Search Results (1,690)

The sawn timber production process generates up to 63% of residues during primary processing in sawmills. For this industry, the devaluation and disposal of these residues remain significant challenges; proper management requires a more accurate quantification of the volume. This study evaluates and compares two indirect methods for estimating the volume of stacked residues: one based on image processing and the other on terrestrial LiDAR technology. Residues of Pinus spp. from a sawmill were used, with their actual volume determined using a xylometer. The image-based method, which uses threshold-based segmentation, achieved a R² = 0.64 and RMSE = 0.006 m³. In contrast, the LiDAR-based method, which derives measurements directly from 3D reconstruction, obtained an R² = 0.506 and RMSE = 0.009 m³. Despite these differences, ANOVA testing (p > 0.05) indicated no statistically significant differences between the methods. The results suggest that both approaches may serve as preliminary tools for forest residue quantification and provide a solid foundation for future research aimed at developing field-applicable technological solutions. Full article

Green perception underlies pro-greenspace behavior, but external stimuli and behavior are not always aligned. Understanding how residents’ perceived external green stimuli influence pro-greenspace behavior, and how the “good citizen” image (face) shapes this relationship, is essential. The study aims to deepen the understanding of the complex mechanisms driving urban residents’ pro-greenspace behavior by constructing an extended Stimulus-Organism-Response theoretical framework (C-SOR) that includes contextual factors. Using data from a 2024 field survey of 959 residents from Shanghai, China, this study employs Ordinary Least Squares (OLS) regression to examine the main effect of green perception on pro-greenspace behavior. A mediation model is employed to analyze the mediating role of nature connectedness, while a moderation model tests the moderating effect of “good citizen” image (face) on the stimulus–behavior relationship. The results show that green perception significantly promotes pro-greenspace behavior, positively influencing it through nature connectedness. However, the “good citizen” image (face) exerts a motivational crowding-out effect on green perception. Further analysis reveals individual heterogeneity in the expression of these effects across different types of pro-greenspace behavior. The findings highlight the importance of green space experience and the activation of environmental wisdom in traditional culture, offering new perspectives for developing strategies to guide pro-greenspace behavior. Full article

Promoting nature-positive forestry requires sustainable timber production that aligns with ecosystem service (ES) conservation. However, Japan’s recently implemented top-down timber production policy may undermine sustainability in local forest landscapes. We assessed the spatial sustainability of plantation forestry by comparing actual logged areas (2000–2019) with allowable logging areas. Logged areas were identified using satellite imagery analysis, while allowable logging areas were estimated by excluding forests at high risk of landslides or with unclear ownership and dividing the remaining area by the standard logged age. While total logged area remained below the experience-based sustainable threshold, logging in profitable forests exceeded allowable levels in recent years. Forests with higher profitability experienced concentrated logging after 2015, indicating the strong influence of the national policy. This spatial imbalance threatens long-term sustainability by depleting productive forest patches while ignoring underutilized unprofitable forests. Our findings demonstrate the risks of uniform, production-oriented policies and highlight the need for adaptive, locally responsive forest governance. By integrating ecological and social constraints into spatial analysis, this study proposes a new sustainability measurement in line with nature-based solutions. Future forest policy must incorporate local knowledge and participatory decision-making to sustain forest ESs and timber supply under changing social and environmental conditions. Full article

Mountain forests in biodiversity hotspots show complex responses to climate and topographic gradients. However, the effect of synergistic controls of elevation and climate on Net Primary Productivity (NPP) dynamics remain insufficiently quantified in complex mountains. Southwest China’s mountains are Asia’s most biodiverse temperate region with pronounced vertical ecosystem stratification, representing a critical continental carbon sink. This study investigated the spatiotemporal dynamics and driving mechanisms of NPP in Southwest China’s typical mountain ecosystems over the past three decades using a high-resolution modeling framework integrated with relative importance analysis, a Geodetector, and an elevation-dependent model. The results showed that (1) NPP revealed a significant increasing trend, rising from 634 ± 325 to 748 ± 348 g C m⁻² yr⁻¹ (mean rate 4 g C m⁻² yr⁻¹) from 1990 to 2018. Spatially, the most rapid increases occurred in eastern regions. (2) Rising CO₂ and climate warming (dominate 17% regions) drove interannual NPP growth, with elevation thresholds dictating driver dominance. The CO₂ governed low elevation, while temperature controlled higher elevation (>4800 m). (3) The elevation-dependent model revealed a more complex and nonlinear relationship between NPP and elevation, identifying three distinct phases: the saturation phase (<500 m) with negligible decay of NPP; the transition phase (500–3500 m) with linear decline (NPP loss of 29 g C m⁻² yr⁻¹ per 100 m); and the collapse phase (>3500 m) with continuously attenuated NPP losses (NPP average loss of 10.5 g C m⁻² yr⁻¹ per 100 m) reflecting high-elevation vegetation adaptation to extreme conditions. (4) Land cover dominated NPP spatial heterogeneity and was amplified by interactions with elevation and temperature, highlighting a vegetation–climate–topography coupling mechanism that critically shapes productivity patterns. Biodiversity-rich widespread mixed forests underpinned the region’s high productivity. Mountain protection should focus on protecting existing evergreen forests from fragmentation, while forestation should prioritize the establishment of biodiversity-rich mixed forest. These findings established a comprehensive framework for spatiotemporal analysis of driving mechanisms and enhanced the understanding of NPP dynamics in complex mountain ecosystems, informing sustainable management priorities in mountain regions. Full article

Traditional deep learning algorithms struggle to effectively utilize local spectral info in forest HS images and adequately capture subtle feature differences, often causing model confusion and misclassification. To tackle these issues, we present SF-UNet, a novel pixel-level classification network for forest HS images. It integrates the strengths of SpectralFormer and U-Net. First, the HGSE module generates semicomponent spectral nesting, strengthening local info element connections via spectral embedding. Next, the CAM within SpectralFormer serves as an auxiliary U-Net encoder. This allows cross-level jump connections and cascading through interlayer soft residuals, enhancing feature representation via cross-regional adaptive learning. Finally, the U-Net decoder is used for pixel-level classification. Experiments on forest Sentinel-2 data show that SF-UNet outperforms mainstream frameworks. While Vision Transformer has an 88.29% classification accuracy, SF-UNet achieves 95.28%, a significant 6.99% improvement. Moreover, SF-UNet excels in land cover change analysis using multi-temporal Sentinel-2 images. It can accurately capture subtle land use changes and maintain classification consistency across seasons and years. These results highlight SF-UNet’s effectiveness in forest remote sensing image classification and its potential application value in deep learning-based forest HS remote sensing image classification research. Full article

The fragmented nature of Chinese households’ forestland hinders the realization of economies of scale in forestry production. Understanding the role of forestry service outsourcing organizations in mitigating this fragmentation provides a critical foundation for the exploration of pathways to scaled forestry management. Based on tracking data from 500 households across 10 counties in Fujian Province between 2013 and 2018, this study examines an unbalanced panel containing six periods and 2780 valid observations. It constructs a panel Logit model to examine the influence of forestry service outsourcing organizations on the likelihood of forestland transfer by households, and it employs a panel Tobit model to analyze the relationship between these organizations and the scale of forestland transferred. To capture potential heterogeneity, the analysis incorporates households’ part-time status and the forestland terrain conditions. The results indicate that the duration of establishment of county-level forestry project teams and forestry companies in households’ regions significantly reduces the tendency of households to lease out their forestland, especially for those in plain and hilly regions and part-time forestry producers. Furthermore, the longer the establishment history of township-level forestry project teams, the more inclined households are to retain their family forestland management rights. Our study demonstrates that, when specialized forestry service outsourcing organizations emerge in the market, households are less likely to lease out their forestland, thereby retaining management rights, avoiding the risk of forestland loss, and reducing forestland abandonment. As forestry service outsourcing organizations continue to develop and expand—with improvements in service levels and production efficiency—forestry production is gradually transitioning toward a new stage of service-oriented scale operations. Full article

Copal is a non-timber forest product of historical, cultural, and industrial significance in Mexico. The use of unsustainable harvesting methods and a lack of understanding of the factors influencing their production have led to a decline in natural populations of resin-producing species. This study aimed to identify the dendrometric, edaphoclimatic, physiological, and resin extraction method variables with the greatest influence on resin yield in Bursera bipinnata using correlation analysis and multiple linear regression. The research was conducted in the Los Sauces micro-watershed, Morelos, Mexico, with a randomly selected sample of 70 trees. Nineteen explanatory variables were categorized into dendrometric, edaphoclimatic, physiological, and extraction method parameters. Variables significantly correlated with resin yield were diameter at breast height, crown diameter, crown volume, altitude, resin tapping faces on the stem, resin tapping faces on branches, total resin tapping faces, resin tapping face height, total resin tapping area, and the Normalized Difference Moisture Index (NDMI) in October. The regression model revealed that resin yield increased significantly with total tapping area ($\beta =0.649$) but decreased with greater incision length ($\beta =-0.308$) and higher NDMI values in October ($\beta =-0.205$), explaining 43.8% of the variation in resin yield. Results highlight the importance of tissue damage intensity, tree physiological status, and water availability as determinants of resin production. The model provides practical guidelines for optimizing extraction techniques, enabling sustainable harvesting that maintains tree vitality and supports long-term productivity in resin-harvesting communities. Full article

The restorative effects of various environmental factors within urban forests on physical and mental health exhibit significant differences. Specifically, vegetation types and topographical slope positions are key elements contributing to the environmental heterogeneity of urban forests. However, there is a lack of studies that have concurrently examined the health restoration effects of both factors. This study conducted an empirical experiment on university students in urban forests during the autumn season, investigating the effects of different vegetation types and slope positions on physiological and psychological restoration, and identifying the key environmental factors contributing to these effects. The results show the following: (1) Urban forests with different vegetation types exhibit varying restorative effects, with coniferous forests offering greater physiological restoration benefits than coniferous–broadleaf mixed forests. (2) Slope position affects both physiological and psychological restoration. In coniferous forests, the restorative effects on physical and mental health are greater at the top and midslope positions compared to the bottom slope position; in coniferous–broadleaf mixed forests, the best physiological restoration effects occur at the midslope position. (3) The key environmental factors influencing physiological restoration in urban coniferous forests are panoramic green coverage and elevation. (4) In urban coniferous–broadleaf mixed forests, temperature, humidity, and wind speed are the key factors affecting physiological restoration. This study reveals the restorative differences in urban forests under different vegetation types and slope positions, identifies the key environmental factors influencing health restoration, and provides a theoretical basis for further research on the impact of urban forests on human health. Future urban forest layout and design should fully consider the characteristics of different slope positions, optimize microclimate regulation, and maximize their role in promoting public health. Full article

Nonstructural carbohydrates (NSCs) are key storage molecules that can be used for tree growth and metabolism. The trade-off between NSC storage and biomass production has been long reported on. However, the carbon source limitation (indicated by NSC storage) to biomass production remains poorly quantitively assessed. The seasonal whole-tree NSC pool dynamics of 12 temperate tree species were quantitatively evaluated across seven seasonal sampling points. The ratio of seasonal variation in whole-tree NSC pool to annual biomass increment (the ΔNSC/ABI ratio) and the linear relationship of annual biomass increment to NSC storage were used to assess the coupling of NSC storage to annual biomass production. Whole-tree NSC pools were consumed in early summer when structural growth peaked and recovered in the nongrowing season, indicating a short-term trade-off between storage and growth. The ΔNSC/ABI ratio was on average 0.59, with a large interspecific variation. Notably, there was a significant positive correlation between the storage of NSC and the 10 yr mean annual biomass increment, indicating a storage–growth coupling and the source limitation of growth in the long term. However, the storage cost of biomass production decreased along the slow-growth-to-fast-growth species continuum, mirroring the spectrum from conservative to acquisitive NSC use strategies. These findings highlight the critical role of time scale in understanding the relationship between storage and growth, which should be considered in the framework of simulation and conceptual models. Full article

This study aims to analyze the driving factors and threshold responses of the NDVI across different regional scales in Hunan Province, revealing the main influences on vegetation cover and the corresponding threshold effects and providing essential data for precise future afforestation planning. We use NDVI data and its associated driving factors, employing correlation analysis methods to investigate the spatial differentiation and threshold effects of vegetation driving factors at different regional scales. First, various analytical techniques, including Sen’s trend analysis, the Mann–Kendall significance test, and the Hurst index, are applied to assess changes in vegetation cover between 2000 and 2020 and to predict future trends. Second, to explore the differences in vegetation’s driving mechanisms at different regional scales, the optimal parameters-based geographic detector model is employed, which integrates continuous variable discretization methods and selects the optimal parameter set by maximizing explanatory power. This approach is particularly suitable for analyzing nonlinear relationships. Lastly, threshold regression analysis is conducted on the key driving factors identified through the optimal parameters-based geographic detector model. The results show that vegetation cover in most areas of Hunan significantly increased from 2000 to 2020; however, our predictions suggest slight degradation in the future. The optimal parameters-based geographic detector model identified topography and geomorphology as the primary factors affecting the spatial and temporal distribution of the NDVI, with notable regional differences in other factors. The influence of natural factors has weakened over time, while anthropogenic activities increasingly affect vegetation. Moreover, dual-factor influences exhibit stronger explanatory power than single-factor influences. The threshold response analysis reveals that slope is a key factor influencing the NDVI, with a positive threshold relationship observed at both the provincial and subregional scales, although the threshold points vary by subregion. The temperature and NDVI are negatively correlated, with varying threshold points across regions. The abovementioned research findings suggest that future afforestation efforts in Hunan should take into account the distinct characteristics of each subregion. Afforestation strategies should be tailored based on the specific threshold relationships observed in each area to enhance their effectiveness. Full article

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R² = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R² = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R² = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R² = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R² = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R² = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change. Full article

The weathering of carbonate rocks consumes significant amounts of soil CO₂, contributing to both direct source reduction and to the enhancement of carbon sinks. This process holds substantial potential as a carbon sink, making it a critical strategy for achieving carbon neutrality and mitigating climate change. However, the control mechanisms for the reverse assessment of karst carbon sinks, with soil CO₂ as the core at the input end of karstification, are unclear. By comparing soil respiration and its δ¹³C values between karst and non-karst regions, we analyzed the impact of karstification on soil respiration. In this study, we examined the karst grassland (KG), woodland (KW), and non-karst woodland (NKW) in the karst region with identical climate conditions as the research subject, analyzing the differences in soil respiration rate (RS), flux (SRF), and isotope δ¹³C under different land-use types, and comparing them with the non-karst region to reveal the carbon sink potential of karstification in reducing carbon emissions. The results showed that after the land-use change from KG to KW in the karst region, the annual mean values of the RS and SRF increased by 55.50% and 20.94%, respectively. Additionally, the annual mean values of the soil respiration contribution to carbonate weathering in KG were approximately 8.2% higher than those in KW. In contrast, the annual mean values of RS and SRF in KW were 25.14% and 41.80% lower than those in NKW, respectively. Furthermore, the soil respiration participation in carbonate weathering in KW was about 8.9% of that in NKW. Land use change can significantly influence karst carbon sinks, with the KG exhibiting the highest carbon sink capacity. Karst soils play a crucial role in reducing atmospheric CO₂ levels and facilitating regional carbon neutralization. Therefore, the karst systems play a pivotal role in mitigating the “land use change term” (source term, E_LUC) in the global carbon balance. Full article

The forests in China’s temperate semi-arid region play a significant role in water conservation, carbon storage, and biodiversity protection. An accurate estimation of their aboveground biomass (AGB) is crucial for assessing key ecological characteristics, such as forest carbon storage capacity, biodiversity, and ecological productivity. This provides a scientific basis for forest resource management and ecological conservation in this region. In this study, we extract 17 features related to the dominant species (Larix gmelinii and Betula platyphylla), including 7 vegetation indices derived from remote sensing data, 14 indices from 7 satellite bands, and 3 forest site characteristics. We then analyze the correlations between the AGB and these features. We compare the performance of AGB estimation models using linear regression (LR), polynomial regression (PR), ridge regression (RR), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and random forest regression (RFR). The results show that for Larix gmelinii, the Landsat 8 bands TM4 and TM7 have a greater degree of correlation with the AGB than the other features, while for Betula platyphylla, bands TM3 and TM4 show a greater degree of correlation with the AGB, and elevation has a weaker correlation with the AGB. Although the linear regression (LR) demonstrates certain advantages for AGB estimation, particularly when the AGB values range from 40 to 70 t/ha, the RFR outperforms in overall performance, with estimation accuracies reaching 85% for Betula platyphylla and 89% for Larix gmelinii. This study reveals that both the species and environmental characteristics may significantly influence the selection of the remote sensing features for AGB estimation, and the choice of algorithm for model optimization is critical. This study innovatively extracts the features related to the dominant species in temperate forests, analyses their relationships with environmental factors, and optimizes the AGB estimation model using advanced regression techniques, offering a method that can be applied to other forest regions as well. Full article

Forestry enterprises play a pivotal role in economic development, ecological civilization construction, and sustainable development. This study employs GIS-based spatial analysis to examine the distribution patterns and interrelationships of forestry enterprises, investigating their key determinants and spatial heterogeneity. The findings provide valuable insights for policymakers aiming to optimize industrial structures and enhance national ecological security. This research develops a comprehensive evaluation index system to assess the factors influencing forestry industry development in China. Nine factors are considered: human resources, economic development, industrial structure, technological support, trade development, financial environment, natural conditions, urbanization, and transportation. Using panel data from 367 cities in 2020, the Multiscale Geographically Weighted Regression (MGWR) method quantifies the influence of these factors and their spatial variations. The results show the following. (1) Forestry enterprises in China exhibit persistent spatial clustering. The eastern regions have a notably higher concentration than the western regions, and new enterprises are increasingly concentrated in a few hotspot cities in the east. (2) The spatial center of forestry enterprises has steadily moved southeast. Initially, the distribution was balanced in the eastern regions, but it has become highly concentrated in the southeastern coastal areas. (3) Regarding spatial autocorrelation, regions within the northwest cold spot cluster have been disappearing entirely. The northeast and southwest hotspot clusters have shrunk significantly, while the southeast hotspot cluster has remained large. (4) Permanent population size and green land area are the most strongly positively correlated with forestry enterprise distribution. Patent authorizations, orchard area, and forest land area also show positive effects. In contrast, road density and total import/export volume are negatively correlated with the number of forestry enterprises. This aligns with the structure of China’s forestry industry, which relies more on natural resources and market demand than on economic development level or financial environment. (5) The factors influencing forestry enterprise distribution show significant spatial variation, driven by regional factors such as resources, economy, and population. These factors ultimately determine the spatiotemporal distribution of forestry enterprises. This study provides data-driven insights to optimize the distribution of forestry industries and formulate more effective ecological protection policies. Full article

Camphora longepaniculata is an endangered evergreen tree listed as National Class II Protected Tree Species in China, highly valued for its medicinal and economic importance. Currently, research on this species has primarily focused on its pharmaceutical properties, while its potential distribution and responses to climate change remain insufficiently explored. In this study, 36 valid occurrence records and 11 environmental variables were utilized to predict its potential distribution and assess its response to future climate scenarios. The MaxEnt model revealed that the current distribution of C. longepaniculata largely aligns with its predicted suitable habitats, with the primary range located in Sichuan Province. Furthermore, this model identified the highly suitable habitats to be predominantly concentrated in Sichuan and Shaanxi Provinces under climate change. Among the environmental variables, annual precipitation (bio12), minimum temperature of the coldest month (bio6), and elevation (dem) were the most influential, collectively contributing over 70% to the model’s predictive accuracy. Future climate projections compared to the current distribution suggest a northward expansion of suitable habitats for C. longepaniculata, although Sichuan Province is predicted to remain the core habitat under future scenarios. Kernel density analysis of occurrence points indicated that the largest concentration of distribution points is near the Sichuan Basin, reinforcing the importance of this region as a stronghold for the species. Based on the results of potential distribution and kernel density analysis, in situ conservation, artificial cultivation, and the establishment of wild protected areas and local germplasm banks are recommended for stable, suitable habitats, such as Sichuan Province and parts of Yunnan and Guizhou Provinces. This study not only sheds light on the potential geographical distribution of C. longepaniculata and its response to climate change but also provides a scientific basis for the development of targeted conservation strategies for this species. Full article