Search Results (7,932)

As an essential blue carbon ecosystem, mangroves play a vital role in coastal protection, biodiversity conservation, and climate regulation. However, their complex and variable growth environments pose challenges for precise monitoring. Hainan Island represents a region within China where mangrove forests are the most concentrated and diverse in type. In recent years, ecological restoration efforts have led to the recovery of their coverage areas. This study analyzed the spatial distribution, canopy height, and aboveground carbon storage variations in Hainan mangrove forests. Deep-learning and multiple machine-learning algorithms were used to integrate multitemporal Sentinel-2 remote sensing imagery from 2019 to 2023 with unmanned aerial vehicle observations and field survey data. Multi-rule image fusion and deep-learning techniques effectively enhanced mangrove identification accuracy. The mangrove classification achieved an overall accuracy exceeding 90%. The mangrove area in Hainan increased from 3948.83 ha in 2019 to 4304.29 ha in 2023. Gradient-boosted decision tree (GBDT) models estimated average canopy height with a high coefficient of determination (R² = 0.89), and Random Forest (RF) models yielded the best estimations of total above-ground carbon stock with strong agreement to field observations. Integrating multisource remote sensing data with artificial intelligence algorithms enabled high-precision dynamic monitoring of mangrove distribution, structure, and carbon storage to provide scientific support for the assessment, management, and carbon sink accounting of Hainan mangrove ecosystems. Full article

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

Forest type strongly influences soil microbial community composition and associated carbon cycling, yet its influence on microbial functional traits remains poorly understood. In this study, metagenomics sequencing was used to investigate soil microbial communities and carbon metabolism genes across three forest types: deciduous broadleaf (DBF), mixed coniferous–broadleaf (CBMF), and coniferous forest (CF) at two soil depths (0–20 cm and 20–40 cm) on Lushan Mountain in subtropical China. The results showed that CF exhibited higher bacterial diversity and a distinct microbial composition, with an increase in Actinomycetota and Bacteroidota and a decrease in Acidobacteriota and Pseudomonadota. The Calvin cycle was the dominant carbon fixation pathway in all forests, while the relative abundance of secondary pathways (i.e., the 3-hydroxypropionate bi-cycle and reductive citrate cycle) varied significantly with forest type. Key carbon fixation genes (sucD, pckA) were more abundant in CF and CBMF, with higher levels of rpiA/B and ackA in DBF. Functional profiling further indicated that CF soils, especially in the surface layer, were enriched in glycoside hydrolases (GHs) and carbohydrate esterases (CEs), while CBMF showed a greater potential for starch and lignin degradation. Multivariate statistical analyses identified soil available phosphorus (AP) and pH as primary factors shaping microbial community variation, with AP emerging as being the dominant regulator of carbon-related functional gene abundance. Overall, the prevalence of these distinct genetic potentials across forest types underscores how vegetation composition may shape microbial functional traits, thereby influencing the stability and dynamics of the soil carbon pool in forest ecosystem. Full article

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Afforestation is recognized as a highly effective strategy for enhancing ecosystem carbon sequestration. However, the changes and drivers of soil organic carbon (SOC) following afforestation are still debated due to climate differences. Clarifying these responses is critical for improving the effectiveness of afforestation-based carbon sequestration strategies. In this study, we analyzed nine 20-year-old afforestation sites (coniferous and broad-leaved) along a Chinese climatic gradient to quantify SOC and its fractional changes following farmland-to-forest conversion, and to identify the dominant factors controlling SOC sequestration across climatic gradients and forest types. The results showed that afforestation enhanced SOC (5.1%–210.5%, p < 0.05) in humid and semi-humid regions, but showed no significant effect in semi-arid regions, and it even reduced SOC in arid regions (−19%–−53.8%). Across all climatic zones, mineral-associated organic carbon was the dominant contributor to SOC accumulation throughout the entire soil profile (0–60 cm). Climatic-scale analyses based on the aridity index determined that root and litter C/N ratios were the primary drivers of SOC sequestration in coniferous forests, whereas in broad-leaved forests, they were more strongly controlled by soil physicochemical properties, particularly total nitrogen, bulk density, and soil water content. This study identified that SOC responses to afforestation are strongly mediated by climate and forest type, which is helpful for managers to take targeted measures to increase soil carbon sequestration in forest management. Full article

Recognizing the evolution of landscape patterns in the Chishui River source region is essential for protecting ecosystems and sustainable growth in the Yangtze River Basin and other similar areas. However, knowledge of landscape pattern evolution within the primary channel zone remains insufficient. To address this gap, the current study used 2000–2020 land-use, geography, and socio-economic data, integrating landscape pattern indices, land-use transfer matrices, dynamic degree, the GeoDetector model, and the PLUS model. Results revealed that forest and cropland remained the prevailing land-use types throughout 2000–2020, comprising over 85% of the landscape. Grassland had the highest dynamic degree (1.58%), and landscape evolution during the study period was characterized by increased fragmentation, enhanced diversity, and stable dominance of major forms of land use. Anthropogenic influence on different landscape types followed the order: construction land > cropland > grassland > forest > water bodies. Land-use change in this region is a complex process governed by the interrelationships among various factors. Scenario-based predictions demonstrate pronounced variability in various land types. These findings provided a more comprehensive understanding of landscape patterns in karst river source regions, provided evidence-based support for regional planning, and offered guidance for ecological management of similar global river sources. Full article

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

Spontaneous living spaces are public activity venues within cities that emerge through residents’ autonomous creation and informal planning. Although these spaces may appear disorganized, they serve vital functions: fostering social interaction, enhancing community vitality, improving spatial adaptability, and increasing life satisfaction. However, research on the formation mechanisms, structural logic, resident satisfaction, and the impact of spontaneous living spaces on community vitality is limited, and there is a lack of robust research methodologies. This study aims to explore the formation mechanisms of spontaneous living spaces within historic cultural districts and their influence on community vitality. Using Wuhan’s Tanhualin National Historic and Cultural District as a case study, this research innovatively combines the Mask R-CNN deep learning model with a Random Forest regression model. The Mask R-CNN model was employed to accurately identify and perform pixel-level segmentation of 1249 spontaneous living spaces. Combined with questionnaire surveys and the Random Forest model, this study reveals non-linear relationships between key factors such as community vitality, resident satisfaction with various types of spontaneous living spaces, and crowd density. The findings show that spontaneous living spaces effectively address residents’ unmet needs for emotional connection and dynamic lifestyles—needs often overlooked by official residential planning. This research provides a reliable technical framework and quantitative decision support for regulating the formation of spontaneous living spaces, thereby enhancing residents’ quality of life and urban vitality while preserving historical character. Full article

In the field of smart agriculture, soil property data at the field scale drives the precision decision-making of agricultural inputs such as seeds and chemical fertilizers. However, soil texture has significant spatial variability at the field scale, and traditional remote sensing monitoring methods have certain data intermittency, which limits small-scale prediction research. In this study, based on the Google Earth Engine platform, soil synthetic images were generated according to different time intervals using mean compositing and median compositing modes, image bands were extracted, and spectral indices were introduced; combined with the random forest algorithm, the effects of different compositing time windows, compositing modes, and compositing data types on prediction accuracy were evaluated; and three partitioning strategies based on crop growth, soil synthetic image brightness, and soil type were adopted to conduct local partitioning regression of soil texture. The results show that: (1) The use of mean compositing of multi-year May images from 2021 to 2024 can improve prediction accuracy. When this method is combined with the “band reflectance + spectral indices” dataset, compared with other compositing methods, the R² of clay particles, silt particles, and sand particles can be increased by 8.89%, 9.50%, and 2.48% on average. (2) Compared with using only image band data, the introduction of spectral indices can significantly improve the prediction accuracy of soil texture at the field scale, and the R² of clay particles, silt particles, and sand particles is increased by 4.58%, 3.43%, and 4.59% on average, respectively. (3) Global regression is superior to local partitioning regression; however, the local partitioning regression strategy based on soil type has good accuracy performance. Under the optimal compositing method, the average R² of soil particles of each size fraction is only 1.08% lower than that of global regression, which has great application potential. This study innovatively constructs a comprehensive strategy of “moisture spectral indices + specific compositing time window + specific compositing mode + soil type partitioning”, providing a new paradigm for soil texture prediction at the field scale in Northeastern China, and lays the foundation for data-driven water and fertilizer decision-making. Full article

Water is vital for producing summer maize (SM) and winter wheat (WW); therefore, its proper management is crucial for sustainable farming. This study aimed to develop new tri-band spectral vegetation indices that enhance the accuracy of monitoring plant moisture content (PMC) in SM and WW. We conducted irrigation treatments, including W0, W1, W2, W3, and W4, in SM–WW rotations to address this issue. Canopy reflectance was measured with a field spectroradiometer. Tri-band hyperspectral vegetation indices were constructed: Normalised Water Stress Index (NWSI), Normalised Difference Index (NDI), and Exponential Water Stress Index (EWSI), for assessing the PMC of SM and WW. Results indicate that NWSI outperformed other indices. In the maize trials, the correlation reached R = −0.8369, while in wheat, it reached R = −0.9313, surpassing traditional indices. Four mainstream machine learning models (Random Forest, Partial Least Squares Regression, Support Vector Machine, and Artificial Neural Network) were employed for modelling. NWSI-PLSR exhibited the best index-type performance with an R² of 0.7878. When the new indices were combined with traditional indices as input data, the NWSI-Published indices-SVM model achieved superior performance with an R² of 0.8203, outperforming other models. The RF model produced the most consistent performance and achieved the highest average R² across all input types. The NDI-Published indices models also outperformed those of the published indices alone. This indicates that these new indices improve the accuracy of moisture content monitoring in SM and WW fields. It provides a technical basis and support for precision irrigation, holding significant potential for application. Full article

Fruit growing is a traditional component of Bulgarian agricultural production. According to the latest statistical data, the share of areas planted with cherries is 10.5% of the total orchard area, and with apples, 7.2%, totaling 67,800 ha. This article presents the results of ground and remote (satellite) measurements and observations of cherry and apple orchards, along with the methods for their processing and interpretation, to define the current state and forecast their expected development. This research aims to combine the capabilities of the two approaches by improving and expanding observation and forecasting activities. Ground-based measurements and observations consider the dates of a permanent transition in air temperature above 5 °C and several cardinal phenological stages, based on the idea that a certain temperature sum (CU, GDH, GDD) must accumulate to move from one phenological stage to another. The obtained data were statistically analyzed, and by means of classification with the Random Forest algorithm, the dates for the occurrence of the stages of bud break, flowering, and fruit ripening in the development of cherry and apple orchards were predicted with an accuracy of −6 to +2 days. Satellite studies include creating a database of Sentinel-2 digital images across different spectral bands for the studied orchards, investigating various post-processing approaches, and deriving indicators of developmental phenostages. Ground data from the 2021–2023 experiment in Kyustendil and Plovdiv were used to determine the phases of fruit bursting, flowering, and ripening through satellite images. An assessment of the two approaches to predicting the development of the accuracy of the models was carried out by comparing their predictions for bud swelling and bursting (BBCH 57), flowering (BBCH 65), and fruit ripening (BBCH 87/89) of the observed phenological events in the two selected orchard types, representatives of stone and pome fruit species. Full article

The abundant satellite data have enabled the study of the dynamics of forest logging and its corresponding carbon balance with remote sensing. Change detection techniques with moderate-resolution imagery have been widely developed. Yet the signal processing or machine learning methods are sample-dependent, lacking an understanding of spectral signals of forest growth and logging cycles, which is necessary to distinguish logging from other types of disturbance, and mechanism models addressing post-logging tree changes are too complex for parameter inversion. We therefore proposed an efficient physical-based model for spectral simulation of annual forest logging by coupling forest dynamic model ZELIG and the stochastic radiative transfer (SRT) model. The forest logging simulation was conducted and validated by Abies forest field data before and after logging in Wangqing County, Northeastern China (R² = 0.85, RMSE = 10.82 t/ha). The spectral changes in Abies forest stands with annual growth and varying logging intensities were simulated by the novel model. The annual Landsat-8 and Gaofen-1 fusion multispectral imagery of the study area from 2013 to 2016 was furtherly used to extract annual sequence spectral data of 350 forest plots and perform inversion of the annual difference in above-ground biomass (dAGB). With the inversion method combining the look-up table of the ZELIG-SRT model and the random forest regression, the retrieved dAGB of the 350 plots indicated consistency with the measured data on the whole (R² = 0.71, RMSE = 13.32 t/ha). The novel physical-based approach for AGB monitoring is more efficient than previous 3D computer models and less dependent on field samples than data-driven models. This study provides a theoretical basis for understanding the remote sensing response mechanism of forest logging and a methodological basis for improving forest logging monitoring algorithms. Full article

Background/Objectives: Significant heterogeneity in the treatment response to peroxisome proliferator-activated receptor γ (PPARγ) agonists exists, and predictive factors for their efficacy remain unclear. We aimed to assess the relationships between routinely available clinical features and the efficacy of PPARγ agonists and pan-PPAR agonists by meta-regression analysis. Methods: We searched PubMed, Embase, Cochrane Library, ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) and included randomised controlled trials involving type 2 diabetes patients with 12-week or longer treatment durations with PPARγ agonists or pan-PPAR agonists published before 11 November 2023 (PROSPERO registration number: CRD42024578987). We conducted mixed-effect meta-regression analyses between baseline variables and treatment response. Moreover, we developed a machine learning-based meta-forest model and ranked the relative importance of each variable. Results: In 147 studies involving 29,250 participants, PPARγ and pan-PPAR agonists significantly reduced HbA1c (mean difference(MD) = −0.8876 [95% confidence interval (CI): −0.8999, −0.8754]; p < 0.0001, I² = 96.0%) and FPG = (MD = −1.7900 [95% CI: −1.9137, −1.6663]; p < 0.0001, I² = 92.0%). Multivariable association analysis suggested that a greater proportion of female participants (β = 0.0066 [95% CI: 0.0012, 0.0121]; p = 0.017), younger age (β = −0.0314 [95% CI: −0.05, −0.0129]; p = 0.0009) and lower HDL-C levels (β = −0.9304 [95% CI: −1.5176, −0.3431]; p = 0.0019) were significantly associated with a greater decrease in HbA1c. A greater proportion of female participants (β = 0.0112 [95% CI: 0.0019, 0.0205]; p = 0.0178) and lower baseline HDL-C levels (β = −1.8722 [95% CI: −2.812, −0.9323]; p < 0.0001) were significantly associated with a greater decrease in FPG. These variables also ranked among the top five most important predictors of drug response in the meta-random forest models. Conclusions: Our study demonstrated that female sex, younger age, and lower HDL-C levels were associated with greater glycaemic lowering effect from PPARγ and pan-PPAR agonists. Full article

The characteristics of litter combustion have a significant impact on the spread of surface fires in the central Yunnan Province, a high-risk forest fire zone. The burning behavior of individual and mixed-species litter samples from five dominant tree species (Pinus yunnanensis Franch., Keteleeria evelyniana Mast., Quercus variabilis Blume., Quercus aliena var. acutiserrata, and Alnus nepalensis D. Don.) was assessed in this study using cone calorimeter tests. Fern fronds and fine branches were included in additional tests to evaluate their effects on specific combustion parameters, such as Fire Performance Index (FPI), Flame Duration (FD), Time to Ignition (TTI), Mass Loss Rate (MLR), Residual Mass Fraction (RMF), Peak Heat Release Rate (PHRR), and Total Heat Release (THR). There were remarkable differences in the burning properties of the three types of litter (broadleaf, pine needles, and short pine needles). The THR and PHRR values of P. yunnanensis were the highest, whereas the PHRR of the other species varied very little. Short pine needle litter showed incomplete combustion and a long flame duration. When measured against pure pine needle litter, mixtures of P. yunnanensis and broadleaf litter showed lower PHRR. When set side by side to pure pine needle litter, P. yunnanensis and broadleaf litter showed lower PHRR. THR rose when fine branches were included, underlining the significance of fine woody fuels in fire behavior. The insertion of ferns increases the percentage of unburned biomass, prolongs TTI, and dramatically reduces PHRR. Full article

The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments. Full article