Search

As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high-level visual tasks, such as object detection, tracking, and object segmentation. In recent years, neural network-based dehazing methods have achieved notable success, with a growing number of Transformer-based dehazing approaches being proposed. Up to now, there is a lack of comprehensive review that thoroughly analyzes Transformer-based image dehazing algorithms. To fill this gap, this paper comprehensively sorts out Transformer-based daytime, nighttime and remote sensing image dehazing algorithms, which not only covers the fundamental principles of various types of dehazing algorithms, but also explores the applicability and performance of these algorithms in different scenarios. In addition, the commonly used datasets and evaluation metrics in image dehazing tasks are introduced. On this basis, analysis of the performance of existing representative dehazing algorithms is carried out from both quantitative and qualitative perspectives, and the performance of typical dehazing algorithms in terms of dehazing effect, operation speed, resource consumption is compared. Finally, the application scenarios of image dehazing technology are summarized, and the challenges and future development directions in the field of image dehazing are analyzed and prospected.

Forest evapotranspiration (ET) is one of the most important factors influencing the terrestrial hydrological cycle and is prone to being influenced by increasing drought events. This highlights the need to understand the interaction between global forest ET and drought. Consequently, we drew 710 sets of ecosystem-scale ET observations from 69 forest sites around the world and then systematically assessed the ET anomalies during droughts across the dominant forest and climate types. Overall, the response of forest ET to drought is non-monotonic. Under severe and extreme droughts with long durations, ET was reduced in most of forests in the world. We attributed the decreased forest ET to both restricted water supply and restricted water consumption by forests; however, lower magnitude and short-term droughts generally increased forest ET, and in some cases, ET even exceeded precipitation during droughts. We attributed this to the increased surface evaporation and the utilization of deep water by deep root systems. Specifically, we find that the positive anomaly of ET under droughts implied the drought paradox, which acts to accelerate the terrestrial hydrological cycle and further amplify the drought. This study as a global synthetic analysis of case studies on the site scale may have great potential for understanding the importance of the drought-modulated forest water cycle and the possibility of increasing drought stress with the effects of the drought paradox. Key words: climate change, water balance, forest hydrology, drought paradox, evapotranspiration. L'evapotranspiration (ET) de la foret est un des plus importants facteurs qui influencent le cycle hydrologique terrestre tout en etant sujette a l'influence des episodes de secheresse de plus en plus frequents. Cela souligne la necessite de comprendre l'interaction entre l'ET globale de la foret et la secheresse. Par consequent, nous avons extrait 710 ensembles d'observations d'ET a l'echelle de l'ecosysteme dans 69 stations forestieres partout dans le monde et nous avons ensuite systematiquement evalue les anomalies de l'ET lors de secheresses parmi les types dominants de forets et de climats. Globalement, la reaction de l'ET de la foret a la secheresse n'est pas monotone. Lors de secheresses severes et extremes qui persistent longtemps, l'ET a diminue dans la plupart des forets dans le monde. Nous avons attribue la diminution de l'ET de la foret a l'approvisionnement restreint en eau et a la consommation reduite d'eau par les forets; cependant, les secheresses moins severes et de courte duree ont generalement augmente l'ET de la foret et dans certains cas l'ET a meme excede la precipitation durant les periodes de secheresse. Nous avons attribue cela a l'augmentation de l'evaporation superficielle et a l'utilisation de l'eau en profondeur par le systeme de racines profondes. Plus precisement, nous trouvons que l'anomalie positive de l'ET en periodes de secheresse implique le paradoxe de la secheresse qui accelere le cycle hydrologique terrestre et amplifie encore davantage la secheresse. Cette etude, en tant qu'analyse synthetique globale d'etudes de cas a l'echelle de la station peut avoir un potentiel eleve pour comprendre l'importance du cycle de l'eau en foret module par la secheresse et la possibilite que le stress du a la secheresse augmente a cause des effets du paradoxe de la secheresse. [Traduit par la Redaction] Mots-cles: changement climatique, bilan hydrique, hydrologie forestiere, paradoxe de la secheresse, evapotranspiration., Introduction Evapotranspiration (ET), which mainly includes soil evaporation and vegetation transpiration, is a critical component of the water cycle in terrestrial ecosystems (Kool et al. 2014) and links water and [...]

Rainfall constitutes the primary input in the nutrient flux within forest ecosystems. The forest canopy modulates this flux by partitioning rainfall and selectively absorbing or adding nutrients. In mixed forests, variation in tree species composition regulates rainwater chemical composition, potentially leading to spatial heterogeneity in nutrient distribution and influencing nutrient cycling processes. This study examined the partitioning of rainfall into throughfall and stemflow, as well as their associated nutrient concentrations and fluxes, in a mixed broadleaf and coniferous forest on Changbai Mountain in Northeast China. We observed a rising trend in nutrient contents from rainfall to throughfall and then stemflow. The nutrient contents of stemflow varied largely with tree species due to the differences in canopy structure and bark morphological characteristics. The nutrient input contributed by throughfall and stemflow was 92.30 kg ha−1 during the observation period, and most elements underwent passive leaching through washout except for F− and Na+. We note that the nutrient fluxes in stemflow differed among tree species, with Pinus koraiensis (PK) delivering more acid group anions and Quercus mongolica (QM) providing more cations. Our research provides new insights into nutrient cycling within mixed forest canopies, sparking a transformative advancement in forest management and protection strategies through hydrochemistry-driven solutions. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania. [ABSTRACT FROM AUTHOR]

Phenology is an important factor indicating environmental changes and regulates the variations of carbon, water, and energy exchange. However, phenology models exhibit large uncertainties due to limited understanding of its mechanisms. In this study, we modified deciduous phenology scheme based on the evaluation of different phenological models using long‐term observations at Chinese Ecosystem Research Network with CLM4.5. The alternating leaf unfolding model and summer‐influenced autumn leaf falling model that we proposed, performed best in simulating leaf‐unfolding and leaf‐falling. Compared with the observed and remote‐sensed phenology, the modified model could better simulate the phenological dates at the site and regional scale. Moreover, the modified model improved the simulation of gross primary productivity (GPP) by decreasing the errors of modeled carbon uptake duration and amplitude. Furthermore, the advance in leaf‐unfolding slowed down from 0.20 days/year during 1981–2015 to 0.11 days/year during 2016–2100 under RCP4.5 because of the slowdown of climate warming, but the delay in leaf‐falling changed little. By the last decade of the twenty‐first century, the leaf‐unfolding would advance (8 days) and leaf‐falling would delay (16 days). The subtropical region had large interannual variation (IAV) in leaf‐unfolding because of the high sensitivity to temperature. The phenological dates IAV in the cold temperate region increased due to enhanced temperature IAV. We suggest that the deciduous phenology models, especially the leaf‐falling process, used in Community Land Model need to be improved to reduce the errors in predicting phenology and carbon flux in the future. Plain Language Summary: As an important factor indicating climate and environmental changes, phenology plays an important role in regulating the variation of carbon, water, and energy exchange. Due to limited understanding of phenology mechanisms, the simulation of phenology remains large uncertainties. In this study, we evaluated and modified the leaf unfolding and leaf falling models using the long‐term phenological observations with the Community Land Model (CLM4.5), validated the modified leaf‐unfolding and leaf‐falling models by observed phenology and remote‐sensing phenology data, examined the ability of CLM4.5 with modified deciduous phenology submodels in simulating gross primary productivity, and used the modified phenology models to predict the changes in phenological dates in the future. Our results suggest that the deciduous phenology models, especially the leaf‐falling process, used in the CLM is urgent to be improved to reduce the errors in predicting growing season length and carbon fluxes in deciduous forests in the context of climate change. Key Points: The involvement of chill accumulation with heat requirement in leaf unfolding models can improve the model performanceWe propose a new leaf falling model by considering the relationship between summer temperature and aging state threshold, which improves the model performanceThe revise of deciduous phenological model in CLM4.5 improves the simulation of gross primary productivity by influencing carbon uptake duration and amplitude [ABSTRACT FROM AUTHOR]

Key message: The responses of tree leaf δ13C to climatic and geographic factors were different among major climate zones in China, suggesting that the environmental influences on tree ecophysiology are different among climates. Leaf stable carbon isotope composition (δ13C) integrates information about environmental effects on plant carbon and water balance. However, extensive inconsistent findings have been reported regarding the variability of leaf δ13C along environmental gradients, impeding the interpretations of plant physiological responses to environmental changes using δ13C values. In this study, data from 505 tree leaf δ13C measurements and climatic and geographic information at 186 sites across China were collected from peer-reviewed articles. The dataset was grouped according to the Köppen–Geiger climate classification for investigating the variations in tree leaf δ13C with climatic and geographic factors among different climate zones. Multiple regression analyses were conducted using this dataset to provide a predictive model for tree leaf δ13C variability in China. The results showed that the tree leaf δ13C was − 28.05‰ on average and was highest in the polar climate (− 26.82‰), lowest in the warm temperate climate (− 29.66‰), and almost the same in the temperate arid (− 27.60‰) and cold temperate (− 27.69‰) climate zones. We found that the magnitude of the leaf δ13C values of certain climates was partly determined by the functional type of local tree species. Moreover, the relationship between tree leaf δ13C and mean annual precipitation (MAP) was found to be significantly positive only for the temperate arid climate zone, while significant negative correlations were found for the other climate zones. Tree leaf δ13C was significantly negatively related to mean annual temperature (MAT) in the warm temperate and polar climate zones, and it was positively related to altitude in the warm temperate and cold temperate climate zones. These results suggest that the widely observed inconsistent responses of leaf δ13C to environmental factors, as well as the inconsistent δ13C values of a specific tree functional type, can be attributed to the climate conditions in which the tree inhabits. Finally, a simple regression model incorporating MAT, MAP, and altitude was proposed, which explained 45% of the variation in tree leaf δ13C. These findings could be useful in evaluating potential changes in tree physiological traits under the changing climate in China. [ABSTRACT FROM AUTHOR]