Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

As a buffer layer for the energy and water exchange between atmosphere and permafrost, the active layer is sensitive to climate warming. Changes in the thermal state in active layer can alter soil organic carbon (SOC) dynamics. It is critical to identify the response of soil microbial communities to warming to better predict the regional carbon cycle under the background of global warming. Here, the active layer soils collected from a wetland-forest ecotone in the continuous permafrost region of Northeastern China were incubated at 5 and 15°C for 45 days. High-throughput sequencing of the 16S rRNA gene was used to examine the response of bacterial community structure to experimental warming. A total of 4148 OTUs were identified, which followed the order 15°C > 5°C > pre-incubated. Incubation temperature, soil layer and their interaction have significant effects on bacterial alpha diversity (Chao index). Bacterial communities under different temperature were clearly distinguished. Chloroflexi, Actinobacteria, Proteobacteria, and Acidobacteria accounted for more than 80% of the community abundance at the phylum level. Warming decreased the relative abundance of Chloroflexi and Acidobacteria, while Actinobacteria and Proteobacteria exhibited increasing trend. At family level, the abundance of norank_o__norank_c__AD3 and Ktedonobacteraceae decreased significantly with the increase of temperature, while Micrococcaccac increased. In addition, the amount of SOC mineralization were positively correlated with the relative abundances of most bacterial phyla and SOC content. SOC content was positively correlated with the relative abundance of most bacterial phyla. Results indicate that the SOC content was the primary explanatory variable and driver of microbial regulation for SOC mineralization. Our results provide a new perspective for understanding the microbial mechanisms that accelerates SOC decomposition under warming conditions in the forest-wetland ecotone of permafrost region.

Climate warming can affect freeze–thaw cycle (FTCs) patterns in northern high-latitude regions and may affect permafrost carbon emissions. The response of carbon release and microbial communities to FTCs has not been well characterized. Here, we conducted laboratory incubation experiments to investigate the relationships among carbon emissions, bacterial community, and soil variables in a permafrost-affected forest–wetland ecotone in Northeast China. The emission rates of CO₂ and CH₄ increased during the FTCs. FTC amplitude, FTC frequency, and patch type had significant effects on carbon emissions. FTCs increased the contents of soil DOC, NH₄⁺-N, and NO₃⁻-N but reduced bacterial alpha diversity. CO₂ emissions were mainly affected by bacterial alpha diversity and composition, and the inorganic nitrogen content was the important factor affecting CH₄ emissions. Our findings indicated that FTCs could significantly regulate CO₂ and CH₄ emissions by reducing bacterial community diversity and increasing the concentration of available soil substrates. Our findings shed new light on the microorganism-substrate mechanisms regulating the response patterns of the soil carbon cycle to FTCs in permafrost regions.

Land surface temperature (LST) is a crucial parameter driving the dynamics of the thermal state on land surface. In high-latitude cold region, a long-term, stable LST product is of great importance in examining the distribution and degradation of permafrost under pressure of global warming. In this study, a generalized additive model (GAM) approach was developed to fill the missing pixels of the MODIS/Terra 8-day Land Surface Temperature (MODIS LST) daytime products with the ERA5 Land Skin Temperature (ERA5ST) dataset in a high-latitude watershed in Eurasia. Comparison at valid pixels revealed that the MODIS LST was 4.8–13.0 °C higher than ERA5ST, which varies with land covers and seasons. The GAM models fairly explained the LST differences between the two products from multiple covariates including satellite-extracted environmental variables (i.e., normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and normalized difference snow index (NDSI) as well as locational information. Considering the dramatic seasonal variation of vegetation and frequent snow in the cold region, the gap-filling was conducted in two seasons. The results revealed the root mean square errors (RMSE) of 2.7 °C and 3.4 °C between the valid MODIS LST and GAM-simulated LST data in the growing season and snowing season, respectively. By including the satellite-extracted land surface information in the GAM model, localized variations of land surface temperature that are often lost in the reanalysis data were effectively compensated. Specifically, land surface wetness (NDWI) was found to be the greatest contributor to explaining the differences between the two products. Vegetation (NDVI) was useful in the growing season and snow cover (NDSI) cannot be ignored in the snow season of the study region. The km-scale gap-filled MODIS LST products provide spatially and temporally continuous details that are useful for monitoring permafrost degradation in cold regions in scenarios of global climate change.

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing’an permafrost map (up to 1 km²), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001–2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing’an permafrost distribution simulated by the SFN model and TTOP model were 6.88 × 10⁵ km² and 6.81 × 10⁵ km², respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

Permafrost in northeast China, which is at the southern edge of the high-latitude permafrost belt in Eurasia, is extremely sensitive to climate warming. However, the distribution of permafrost in the region in recent years has been poorly studied, and there is a lack of understanding of the relative importance of environmental factors affecting the region. Based on observed ground surface temperature (GST) data, this study quantifies changes in the permafrost area in northeast China from 1982 to 2020 using a surface frost number model, and the influencing factors are identified based on dominance analysis and spatial correlation analysis. The results suggest that the permafrost in northeast China during the observation period underwent degradation with a degradation rate of 0.33 × 10⁴ km²/a. In addition, the permafrost degradation also exhibited altitudinal and latitudinal zonality. Permafrost degradation under typical grassland, deciduous forest, and savannah cover was more significant than that under evergreen forest, mixed forest, and shrubbery cover. As revealed by the dominance analysis results, the annual average snow cover, annual average snow depth and annual average normalized difference vegetation index (NDVI) had the largest contributions to the variance of the permafrost area in northeast China, accounting for 88.3% of the total variance contribution of the six influencing factors. The spatial correlation results reveal that areas with a significantly increased NDVI and significantly reduced snow depth and snow cover were coincident with areas with significantly degraded permafrost. Hence, the snow cover, snow depth, and NDVI were found to have the greatest influence on the permafrost distribution in northeast China. The results of this study evidently increase the understanding of the changing permafrost in northeast China, providing important knowledge about permafrost for researchers and the related community.