The evolution of land use patterns and the emergence of urban heat islands (UHI) over time are critical issues in city development strategies. This study aims to establish a model that maps the correlation between changes in land use and land surface temperature (LST) in the Mashhad City, northeastern Iran. Employing the Google Earth Engine (GEE) platform, we calculated the LST and extracted land use maps from 1985 to 2020. The convolutional neural network (CNN) approach was utilized to deeply explore the relationship between the LST and land use. The obtained results were compared with the standard machine learning (ML) methods such as support vector machine (SVM), random forest (RF), and linear regression. The results revealed a 1.00°C-2.00°C increase in the LST across various land use categories. This variation in temperature increases across different land use types suggested that, in addition to global warming and climatic changes, temperature rise was strongly influenced by land use changes. The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C, while forest lands experienced the smallest increase of 1.19°C. The developed CNN demonstrated an overall prediction accuracy of 91.60%, significantly outperforming linear regression and standard ML methods, due to the ability to extract higher level features. Furthermore, the deep neural network (DNN) modeling indicated that the urban lands, comprising 69.57% and 71.34% of the studied area, were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030, respectively. In conclusion, the LST predictioin framework, combining the GEE platform and CNN method, provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

Increasing concerns regarding aquatic ecological health and eutrophication driven by urbanization and human activities have highlighted the need to understand primary productivity (PP) dynamics in aquatic ecosystems. This study investigated the spatial distribution of PP across the Weihe River Basin, China using inverse distance weighting and analyzed the influence of different land uses and water physical-chemical parameters on PP using Mantel test and Spearman analysis. Significantly spatial heterogeneity in PP concentrations, ranging from 0.458 to 3262.807 mg C/(m²•d), was observed with high-PP sites clustered in the middle-lower reaches dominated by farmland-construction land mosaics. Core drivers included light availability (Secchi depth and sunlight duration) and phytoplankton biomass (chlorophyll-a (Chl-a)), while water temperature exhibited threshold-dependent effects. Total organic carbon played dual roles, promoting PP concentrations in low-Chl-a regions, but suppressing it under high-Chl-a regions. Dual-scale buffer analysis (500 and 1000 m buffer zones) revealed PP heterogeneity stemed from interactive land use configurations, rather than isolated types. Balanced construction land-to-farmland ratio (0.467-2.890) elevated PP concentrations in human-dominated basins (the main stem of the Weihe River and Jinghe River), whereas excessive agricultural homogenization reduced PP likely due to fertilizer saturation and algal self-shading. Ecologically sensitive basins (the Beiluohe River Basin) demonstrated distinct patterns, in which PP concentration was regulated through natural-agricultural synergies. These results deepened the understanding of land use effects on aquatic PP, providing a theoretical basis for optimizing land use strategies to reconcile eutrophication control with ecological productivity in human-stressed basins.

The Erenhot-Huailai zone, as an important dust emission source area in northern China, affects the air quality of Beijing City, Tianjin City, and Hebei Province and human activities in this zone have a profound impact on surface dust emission. In order to explore the main source areas of surface dust emission and quantify the impacts of human activities on surface dust emission, we investigated the surface dust emission of different land types on the Erenhot-Huailai zone by model simulation, field observation, and comparative analysis. The results showed that the average annual inhalable atmospheric particles (PM₁₀) dust emission fluxes in arid grassland, Hunshandake Sandy Land, semi-arid grassland, semi-arid agro-pastoral area, dry sub-humid agro-pastoral area, and semi-humid agro-pastoral area were 4.41, 0.71, 3.64, 1.94, 0.24, and 0.14 t/hm², respectively, and dust emission in these lands occurred mainly from April to May. Due to the influence of human activities on surface dust emission, dust emission fluxes from different land types were 1.66-4.41 times greater than those of their background areas, and dust emission fluxes from the main dust source areas were 1.66-3.89 times greater than those of their background areas. According to calculation, the amount of PM₁₀ dust emission influenced by human disturbance accounted for up to 58.00% of the total dust emission in the study area. In addition, the comparative analysis of model simulation and field observation results showed that the simulated and observed dust emission fluxes were relatively close to each other, with differences ranging from 0.01 to 0.21 t/hm² in different months, which indicated that the community land model version 4.5 (CLM4.5) had a high accuracy. In conclusion, model simulation results have important reference significance for identifying dust source areas and quantifying the contribution of human activities to surface dust emission.

Amid global climate change, rising levels of nitrogen (N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation (a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange (NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model (SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE. However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.

The net primary productivity (NPP) is an important indicator for assessing the carbon sequestration capacities of different ecosystems and plays a crucial role in the global biosphere carbon cycle. However, in the context of the increasing frequency, intensity, and duration of global extreme climate events, the impacts of extreme climate and vegetation phenology on NPP are still unclear, especially on the Qinghai-Xizang Plateau (QXP), China. In this study, we used a new data fusion method based on the MOD13A2 normalized difference vegetation index (NDVI) and the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI_3g datasets to obtain a NDVI dataset (1982-2020) on the QXP. Then, we developed a NPP dataset across the QXP using the Carnegie-Ames-Stanford Approach (CASA) model and validated its applicability based on gauged NPP data. Subsequently, we calculated 18 extreme climate indices based on the CN05.1 dataset, and extracted the length of vegetation growing season using the threshold method and double logistic model based on the annual NDVI time series. Finally, we explored the spatiotemporal patterns of NPP on the QXP and the impact mechanisms of extreme climate and the length of vegetation growing season on NPP. The results indicated that the estimated NPP exhibited good applicability. Specifically, the correlation coefficient, relative bias, mean error, and root mean square error between the estimated NPP and gauged NPP were 0.76, 0.17, 52.89 g C/(m²•a), and 217.52 g C/(m²•a), respectively. The NPP of alpine meadow, alpine steppe, forest, and main ecosystem on the QXP mainly exhibited an increasing trend during 1982-2020, with rates of 0.35, 0.38, 1.40, and 0.48 g C/(m²•a), respectively. Spatially, the NPP gradually decreased from southeast to northwest across the QXP. Extreme climate had greater impact on NPP than the length of vegetation growing season on the QXP. Specifically, the increase in extremely-wet-day precipitation (R99p), simple daily intensity index (SDII), and hottest day (TXx) increased the NPP in different ecosystems across the QXP, while the increases in the cold spell duration index (CSDI) and warm spell duration index (WSDI) decreased the NPP in these ecosystems. The results of this study provide a scientific basis for relevant departments to formulate future policies addressing the impact of extreme climate on vegetation in different ecosystems on the QXP.

In recent decades, global climate change and overgrazing have led to severe degradation of alpine meadows. Understanding the changes in soil characteristics and vegetation communities in alpine meadows with different degrees of degradation is helpful to reveal the mechanism of degradation process and take the remediation measures effectively. This study analyzed the changes in vegetation types and soil characteristics and their interrelationships under three degradation degrees, i.e., non-degradation (ND), moderate degradation (MD), and severe degradation (SD) in the alpine meadows of northeastern Qinghai-Xizang Plateau, China through the long-term observation. Results showed that the aggressive degradation changed the plant species, with the vegetation altering from leguminous and gramineous to forbs and harmful grasses. The Pielou evenness and Simpson index increased by 24.58% and 7.01%, respectively, the Shannon-Wiener index decreased by 17.52%, and the species richness index remained constant. Soil conductivity, soil organic matter, total potassium, available potassium, and porosity declined. However, the number of vegetation species increased in MD. Compared with ND, the plant diversity in MD enhanced by 8.33%, 8.69%, and 7.41% at family, genus, and species levels, respectively. In conclusion, changes in soil properties due to degradation can significantly influence the condition of above-ground vegetation. Plant diversity increases, which improves the structure of belowground network. These findings may contribute to designing better protection measures of alpine meadows against global climate change and overgrazing.

Under current climate warming, the growth resilience of plantation forests after extreme droughts has garnered increasing attention. Platycladus orientalis Linn. is an evergreen tree species commonly used for afforestation, and the stability of P. orientalis plantation forests in the Loess Hilly region directly affects the ecological and environmental security of the entire Loess Plateau of China. However, systematic analyses of the growth resilience of P. orientalis plantation forests after extreme droughts along precipitation gradients remain scarce. In this study, we collected tree ring samples of P. orientalis along a precipitation gradient (255, 400, and 517 mm) from 2021 to 2023 and used dendroecological methods to explore the growth resilience of P. orientalis to drought stress on the Loess Plateau. Our findings revealed that the growth resilience of P. orientalis increased with increasing precipitation, enabling the trees to recover to the pre-drought growth levels. In regions with low precipitation (255 mm), the plantation forests were more sensitive to extreme droughts, struggling to recover to previous growth levels, necessitating conditional artificial irrigation. In regions with medium precipitation (400 mm), the growth of P. orientalis was significantly limited by drought stress and exhibited some recovery ability after extreme droughts, therefore warranting management through rainwater harvesting and conservation measures. Conversely, in regions with high precipitation (517 mm), the impacts of extreme droughts on P. orientalis plantation forests were relatively minor. This study underscored the need for targeted strategies tailored to different precipitation conditions rather than a "one-size-fits-all" approach to utilize precipitation resources effectively and maximize the ecological benefits of plantation forests. The findings will help maintain the stability of plantation forests and improve their ecosystem service functions in arid and semi-arid areas.

In floristic research, the grid mapping method is a crucial and highly effective tool for investigating the flora of specific regions. This methodology aids in the collection of comprehensive data, thereby promoting a thorough understanding of regional plant diversity. This paper presents findings from a grid mapping study conducted in the Surkhan-Sherabad botanical-geographic region (SShBGR), acknowledged as one of the major floristic areas in southwestern Uzbekistan. Using an expansive dataset of 14,317 records comprised of herbarium specimens and field diary entries collected from 1897 to 2023, we evaluated the stages and seasonal dynamics of data accumulation, species richness (SR), and collection density (CD) within 5 km×5 km grid cells. We further examined the taxonomic and life form composition of the region's flora. Our analysis revealed that the grid mapping phase (2021-2023) produced a significantly greater volume of specimens and taxonomic diversity compared with other periods (1897-1940, 1941-1993, and 1994-2020). Field research spanned 206 grid cells during 2021-2023, resulting in 11,883 samples, including 6469 herbarium specimens and 5414 field records. Overall, fieldwork covered 251 of the 253 grid cells within the SShBGR. Notably, the highest species diversity was documented in the B198 grid cell, recording 160 species. In terms of collection density, the E198 grid cell produced 475 samples. Overall, we identified 1053 species distributed across 439 genera and 78 families in the SShBGR. The flora of this region aligned significantly with the dominant families commonly found in the Holarctic, highlighting vital ecological connections. Among our findings, the Asteraceae family was the most polymorphic, with 147 species, followed by the continually stable and diverse Poaceae, Fabaceae, Brassicaceae, and Amaranthaceae. Besides, our analysis revealed a predominance of therophyte life forms, which constituted 52% (552 species) of the total flora. The findings underscore the necessity for continual data collection efforts to further enhance our understanding of the biodiversity in the SShBGR. The results of this study demonstrated that the application of grid-based mapping in floristic studies proves to be an effective tool for assessing biodiversity and identifying key taxonomic groups.