Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE.

The development of urbanization has changed the original land cover and exacerbated the urban heat island effect, seriously affecting the sustainable development of the ecological environment. Research on urban heat island characteristics and land cover changes in five major urban agglomerations in China to provide a reference for preventing thermal environmental risks and urban agglomeration construction planning. This paper estimates the surface urban heat island intensity (SUHII) of the five major urban agglomerations in China from 2003 to 2019 based on Google Earth Engine (GEE) through the urban-rural dichotomy, analyzes their trends through the Sen + M-K trend analysis method, and combines the detrending rate matrix to analyze the impact of land cover type shift on urban heat island change. Research shows that (1) the land cover types of the five major urban agglomerations in China have changed considerably from 2003 to 2019, and all five major urban agglomerations in China experienced varying degrees of urban expansion. (2) The annual average value of SUHII decreases in Beijing-Tianjin-Hebei, Yangtze River Delta, and middle reaches of the urban agglomerations, while the annual average value of SUHII increases in Chengdu-Chongqing and Pearl River Delta urban agglomerations. (3) The spatial composition of land cover types in the five major urban agglomerations in China is highly spatially correlated with urban heat islands, with urban land and bare land urban heat islands being the most pronounced. (4) The land cover type shift has the most significant heat island impact on Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengdu-Chongqing urban agglomerations. (5) The land cover change (LCC) with an increasing trend in SUHII is mainly bare land converted to arable land, and water bodies, grassland, forest land, and arable land converted to urban land.

Prediction of Spatiotemporal Invasive Risk of the Red Import Fire Ant, Solenopsis invicta (Hymenoptera: Formicidae), in China.

The red imported fire ant, Solenopsis invicta (Hymenoptera: Formicidae), is an invasive pest, and it has spread rapidly all over the world. Predicting the suitable area of S. invicta growth in China will provide a reference that will allow for its invasion to be curbed. In this study, based on the 354 geographical distribution records of S. invicta, combined with 24 environmental factors, the suitable areas of S. invicta growth in China under current (2000s) and future (2030s and 2050s) climate scenarios (SSPs1-2.5s, SSPs2-3.5s and SSPs5-8.5s) were predicted by using the optimized MaxEnt model and geo-detector model. An iterative algorithm and knife-cut test were used to evaluate the important environmental factors that restrict the suitable area under the current climatic conditions. This study also used the response curve to determine the appropriate value of environmental factors to further predict the change and the center of gravity transfer of the suitable area under climate change. The optimized MaxEnt model has high prediction accuracy, and the working curve area (AUC) of the subjects is 0.974. Under climatic conditions, the suitable area is 81.37 × 104 km2 in size and is mainly located in the south and southeast of China. The main environmental factors affecting the suitable area are temperature (Bio1, Bio6, and Bio9), precipitation (Bio12 and Bio14) and NDVI. In future climate change scenarios, the total suitable area will spread to higher latitudes. This distribution will provide an important theoretical basis for relevant departments to rapidly prevent and control the invasion of S. invicta.

Predicting the Distribution of the Invasive Species Leptocybe invasa: Combining MaxEnt and Geodetector Models.

Leptocybe invasa is a globally invasive pest of eucalyptus plantations, and is steadily spread throughout China. Predicting the growth area of L. invasa in China is beneficial to the establishment of early monitoring, forecasting, and prevention of this pest. Based on 194 valid data points and 21 environmental factors of L. invasa in China, this study simulated the potential distribution area of L. invasa in China under three current and future climate scenarios (SSPs1-2.5, SSPs2-3.5, and SSPs5-8.5) via the MaxEnt model. The study used the species distribution model (SDM) toolbox in ArcGIS software to analyze the potential distribution range and change of L. invasa. The importance of crucial climate factors was evaluated by total contribution rate, knife-cut method, and environmental variable response curve, and the area under the receiver operating characteristic (ROC) curve was used to test and evaluate the accuracy of the model. The results showed that the simulation effect of the MaxEnt model is excellent (area under the ROC curve (AUC) = 0.982,). The prediction showed that L. invasa is mainly distributed in Guangxi, Guangdong, Hainan, and surrounding provinces, which is consistent with the current actual distribution range. The distribution area of the potential high fitness zone of L. invasa in the next three scenarios increases by between 37.37% and 95.20% compared with the current distribution. Climate change affects the distribution of L. invasa, with the annual average temperature, the lowest temperature of the coldest month, the average temperature of the driest season, the average temperature of the coldest month, and the precipitation in the wettest season the most important. In the future, the core areas of the potential distribution of L. invasa in China will be located in Yunnan, Guangxi, Guangdong, and Hainan. They tend to spread to high latitudes (Hubei, Anhui, Zhejiang, Jiangsu, and other regions).