ecological quality areas, while East Asian countries had a significantly larger proportion of declining ecological quality areas. We also found that ecosystem changes and human activities had an influence on ecological quality in the Pan-Third Pole region. This study provides an important empirical study on ecosystem services in the region.