Search Results (751)

With rapid global urbanization development, impermeable surface increase, urban population growth, building area expansion, and rising energy consumption, the urban heat island (UHI) effect is becoming increasingly serious. However, the spatial distribution of the UHI cannot be accurately extracted. Therefore, we focused on Luoyang City as the research area and combined the Getis-Ord-Gi* statistic and the greenest image to extract the UHI based on the Google Earth Engine using land surface temperature–spatial autocorrelation characteristics and seasonal changes in vegetation. As bare land considerably influenced the UHI extraction results, we combined the greenest image with the initial extraction results and applied the maximum normalized difference vegetation index threshold method to remove this effect on UHI distribution extraction, thereby achieving improved UHI extraction accuracy. Our results showed that the UHI of Luoyang continuously expanded outward, increasing from 361.69 km² in 2000 to 912.58 km² in 2023, with a continuous expansion rate of 22.95 km²/year. Furthermore, the urban area had a higher UHI area growth rate than the county area. Analysis indicates that the UHI effect in Luoyang has increased in parallel with the expansion of the building area. Intensive urban construction is a primary driver of this growth, directly exacerbating the UHI effect. Additionally, rising temperatures, population growth, and gross domestic product accumulation have collectively contributed to the ongoing expansion of this phenomenon. This study provides scientific guidance for future urban planning through the accurate extraction of the UHI effect, which promotes the development of sustainable human settlements. Full article

Forests play a crucial role in storing excess carbon released into the atmosphere. By mitigating climate change, forest carbon stocks play a vital role in achieving green transitions. However, limited information is available regarding the factors that affect forest carbon stocks. The primary objective of this analysis is to investigate the impact of green agricultural technologies and bioenergy on forest carbon stocks. The empirical investigation was conducted using the method of moments quantile regression (MMQR) technique. Results using the MMQR approach indicate that bioenergy is beneficial in augmenting forest carbon stores at all levels. A 1% increase in bioenergy is associated with an increase in forest carbon stocks ranging from 3.100 at the 10th quantile to 1.599 at the 90th quantile. In the context of developing economies, similar findings are observed; however, in developed economies, bioenergy only fosters forest carbon stocks at lower and middle quantiles. In contrast, green agricultural technologies have an adverse effect on forest carbon stocks. Green agricultural technologies have a significant negative impact on forest carbon stocks, particularly between the 10th and 80th quantiles, with their influence declining in magnitude from −2.398 to −0.619. This negative connection is observed in both developed and developing countries at most quantiles, except for higher quantiles in developed economies. Gross domestic product (GDP) has an adverse effect on forest carbon stores only in developing countries, whereas human capital diminishes forest carbon stocks in both developed and developing nations. Governments should provide support for the creators of bioenergy and agroforestry technologies so that forest carbon stocks can be increased. Full article

To investigate the spatiotemporal evolution characteristics and primary driving factors of Gross Primary Productivity (GPP) on the Qinghai-Tibet Plateau, we employed an enhanced MODIS-PSN model. Utilizing the fifth-generation global climate reanalysis dataset (ECMWF ERA5), we generated GPP remote sensing products by integrating six natural factors. Through correlation analysis and geographical detector modeling, we quantitatively analyzed the spatiotemporal dynamics and key drivers of vegetation GPP across the Qinghai-Tibet Plateau from 2001 to 2022. The results demonstrate that GPP changes across the Qinghai-Tibet Plateau display pronounced spatial heterogeneity. The humid northeastern and southeastern regions exhibit significantly positive change rates, primarily distributed across wetland and forest ecosystems, with a maximum mean annual change rate of 12.40 gC/m²/year. In contrast, the central and southern regions display a decreasing trend, with the minimum change rate reaching −1.61 gC/m²/year, predominantly concentrated in alpine grasslands and desert areas. Vegetation GPP on the Qinghai-Tibet Plateau shows significant correlations with temperature, vapor pressure deficit (VPD), evapotranspiration (ET), leaf area index (LAI), precipitation, and radiation. Among the factors analyzed, LAI demonstrates the strongest explanatory power for spatial variations in vegetation GPP across the Qinghai-Tibet Plateau. The dominant factors influencing vegetation GPP on the Qinghai-Tibet Plateau are LAI, ET, and precipitation. The pairwise interactions between these factors exhibit linear enhancement effects, demonstrating synergistic multifactor interactions. This study systematically analyzed the response mechanisms and variations of vegetation GPP to multiple driving factors across the Qinghai-Tibet Plateau from a spatial heterogeneity perspective. The findings provide both a critical theoretical framework and practical insights for better understanding ecosystem response dynamics and drought conditions on the plateau. Full article

Arid and semi-arid areas serve a critical regulatory function within the global carbon cycle. Understanding the response mechanisms of vegetation productivity to variations in moisture availability represents a fundamental scientific challenge in elucidating terrestrial carbon dynamics. This study systematically disentangled the respective influences of summer surface soil moisture (RSM) and precipitation (PRE) on gross primary productivity (GPP) across arid and semi-arid regions of China from 2000 to 2022. Utilizing GPP datasets alongside correlation analysis, ridge regression, and data binning techniques, the investigation yielded several key findings: (1) Both GPP and RSM exhibited significant upward trends within the study area, whereas precipitation showed no statistically significant trend; notably, GPP demonstrated the highest rate of increase at 0.455 Cg m⁻² a⁻¹. (2) Decoupling analysis indicated a coupled relationship between RSM and PRE; however, their individual effects on GPP were not merely a consequence of this coupling. Controlling for evapotranspiration and root-zone soil moisture interference, the analysis revealed that under conditions of elevated RSM, the average increase in summer–autumn GPP (SA_GPP) was 0.249, significantly surpassing the increase observed under high-PRE conditions (−0.088). Areas dominated by RSM accounted for 62.13% of the total study region. Furthermore, examination of the aridity gradient demonstrated that the predominance of RSM intensified with increasing aridity, reaching its peak influence in extremely arid zones. This research provides a quantitative assessment of the differential impacts of RSM and PRE on vegetation productivity in China’s arid and semi-arid areas, thereby offering a vital theoretical foundation for improving predictions of terrestrial carbon sink dynamics under future climate change scenarios. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Sun-induced chlorophyll fluorescence (SIF) is an important indicator of vegetation photosynthesis. While remote sensing enables large-scale monitoring of SIF, existing products face the challenge of trade-offs between temporal and spatial resolutions, limiting their applications. To select the optimal model for SIF data downscaling, we used a consistent dataset combined with vegetation physiological and meteorological parameters to evaluate four different regression methods in this study. The XGBoost model demonstrated the best performance during cross-validation (R² = 0.84, RMSE = 0.137 mW/m²/nm/sr) and was, therefore, selected to downscale GOME-2 SIF data. The resulting high-resolution SIF product (HRSIF) has a temporal resolution of 8 days and a spatial resolution of 0.05° × 0.05°. The downscaled product shows high fidelity to the original coarse SIF data when aggregated (correlation = 0.76). The reliability of the product was ensured through cross-validation with ground-based and satellite observations. Moreover, the finer spatial resolution of HRSIF better matches the footprint of eddy covariance flux towers, leading to a significant improvement in the correlation with tower-based gross primary productivity (GPP). Specifically, in the mixed forest vegetation type with the best performance, the R² increased from 0.66 to 0.85, representing an increase of 28%. This higher-precision product will support more effective ecosystem monitoring and research. Full article

Multiple studies, starting with Preston’s work in 1975, have suggested that gross domestic product per capita (GDPPC) is an important explanatory factor for understanding differentials in life expectancy at birth (LEB) in countries around the world. This proposition was tested in the present study using two-period cross-sectional regression across a large number of both advanced and developing countries and 16 socioeconomic factors, including GDPPC. The best-performing regression equations in the periods around 2000 and 2015 included four to six of these factors (government effectiveness, safe sanitation, poverty and contraception, plus, in the circa-2000 period, the Gini index and CO₂ emissions); perhaps surprisingly, these equations did not include GDPPC. The results were examined in greater detail for the world’s 15 most populous countries, helping to identify key drivers of LEB growth for each of these countries from circa 2000 to 2015. The fact that GDPPC drops out of the best equations calls into question the view that economic growth is the correct primary target for nations seeking to increase their average life expectancy. Full article

Global climate change has increased the frequency of compound drought–heatwave events (CDHEs), seriously threatening tropical forest ecosystems. However, due to the complex structure of natural tropical forests, related research remains limited. To address this, we focused on rubber plantations on Hainan Island, which have simpler structures, to explore the impacts of CDHEs on their primary productivity. We used Pearson and Spearman correlation analyses to select the optimal combination of drought and heatwave indices. Then, we constructed a Compound Drought–Heatwave Index (CDHI) using Copula functions to describe the temporal patterns of CDHEs. Finally, we applied a Bayes–Copula conditional probability model to estimate the probability of GPP loss under CDHE conditions. The main findings are as follows: (1) The Standardized Precipitation Evapotranspiration Index (SPEI-3) and Standardized Temperature Index (STI-1) formed the best index combination. (2) The CDHI successfully identified typical CDHEs in 2001, 2003–2005, 2010, 2015–2016, and 2020. (3) Temporally, CDHEs significantly increased the probability of GPP loss in April and May (0.58 and 0.64, respectively), while the rainy season showed a reverse trend due to water buffering (lowest in October, at 0.19). (4) Spatially, the northwest region showed higher GPP loss probabilities, likely due to topographic uplift. This study reveals how tropical plantations respond to compound climate extremes and provides theoretical support for the monitoring and management of tropical ecosystems. Full article

Understanding vegetation responses to atmospheric drought is critical for arid ecosystem management under climate change. However, the threshold of the response mechanism of grassland in arid regions to atmospheric drought remains unclear. This study investigates how vapor pressure deficit (VPD) regulates grassland gross primary productivity (GPP) in Xinjiang, China, using MODIS and other multi-source remote sensing data (2000–2020). The results show intensified atmospheric drought in central Tianshan Mountains and southern Junggar Basin, with VPD exhibiting a widespread increasing trend (significant increase: 15.75%, extremely significant increase: 4.68%). Intensified atmospheric drought occurred in the central Tianshan Mountains and southern Junggar Basin. Integrated analyses demonstrate that VPD has a dominant negative impact on GPP (path coefficient = −0.58, p < 0.05), primarily driven by atmospheric drought stress. A ridge regression-derived threshold was identified at 0.61 kPa, marking the point where VPD transitions from stimulating to suppressing productivity. Spatially, 58.75% of the total area showed a significant increase in GPP. These findings advance the mechanistic understanding of atmospheric drought impacts on arid ecosystems and inform adaptive grassland management strategies. Full article

Land surface models (LSMs) play a crucial role in climate prediction and carbon cycle assessment. To ensure their reliability, it is crucial to evaluate their performance in simulating key processes, such as evapotranspiration (ET) and gross primary productivity (GPP), across various temporal scales and vegetation types. This study systematically evaluates the performance of the newly modernized Noah-MP LSM version 5.0 in simulating water and carbon fluxes, specifically ET and GPP, across temporal scales ranging from half-hourly (capturing diurnal cycles) to annual using observational data from 105 sites within the globally FLUXNET2015 dataset. The results reveal that Noah-MP effectively captured the overall variability of both ET and GPP, particularly at short temporal scales. The model successfully simulated the diurnal and seasonal cycles of both fluxes, though cumulative errors increased at the annual scale. Diurnally, the largest simulation biases typically occurred around noon; while, seasonally, biases were smallest in winter. Performance varied significantly across vegetation types. For ET, the simulations were most accurate for open shrublands and deciduous broadleaf forests, while showing the largest deviation for woody savannas. Conversely, GPP simulations were most accurate for wetlands and closed shrublands, showing the largest deviation for evergreen broadleaf forests. Furthermore, an in-depth analysis stratified by the climate background revealed that ET simulations failed to capture inter-annual variability in the temperate and continental zones, while GPP was severely overestimated in arid and temperate climates. This study identifies the strengths and weaknesses of Noah-MP in simulating water and carbon fluxes, providing valuable insights for future model improvements. Full article

Salinity serves as a critical environmental factor influencing the physiological and morphological characteristics of Spirulina, a filamentous cyanobacterium used for food production and commercial purposes. This study examined a Spirulina strain’s responses to different salinity levels (10–45 ppt) through three independent laboratory experiments that determined growth, productivity, and size structure. Growth across salinity treatments was assessed by monitoring optical density in 24-well microplates over 20 days and estimating specific growth rates using a logistic growth model. Primary productivity under different salinity and light conditions was measured using light and dark bottle experiments to calculate gross primary productivity (GPP) and to estimate photosynthetic efficiency through linear regression of GPP against light intensity. The size structure was assessed through tube-based experiments and image analysis, with organism sizes categorized and analyzed to identify salinity-induced patterns in filament structure. The study demonstrated that the Spirulina strain achieved its greatest growth at 10 ppt yet produced the highest photosynthetic efficiency between 27 and 45 ppt because it reallocated energy during salinity stress. The morphological analysis revealed that the Spirulina strain produced medium-sized filaments between 400 and 799 µm at elevated salinity levels, and our analysis confirmed substantial variations in size structure. The Spirulina strain demonstrates both physiological and morphological plasticity when exposed to salinity changes. The cultivation of the Spirulina strain at 27 ppt provides conditions that support moderate growth, enhanced productivity, and manageable morphological shifts while using its natural salinity tolerance to improve the efficiency and scalability of production for diverse biotechnological applications. Full article

Abundant large organic aggregates, which form mucous webs up to a few decimeters in length, have been observed in Baía de Todos os Santos (BTS), northeastern Brazil. This communication presents preliminary results from field (February 2015) and laboratory (June 2015) experiments that aimed to determine preliminary values for respiration and near-maximum photosynthesis and the impact of macroaggregates on respiration rates. The experiments included the determination of respiration in controls, with the mechanical removal and addition of macroaggregates. The field experiment during a flood tide presented the lowest respiration rate (−7.0 ± 0.7 µM L⁻¹ d⁻¹), average net primary production (8.9 ± 4.5 µM L⁻¹ d⁻¹), and gross primary production (16.0 ± 10 µM L⁻¹ d⁻¹), with a ratio of gross primary production to respiration of 2.3. The control experiments during an ebb tide showed a mean respiration rate of 8.7 ± 2.3 µM L⁻¹ d⁻¹, whereas, after macroaggregate removal, this was 9.5 ± 4.5 µM L⁻¹ d⁻¹. In the laboratory experiments, the control sample respiration rate of 18.4 ± 1.4 µM L⁻¹ d⁻¹ was slightly increased to 20.6 ± 0.1 µM L⁻¹ d⁻¹ after aggregate removal. The addition of aggregates to the control sample increased the respiration rate by approximately 3-fold, to 56.5 ± 4.8 µM L⁻¹ d⁻¹. These results indicate that macroaggregates could have an important role in pelagic and benthic respiration, as well as in the whole bay’s metabolism. Full article

Vegetation photosynthesis is a key Earth system process that can fix carbon dioxide in the atmosphere. Mountainous areas usually have high productivity and extensive vegetation cover, but their study requires a higher spatiotemporal resolution due to the complex climate and vegetation variations with altitude. In this study, we analyzed the variations and climatic responses of vegetation gross primary productivity (GPP) in northwestern Hubei, China, at a 30 m spatial resolution from 2001 to 2020, based on the fusion of multi-source remote sensing data. A GPP estimation framework based on the CASA model was applied, and spatiotemporal fusion of Landsat and MODIS data was achieved using the STNLFFM algorithm. The results indicate that GPP exhibits higher values in the mountainous regions of west Shennongjia, compared to the eastern plain regions, with a generally increasing trend with increasing elevation. GPP has shown an overall increasing trend over the past 20 years, with almost 90% of the high-elevation regions showing an increasing trend, and the low-elevation regions showing an opposite trend. The relationship between GPP and climate factors is greatly impacted by the temporal scale, with the most pronounced correlation at a seasonal scale. The impact of temperature has been generally stable over the past 20 years across different altitudes, while the relationship with precipitation has exhibited an overall decreasing trend with the increase of altitude. Precipitation and temperature correlations show opposing variations in different months and elevations, which can be mainly attributed to the varied climatic conditions in the different elevations. Full article

Understanding the spatiotemporal patterns of cropland carbon and carbon water use efficiency (CWUE) and its driving factors is essential for sustainable agricultural development. Based on a multi-source remote sensing dataset, this study applies a trend analysis (Sen + Mann–Kendall), a dual-type randomized extraction algorithm, and an optimized XGBoost model to examine the spatiotemporal variations in cropland CWUE, including the water use efficiency of net primary production (WUE_NPP), water use efficiency of gross primary production (WUE_GPP), and carbon use efficiency (CUE) in Henan Province from 2001 to 2019. This study further quantifies the impact of irrigation on the cropland CWUE and explores the synergistic effects of its driving factors in irrigated areas. Results reveal significant regional differences in cropland CWUE across Henan Province. Higher multi-year average values of CUE and WUE_NPP were observed in the western region, while the WUE_GPP was more prominent in the south-central region. Over 76% of cropland areas showed a general downward trend in three indicators, with significant interannual declines. Non-irrigated cropland exhibited higher CWUE values than irrigated ones. The average values over multiple years of the WUE_GPP, WUE_NPP, and CUE of irrigated cropland were 2.51 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, 1.08 ${\mathrm{g} \mathrm{C} \mathrm{m}}^{-2}{ \mathrm{mm}}^{-1}$, and 0.43, respectively. Sunlight was the dominant factor influencing the WUE_GPP in irrigated areas, while precipitation primarily regulated the WUE_NPP and CUE. The influence of the gross domestic product (GDP) was found to be minimal. Notably, both the leaf area index (LAI) and precipitation exhibited a shift from a positive to negative influence on CUE once their values exceeded optimal thresholds, indicating that resource overabundance can lead to physiological limitations. This study offers valuable insights into how irrigated cropland responds to the combined effects of multiple environmental and socio-economic drivers. Full article

Forest–atmosphere interactions through mass and energy fluxes significantly influence climate processes. However, due to anthropogenic actions, native Araucaria forests in southern Brazil, part of the Atlantic Forest biome, have been drastically reduced. This study quantifies CO₂ and energy flux contributions from each forest stratum to improve understanding of surface–atmosphere interactions. Eddy covariance data from November 2009 to April 2012 were used to assess fluxes in an Araucaria forest in Paraná, Brazil, across the ecosystem, understory, and overstory strata. On average, the ecosystem acts as a carbon sink of −298.96 g C m⁻² yr⁻¹, with absorption doubling in spring–summer compared to autumn–winter. The understory primarily acts as a source, while the overstory functions as a CO₂ sink, driving carbon absorption. The overstory contributes 63% of the gross primary production (GPP) and 75% of the latent heat flux, while the understory accounts for 94% of the ecosystem respiration (RE). The energy fluxes exhibited marked seasonality, with higher latent and sensible heat fluxes in summer, with sensible heat predominantly originating from the overstory. Annual ecosystem evapotranspiration reaches 1010 mm yr⁻¹: 60% of annual precipitation. Water-use efficiency is 2.85 g C kgH₂O⁻¹, with higher values in autumn–winter and in the understory. The influence of meteorological variables on the fluxes was analyzed across different scales and forest strata, showing that solar radiation is the main driver of daily fluxes, while air temperature and vapor pressure deficit are more relevant at monthly scales. This study highlights the overstory’s dominant role in carbon absorption and energy fluxes, reinforcing the need to preserve these ecosystems for their crucial contributions to climate regulation and water-use efficiency. Full article