Search Results (206)

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article

This study was carried out to investigate stand structure, growth dynamics, and carbon fluxes in Pinus rigida plantations of varying ages in South Korea. Field measurements across four mountain sites quantified diameter-class distributions, net primary productivity (NPP), soil respiration, and net ecosystem production (NEP). P. rigida exhibited normally distributed diameter structures in larger classes, whereas Quercus spp. showed reverse J-shaped patterns, indicating active regeneration and ongoing succession toward mixed broadleaved stands. Individual NPP was highest in P. densiflora (4.77 kg yr⁻¹) and P. rigida (4.31 kg yr⁻¹), while Quercus spp. displayed lower growth due to light limitation. Stand-level NPP peaked in 20–40-year-old stands (4.27–4.88 ton C ha⁻¹ yr⁻¹) and declined with age (2.30 ton C ha⁻¹ yr⁻¹). Soil respiration averaged 1.0 ton C ha⁻¹ yr⁻¹ and was strongly temperature dependent (R² = 0.56; Q₁₀ = 2.70). NEP on Mt. Galmi reached 4.38 ton C ha⁻¹ yr⁻¹, demonstrating substantial carbon sink capacity. These findings indicate that aging P. rigida plantations maintain ecosystem-level carbon uptake through successional compensation. Policy efforts should prioritize adaptive thinning, assisted natural regeneration, and long-term monitoring frameworks to accelerate the transition toward climate-resilient mixed forests and to strengthen national forest carbon neutrality strategies. Future research should integrate long-term carbon flux observations, species interaction modeling, and assessments of climate-driven disturbance regimes to refine management pathways for resilient mixed-forest landscapes. Full article

Air pollution, soil contamination, and rising illness demand integrated, nature-based solutions. Willow trees (Salix spp.) uniquely combine ecological resilience with therapeutic value, remediating polluted environments while supporting human well-being. This review synthesizes recent literature on the established role of Salix spp. in phytoremediation and growing contribution to forest therapy through emissions of biogenic volatile organic compounds (BVOCs). As urbanization accelerates and environmental pressures intensify globally, the surprising adaptability and multifunctionality of Salix justify the utilization of this genus in building resilient and health-promoting ecosystems. The major points discussed in this work include willow-based phytoremediation strategies, such as rhizodegradation, phytoextraction, and phytostabilization, contributing to restoring even heavily polluted soils, especially when combined with specific strategies of microbial augmentation and trait-based selection. Salix plantations and even individual willow trees may contribute to forest therapy (and ‘forest bathing’ approaches) through volatile compounds emitted by Salix spp. such as ocimene, β-caryophyllene, and others, which exhibit neuroprotective (against Parkinson’s disease), anti-inflammatory, and mood-enhancing properties. Willow’s significantly extended foliage season in temperate regions allows for prolonged ‘forest bathing’ opportunities, enhancing passive therapeutic engagement in urban green infrastructures. Remarkably, the pharmacological potential of willow extends beyond salicin, encompassing a diverse array of phytocompounds with applications in phytomedicine. Finally, willow’s ease of propagation and adaptability make this species a convenient solution for multifunctional landscape design, where ecological restoration and human well-being converge. Overall, this review demonstrates the integrative value of Salix spp. as a keystone genus in sustainable landscape planning, combining remarkable environmental resilience with therapeutic benefits. Future studies should explore standardized methods to evaluate the combined ecological and therapeutic performance of Salix spp., integrating long-term field monitoring with analyses of BVOC emissions under varying environmental stresses. Full article

UAV laser scanning (UAV-LS) combines extensive scanning coverage with high point cloud density, enabling efficient and precise acquisition of key forest attributes. Based on field-measured data and UAV-LS data from 138 Chinese fir (Cunninghamia lanceolata) plantation plots in southern China, this study systematically developed growing stock volume (GSV) estimation models at both tree and stand levels. The models included base models (allometric models), linear models, dummy variable models incorporating age groups, and nonlinear mixed-effects models incorporating random effects (plot and area levels for the tree level, and only the area level for the stand level). The results showed the following: (1) Stand-level GSV prediction relied primarily on height metrics, achieving optimal performance through a combination of the 10th cumulative height percentile (AIH₁₀) and canopy cover (CC), both of which showed near-linear relationships with GSV; tree-level GSV was predicted by LiDAR-derived tree height (LH) and crown width (LCW), with LH explaining most variation. (2) Tree-level models achieved R² = 0.639–0.725 and RMSE = 0.050–0.058 m³, exhibiting larger individual prediction errors (mean percentage standard error, MPSE > 30%) with smaller aggregate prediction errors (mean prediction error, MPE < 1%); stand-level models reached R² = 0.785–0.879 and RMSE = 46.052–61.314 m³ ha⁻¹ while maintaining controlled errors across scales (MPE < 5%, MPSE < 20%). (3) At both the tree and stand levels, the nonlinear mixed-effects model outperformed the others, followed by the dummy variable model and the base model, with the linear model exhibiting the worst performance; area-level random effects primarily influenced the baseline value of tree-level GSV and the allometric relationship between stand-level GSV and AIH₁₀, whereas plot-level random effects affected the allometric relationships of tree-level GSV with LH and LCW. This study confirms the effectiveness of UAV-LS for large-scale forest resource monitoring, while underscoring the necessity of incorporating spatial heterogeneity in GSV estimation. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

Accurate detection of machinery-induced strip roads after forest operations is fundamental for assessing soil disturbance and supporting sustainable forest management. However, in Mediterranean pine forests where canopy openings after boom-corridor thinning are moderate, the effectiveness of different remote sensing techniques remains uncertain. Previous studies have shown that LiDAR-based methods can reliably detect logging trails in different forest stands, but their direct transfer to structurally simpler, even-aged Mediterranean stands has not been validated. This study addresses this gap by testing whether UAV-derived RGB imagery can achieve comparable accuracy to LiDAR-based methods under the canopy conditions of boom-corridor thinning. We compared four approaches for detecting strip roads in a black pine (Pinus nigra Arn.) plantation on Mount Amiata (Tuscany, Italy): one based on high-resolution UAV RGB imagery and three based on LiDAR data, namely Hillshading (Hill), Local Relief Model (LRM), and Relative Density Model (RDM). The RDM method was specifically adapted to Mediterranean conditions by redefining its return-density height interval (1–30 cm) to better capture areas of bare soil typical of recently trafficked strip roads. Accuracy was evaluated against a GNSS-derived control map using nine performance metrics and a balanced subsampling framework with bootstrapped confidence intervals and ANOVA-based statistical comparisons. Results confirmed that UAV-RGB imagery provides reliable detection of strip roads under moderate canopy openings (accuracy = 0.64, Kappa = 0.27), while the parameter-tuned RDM achieved the highest accuracy and recall (accuracy = 0.75, Kappa = 0.49). This study demonstrates that RGB-based mapping can serve as a cost-effective solution for operational monitoring, while a properly tuned RDM provides the most robust performance when computational resources are sufficient to work on large point clouds. By adapting the RDM to Mediterranean forest conditions and validating the effectiveness of low-cost UAV-RGB surveys, this study bridges a key methodological gap in post-harvest disturbance mapping, offering forest managers practical, scalable tools to monitor soil impacts and support sustainable mechanized harvesting. Full article

In Portugal, increasing wildfire frequency and severe storm events have intensified the need for advanced monitoring tools to assess forest damage and recovery efficiently. This study explores the application of deep learning neural network techniques, specifically the Mask R-CNN architecture, for the automatic detection of trees in Pinus pinaster stands using RGB and multispectral imagery captured by a drone. The research addresses two distinct forest scenarios, resulting from disturbances intensified by climate change. The first concerns the detection of fallen trees following an extreme weather event to support damage assessment and inform post-disturbance forest management. The second focuses on segmenting individual trees in a newly established plantation after wildfire to evaluate the effectiveness of ecological restoration efforts. The collected images were processed to generate high-resolution orthophotos and orthomosaics, which were used as input for tree detection using Mask R-CNN. Results showed that integrating drone-based imagery with deep learning models can significantly enhance the efficiency of forest assessments, reducing the need for fieldwork effort and increasing the reliability of the collected data. Results demonstrated high performance, with average precision scores of 90% for fallen trees and 75% for recently planted trees, while also enabling the extraction of spatial metrics relevant to forest monitoring. Overall, the proposed methodology shows strong potential for rapid response in post-disturbance environments and for monitoring the early development of forest plantations. Full article

Tea plantations are one of the most intensive land-use systems in subtropical China, but the long-term effects on soil microbial functioning remain insufficiently understood. This study combined extracellular enzyme activity, ecoenzymatic stoichiometry, and partial least squares path modeling (PLS-PM) to assess the impacts of forest-to-tea conversion and plantation age on microbial nutrient acquisition and metabolic limitations. The results showed that tea plantations had significantly higher activities of carbon (C)-, nitrogen (N)-, and phosphorus (P)-acquiring hydrolases compared to adjacent pine forests, and oxidase activity increased significantly with plantation age, reaching a fivefold higher level in the oldest plantation. Soil acidification, decreased soil organic carbon, and shifts in microbial composition (decline in bacteria and actinomycetes, increase in fungi) were the main drivers of these changes. The study indicates that tea planting intensifies microbial limitations on carbon and phosphorus and shifts microbial metabolism toward oxidative pathways, which may destabilize soil carbon pools and reduce long-term fertility. These findings highlight the importance of balanced nutrient management in tea plantation practices. However, the study is limited by the short duration of field sampling. Future research should focus on long-term monitoring to better understand the sustained impacts of tea cultivation on soil microbial functions and explore the role of different management practices in mitigating these effects. Full article

Natural latex harvest is pushing the boundaries of unmanned agricultural production in rubber milk collection via integrated robots in hilly and mountainous regions, such as the fixed and mobile tapping robots widely deployed in forests. As there are bad working conditions and complex natural environments surrounding rubber trees, the real-time and precision assessment of rubber milk yield status has emerged as a key requirement for improving the efficiency and autonomous management of these kinds of large-scale automatic tapping robots. However, traditional manual rubber milk yield status detection methods are limited in their ability to operate effectively under conditions involving complex terrain, dense forest backgrounds, irregular surface geometries of rubber milk, and the frequent occlusion of rubber milk bowls (RMBs) by vegetation. To address this issue, this study presents an unmanned aerial vehicle (UAV) imagery rubber milk yield state detection method, termed YOLOv8n-RMB, in unstructured field environments instead of manual watching. The proposed method improved the original YOLOv8n by integrating structural enhancements across the backbone, neck, and head components of the network. First, a receptive field attention convolution (RFACONV) module is embedded within the backbone to improve the model’s ability to extract target-relevant features in visually complex environments. Second, within the neck structure, a bidirectional feature pyramid network (BiFPN) is applied to strengthen the fusion of features across multiple spatial scales. Third, in the head, a content-aware dynamic upsampling module of DySample is adopted to enhance the reconstruction of spatial details and the preservation of object boundaries. Finally, the detection framework is integrated with the BoT-SORT tracking algorithm to achieve continuous multi-object association and dynamic state monitoring based on the filling status of RMBs. Experimental evaluation shows that the proposed YOLOv8n-RMB model achieves an AP@0.5 of 94.9%, an AP@0.5:0.95 of 89.7%, a precision of 91.3%, and a recall of 91.9%. Moreover, the performance improves by 2.7%, 2.9%, 3.9%, and 9.7%, compared with the original YOLOv8n. Plus, the total number of parameters is kept within 3.0 million, and the computational cost is limited to 8.3 GFLOPs. This model meets the requirements of yield assessment tasks by conducting computations in resource-limited environments for both fixed and mobile tapping robots in rubber plantations. Full article

The farming-pastoral ecotone (FPE) of northern China serves as a critical ecological transition zone, in which poplar plantations significantly contribute to afforestation for large-scale ecological restoration projects. Due to concerns about sustainability, precise monitoring of the spatiotemporal dynamics of poplar plantations is needed, but systematic research is lacking. This study investigated the spatiotemporal dynamics of poplar plantation area and growth status from 1989 to 2022, taking the Anguli Nao watershed, a typical region in the FPE of northern China, as an example. Firstly, by utilizing satellite images and the random forest classification algorithm, the poplar plantation areas were well extracted, with a high accuracy over 93% and extremely strong consistency as demonstrated by a Kappa coefficient larger than 0.88. Significant changes in poplar plantation areas existed from 1989 to 2022, with an overall increasing trend (1989: 130.3 km², 2002: 275.9 km², 2013: 256.0 km², and 2022: 289.2 km²). Furthermore, the accuracy of our extraction method significantly outperformed six widely used global land cover products, all of which failed to capture the distribution of poplar plantations (producer’s accuracy < 0.21; Kappa coefficient < 0.18). In addition, the analysis of vegetation growth status revealed large-scale degradation from 2002 to 2013, with a degradation ratio of 24.4% that further increased to 31.1% by 2022, satisfying the significance test via Theisl–Sen trend analysis and the Mann–Kendall test. This study points out the uncertainty of existing land cover products and risk of poplar plantations in the FPE of northern China and provides instructive reference for similar research. Full article

Accurate forest biomass estimation is essential for quantifying carbon stocks, guiding sustainable forest management, and informing climate change mitigation strategies. Kenya’s forests are diverse, ranging from Afromontane and mangrove ecosystems to dryland woodlands and plantations, each presenting unique challenges for biomass measurement. This review synthesizes literature on field-based, remote sensing, and machine learning approaches applied in Kenya, highlighting their effectiveness, limitations, and integration potential. A systematic search across multiple databases identified peer-reviewed studies published in the last decade, screened against defined inclusion and exclusion criteria. The main findings are (1) Field-based techniques (e.g., allometric equations, quadrat sampling) provide reliable and site-specific estimates but are labor-intensive and limited in scalability. (2) Remote sensing methods (LiDAR, UAVs, multispectral and radar imagery) enable large-scale and repeat assessments, though they require extensive calibration and investment. (3) Machine learning and hybrid approaches enhance prediction accuracy by integrating multi-source data, but their success depends on data availability and methodological harmonization. This review identifies opportunities for integrating field and remote sensing data with machine learning to strengthen biomass monitoring. Establishing a national biomass inventory, supported by robust policy frameworks, is critical to align Kenya’s forest management with global climate and biodiversity goals. Full article

Coffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed. This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices. Full article

Restoration plantations in subtropical regions, often established with fast-growing tree species such as Acacia auriculiformis A. Cunn. ex Benth and Eucalyptus urophylla S. T. Blake, are frequently developed on highly weathered soils characterized by phosphorus deficiency. To investigate strategies for mitigating nutrient imbalances in such ecosystems, a long-term (≥13 years) fertilization experiment was designed. The experiment involved three fertilization regimes: nitrogen fertilizer alone (N), phosphorus fertilizer alone (P), and a combination of nitrogen and phosphorus (NP) fertilizers. The objective of this study was to investigate the effects of long-term fertilization practices on soil quality in subtropical plantations using a soil quality index (SQI). Consequently, all conventional soil physical, chemical, and biological indicators associated with the SQI responses to long-term fertilization treatments were systematically evaluated, and a principal component analysis (PCA) was conducted, along with a literature review, to develop a minimum dataset (MDS) for calculating the SQI. Three physical indicators (silt, clay, and soil water content), three chemical indicators (soil organic carbon, inorganic nitrogen, and total phosphorus), and two biological indicators (microbial biomass carbon and phosphodiesterase enzyme activity) were finally chosen for the MDS from a total dataset (TDS) of eighteen soil indicators. This study shows that the MDS provided a strong representation of the TDS data (R² = 0.81), and the SQI was positively correlated with litter mass (R² = 0.37). An analysis of individual soil indicators in the MDS revealed that phosphorus addition through fertilization (P and NP treatments) significantly enhanced the soil phosphorus pool (64–101%) in the subtropical plantation ecosystem. Long-term fertilization did not significantly change the soil quality, as measured using the SQI, in either the Acacia auriculiformis (p = 0.25) or Eucalyptus urophylla (p = 0.45) plantation, and no significant differences were observed between the two plantation types. These findings suggest that the MDS can serve as a quantitative and effective tool for long-term soil quality monitoring during the process of forest sustainable management. Full article

The estimation of rubber plantation aboveground biomass (AGB) is crucial for carbon sequestration assessment and management optimization. Unmanned Aerial Vehicles (UAVs) fitted with multispectral sensors present an economical approach for local-scale AGB monitoring. However, the prevailing studies primarily concentrate on spectral characteristics and algorithmic enhancements, failing to incorporate key ecological parameters such as stand age. Moreover, the current approaches remain constrained to local-scale assessments due to the absence of reliable upscaling methodologies from UAV to satellite platforms, limiting their applicability for regional monitoring. Thus, this study aims to establish an improved estimation model for rubber plantation AGB based on UAV multispectral imagery and stand age, develop an upscaling algorithm to bridge the gap between UAV and satellite scales, and ultimately achieve accurate regional-scale monitoring of rubber forest AGB. Combining optimized multispectral features, Landsat-derived stand age, and machine learning techniques yields the most accurate UAV-scale AGB estimates in this study, with performance metrics of R² = 0.90, an RMSE = 13.24 t/ha, and an MAE = 11.09 t/ha. Notably, the novel ‘UAV-satellite’ upscaling approach proposed in this study enables regional-scale AGB estimation using Sentinel-2 imagery, with remarkable consistency (correlation coefficient of 0.93). The developed framework synergistically combines Landsat-derived stand age data with spectral features, effectively improving rubber plantation AGB estimation accuracy through machine learning and enabling UAVs to replace manual measurements. This cross-scale upscaling framework demonstrates applicability beyond rubber plantation AGB monitoring, while providing novel insights for estimating critical parameters, including regional-scale stock volume and leaf area index, across diverse tree species. Full article

Mangroves play a pivotal role in carbon sequestration. To investigate the characteristics and driving factors of carbon sequestration in planted mangrove forests, we focused on planted mangrove forests in Wenzhou City, Zhejiang Province, China. Through a statistical analysis of soil physicochemical properties and plant morphological characteristics, we assessed carbon stock distribution patterns and identified key influencing factors, providing scientific support for the northward expansion of mangroves. The results demonstrated significant differences in soil properties and plant morphological characteristics among different stands (p < 0.05). The mean soil carbon stock of restored planted mangroves was 78.75 Mg C/ha (mature stands: 87.84 Mg C/ha; middle-aged stands: 74.09 Mg C/ha; young stands: 74.31 Mg C/ha), while the average plant carbon stock was 12.28 Mg C/ha, indicating that soil is the primary contributor to carbon sequestration in mangroves. Compared to natural mangroves, the restored planted mangroves still exhibited a lower carbon sequestration capacity. The variations in carbon sequestration levels among the planted mangrove forests were mainly attributed to differences in tree species and age composition, hydrothermal conditions, and biomass carbon quantification methods. Key drivers of soil carbon sequestration included total phosphorus content, bulk density, and clay content. Carbon storage in restored planted mangroves depends on short-term soil carbon accumulation and long-term biomass carbon accumulation. Ultimately, we recommend optimal species selection and planting design, improved soil carbon storage mechanisms, and integrated conservation monitoring systems to enhance carbon sequestration in mangrove plantations. Full article