Urban rainstorm waterlogging has become a typical “city disease” in China. It can result in a huge loss of social economy and personal property, accordingly hindering the sustainable development of a city. Impervious surface expansion, especially the irregular spatial pattern of impervious surfaces, derived from rapid urbanization processes has been proven to be one of the main influential factors behind urban waterlogging. Therefore, optimizing the spatial pattern of impervious surfaces through urban renewal is an effective channel through which to attenuate urban waterlogging risk for developed urban areas. However, the most important step for the optimization of the spatial pattern of impervious surfaces is to understand the mechanism of the impact of urbanization processes, especially the spatiotemporal pattern of impervious surfaces, on urban waterlogging. This research aims to elucidate the mechanism of urbanization’s impact on waterlogging by analysing the spatiotemporal characteristics and variance of urban waterlogging affected by urban impervious surfaces in a case study of Guangzhou in China. First, the study area was divided into runoff plots by means of the hydrologic analysis method, based on which the analysis of spatiotemporal variance was carried out. Then, due to the heterogeneity of urban impervious surface effects on waterlogging, a geographically weighted regression (GWR) model was utilized to assess the spatiotemporal variance of the impact of impervious surface expansion on urban rainstorm waterlogging during the period from the 1990s to the 2010s. The results reveal that urban rainstorm waterlogging significantly expanded in a dense and circular layer surrounding the city centre, similar to the impervious surface expansion affected by urbanization policies. Taking the urban runoff plot as the research unit, GWR has achieved a good modelling effect for urban storm waterlogging. The results show that the impervious surfaces in the runoff plots of the southeastern part of Yuexiu, the southern part of Tianhe and the western part of Haizhu, which have experienced major urban engineering construction, have the strongest correlation with urban rainstorm waterlogging. However, for different runoff plots, the impact of impervious surfaces on urban waterlogging is quite different, as there exist other influence factors in the various runoff plots, although the impervious surface is one of the main factors. This result means that urban renewal strategy to optimize the spatial pattern of impervious surfaces for urban rainstorm waterlogging prevention and control should be different for different runoff plots. The results of the GWR model analysis can provide useful information for urban renewal strategy-making.

With the rapid expansion of impervious surfaces, urban waterlogging has become a typical “urban disease” in China, seriously hindering the sustainable development of cities. Therefore, reducing the impact of impervious surfaces on surface runoff is an effective approach to alleviate urban waterlogging. Presently, the development mode of many cities in China has shifted from an increase in urban scale to the improvement of urban quality through urban renewal, which is the current and future development path for most cities. Optimizing the design of impervious surfaces in urban renewal planning to reduce its impact on surface runoff is an important way to prevent and control urban waterlogging. The aim of this research is to construct an optimization model of impervious surface space layout under the framework of a geographic simulation technology-integrated ant colony optimization (ACO) and Soil Conservation Service curve number (SCS-CN) model (ACO-SCS) in a case study of Guangzhou in China. Urban runoff plots in the study area are divided according to the area of the urban planning unit. With the goal of minimizing the runoff coefficient, the optimal space layout of the impervious surfaces is obtained, which provides a technical method and reference for urban waterlogging prevention and control through urban renewal planning. The results reveal that the optimization of impervious surface space layout through ACO-SCS achieves a satisfactory effect with an average optimization rate of 9.52%, and a maximum optimization rate of 33.16%. The research also shows that the initial impervious surface layout is the key influencing factor in ACO-SCS. In the urban renewal planning stage, the space layout of the impervious surfaces with a high–low–high density discontinuous connection can be constructed by transforming medium-density impervious surfaces into low-density impervious surfaces to achieve the flat and long-type agglomeration of the low-density and high-density impervious surfaces, which can effectively reduce the influence of urban development on surface runoff. There is spatial heterogeneity of the optimal results in different urban runoff plots. Therefore, the policy of urban renewal planning for urban waterlogging prevention and control should be different. The optimized results of impervious surface space layout provide useful reference information for urban renewal planning.

The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. The use of machine learning (ML) methods to assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML‐based wildfire susceptibility assessments overly focus on spatially evaluating the disaster threat, while ignoring the potential threats of wildfires to local ecosystems. This situation makes it difficult to determine seasonal variations in wildfire susceptibility and limits the value of assessment results. We present a framework to assess wildfire susceptibility and wildfire threats seasonally to local ecosystems. The ecosystem service value (ESV) was used as a proxy for the economic value of an ecosystem, the random forest algorithm was used to evaluate wildfire susceptibility, and the Daxinganling region, the largest forested area in China, was selected as the study area, and the dynamic equivalent coefficient factor method was used to calculate the ESV of each cell. Our main findings were as follows: (a) wildfire susceptibility exhibited obvious disparities in terms of spatial distribution across the four seasons; (b) each ecosystem in the study area faced a different magnitude of wildfire disturbance; and (c) the expected ESV loss (USD 10.8 billion) due to wildfires was much higher than the region’s total GDP (USD 2 billion) in 2019. This study was repeatable, and all data required were obtained freely. The methodologies used can be applied directly to other regions. Our study will be of particular interest to developing counties where intensive wildfire monitoring is limited.

The surface urban heat islands (SUHI) of urban agglomeration has always been an important topic in the studies of urban heat islands (UHI), especially with the development of satellite-based land surface temperature (LST) products. However, most studies are limited to the perspective of a single city, ignoring the impact of urban agglomeration and the changes of LST at day and night on the reference LST (RLST) (e.g., rural areas). Consequently, this paper proposed a novel method about SUHI intensity estimation for the multi-centers in urban agglomeration (mcSUHII) using nighttime light (NTL) data. The mcSUHII method took the RLST of SUHII estimation account of multicenter structure, and was more flexible to adapt the impact of human activity intensity. The proposed method was applied to the urban agglomeration in Guangdong-Hong Kong-Macao Greater Bay Area (GHMBay) using DMSP/OLS in October, 2010. The study showed that compared with other RLSTs such as suburban and forest, mcSUHII mitigates the underestimation bias caused by ignoring the multicenter structure. Moreover, it was illustrated that the variation of SUHII presented an obvious inverted U-shape along the gradient from the inland to the delta and the coastal cities. The delta cities presented the highest SUHII at day and night (approximately ~7.27±1.71°C, and ~4.46±1.42°C, respectively). Additionally, the Geo-detector illustrated that NTL served as the dominator together with other factors which were capable of explaining more than 90% of the spatial variation in SUHII in GHMBay. Therefore, considering multi-centers more in estimation of SUHII of urban agglomeration for the sustainable development.

For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manual setting of conditions or thresholds and sometimes encounter special cases that make it difficult to directly match certain rules or integrate different rules together. An alternative method is using a data-driven strategy under AI technology background to simulate cartographer behaviors through typical sample training, such as deep learning. The integration of cartography domain knowledge and deep learning is a better choice to settle generalization decisions. This study uses a combination of domain knowledge and a data-driven approach to introduce graph neural networks into point cluster generalization. First, we construct a virtual graph structure of point clusters using Delaunay triangulation, secondly, we extract spatial features, contextual features, and attributes of each point separately, and then propose a generalization model based on the TAGCN network. Finally, this model is trained with the manually generalized sample to realize the automatic point cluster generalization. The results demonstrate that the proposed model is valid and efficient for point cluster generalization and that this algorithm can better maintain various characteristics of the point cluster in both the local area and the overall map compared to other methods.