Search Results (123)

Forest ecosystems serve as pivotal components of the global carbon cycle, with canopy height representing a critical biophysical parameter for quantifying ecosystem functionality, thereby holding substantial implications for forest resource management and carbon sequestration assessments. The precise extraction of ground elevation and vegetation canopy height is essential for advancing topographic and ecological research. The Terrestrial Ecosystem Carbon Inventory Satellite (referred to as TECIS hereafter) offers unprecedented capabilities for the large-scale, high-precision extraction of ground elevation and vegetation canopy height. Using the Northeast China Tiger and Leopard National Park as our study area, we first processed TECIS data to derive topographic and canopy height profiles. Subsequently, the accuracy of TECIS-derived ground and canopy height estimates was validated using onboard light detection and ranging (LiDAR) measurements. Finally, we systematically evaluated the influence of multiple factors on estimation accuracy. Our analysis revealed that TECIS-derived ground and canopy height estimates exhibited mean errors of 0.7 m and −0.35 m, respectively, with corresponding root mean square error (RMSE) values of 3.83 m and 2.70 m. Furthermore, slope gradient, vegetation coverage, and forest composition emerged as the dominant factors influencing canopy height estimation accuracy. These findings provide a scientific basis for optimizing the screening and application of TECIS data in global forest carbon monitoring. Full article

This study evaluates the effectiveness of combining remote sensing techniques with the Random Forest algorithm for estimating the Periodic Annual Increment (PAI) in a dry tropical forest located within the Caatinga biome in northeastern Brazil. The analysis integrates forest inventory data collected from permanent plots monitored between 2011 and 2019 with Landsat satellite imagery processed through the Google Earth Engine platform. By incorporating surface reflectance and vegetation indices, the approach significantly improved the accuracy of productivity estimates while reducing the costs and efforts associated with traditional field-based methods. The Random Forest model achieved a strong performance (R² = 0.8867; RMSE = 0.87), and its predictions were further refined using post-processing correction factors. These results demonstrate the potential of data-driven modeling to support forest monitoring and sustainable management practices, especially in ecosystems vulnerable to the impacts of climate change. Full article

Understanding the complex interplay among altitudinal gradients, tree species diversity, structural attributes, and soil carbon (C) is critical for effective coniferous forest management and climate change mitigation. This study addresses a knowledge gap by investigating the effects of altitudinal gradient on coniferous tree diversity, biomass, carbon stock, regeneration, and soil organic carbon storage (SOCs) in the understudied temperate forests of the Hindu-Kush Kalam Valley. Using 120 sample plots 20 × 20 m (400 m²) each via a field inventory approach across five altitudinal gradients [E1 (2000–2200 m)–E5 (2801–3000 m)], we comprehensively analyzed tree structure, composition, and SOCs. A total of four coniferous tree species and 2172 individuals were investigated for this study. Our findings reveal that elevation indirectly influences species diversity, SOCs, and forest regeneration. Notably, tree height has a positive relationship with altitudinal gradients, while tree carbon stock exhibits an inverse relationship. Forest disturbance was high in the middle elevation gradients E2–E4, with high deforestation rate at E1 and E2. Cedrus deodara, the dominant species, showed the highest deforestation rate at lower elevations (R² = 0.72; p < 0.05) and regeneration ability (R² = 0.77; p < 0.05), which declined with increasing elevation. Middle elevations had the highest litter carbon stock and SOCs values emphasizing the critical role of elevation gradients in carbon sink and species distribution. The regeneration status and number of trees per ha in Kalam Valley forests showed a significant decline with increasing elevation (p < 0.05), with Cedrus deodara recording the highest regeneration rate at E1 and Abies pindrow the lowest at E5. The PCA revealed that altitudinal gradients factor dominate variability via PCA1, while the Shannon and Simpson Indices drives PCA2, highlighting ecological diversity’s independent role in shaping distinct yet complementary vegetative and ecological perspectives. This study reveals how altitudinal gradients shape forest structure and carbon sequestration, offering critical insights for biodiversity conservation and climate-resilient forest management. Full article

Coral reefs are well known for their diversity and value, providing habitats for a third of marine species within just 0.2% of the ocean. However, these natural habitats face significant threats and degradation, leading to unresolved issues related to coral loss inventory, coral protection, and the implementation of long-term conservation policies. In this study, we examined two decades of changes in coral spatial distribution within the Nha Trang Bay Marine Protected Area (MPA) using remote sensing and machine learning (ML) approaches. We identified various factors contributing to coral reef loss and analyzed the effectiveness of management policies over the past 20 years. By employing the Light Gradient Boosting Machine (LGBM) and Deep Forest (DF) models on Landsat (2002, κ = 0.83, F₁ = 0.85) and Planet (2016, κ = 0.89, F₁ = 0.82; 2024, κ = 0.92, F₁ = 0.86) images, we achieved high confidence in our inventory of coral changes. Our findings revealed that 191.38 hectares of coral disappeared from Nha Trang Bay MPA between 2002 and 2024. The 8-year period from 2016 to 2024 saw a loss of 66.32 hectares, which is in linear approximation to the 125.06 hectares lost during the 14-year period from 2002 to 2016. It is concluded that the key factors contributing to coral loss include land-use dynamics, global warming, and the impact of starfish. To address these challenges, we propose next a modern community-based management paradigm to enhance the conservation of existing coral reefs and protect potential habitats within Nha Trang Bay MPA. Full article

This study assessed the structure and composition of a Miombo woodland stand subjected to selective logging through a forest inventory, measuring all trees with DBH ≥ 10 cm across 34 plots (1 ha each) for diameter, height, stem quality, and health status. The stand had a mean stem density of 255 stems/ha, basal area of 15 m²/ha, above ground biomass of 110 Mg/ha, and total volume of 145 m³/ha. The Fabaceae family, particularly Brachystegia spiciformis, dominated the composition. Diversity indices revealed moderate diversity (Shannon = 2.3, Simpson = 0.8, Pielou = 0.6), with a few dominant species. The diameter distribution followed a reverse J-shaped pattern typical of Miombo woodlands. The study (LevasFlor. (2024). Plano De Maneio Da LevasFlor, LDA) highlighted common features of selectively logged woodlands, including a low occurrence of large-diameter individuals from high-value commercial species, prevalence of disturbance-tolerant species, and limited regeneration for some species. These findings underscore the need for management strategies that balance ecological and socio-economic factors, mitigate logging impacts, promote regeneration, and ensure long-term sustainability. Effective policies are crucial for maintaining the ecological integrity and economic value of Miombo woodlands while addressing climate resilience and biodiversity conservation. Full article

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r² = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r² = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling. Full article

Wildfire activity in the western U.S. has highlighted the importance of effective management to address this growing threat. The Lookout Mountain Thinning and Fuels Reduction Study (LMS) is an operational-scale, long-term study of the effects of forest restoration and fuel reduction treatments in ponderosa pine (Pinus ponderosa Dougl. ex Laws.) and mixed-conifer forests in central Oregon, U.S. The broad objectives of the LMS are to examine the effectiveness and longevity of treatments on wildfire risk and to assess the collateral effects. Treatments include four levels of overstory thinning followed by mastication of the understory vegetation and prescribed burning. Stands were thinned to residual densities of 50, 75, or 100% of the upper management zone (UMZ), which accounts for site differences as reflected by stand density relationships for specific plant communities. A fourth treatment combines the 75 UMZ with small gaps (~0.1 ha) to facilitate regeneration (75 UMZ + Gaps). A fifth treatment comprises an untreated control (UC). We examined the causes and levels of tree mortality that occurred 2–9 years after treatments. A total of 391,292 trees was inventoried, of which 2.3% (9084) died. Higher levels of tree mortality (all causes) occurred on the UC (7.1 ± 1.9%, mean ± SEM) than on the 50 UMZ (0.7 ± 0.1%). Mortality was attributed to several bark beetle species (Coleoptera: Curculionidae) (4002 trees), unknown factors (2682 trees), wind (1958 trees), suppression (327 trees), snow breakage (61 trees), prescribed fire (19 trees), western gall rust (15 trees), cankers (8 trees), mechanical damage (5 trees), dwarf mistletoe (4 trees), and woodborers (3 trees). Among bark beetles, tree mortality was attributed to western pine beetle (Dendroctonus brevicomis LeConte) (1631 trees), fir engraver (Scolytus ventralis LeConte) (1580 trees), mountain pine beetle (Dendroctonus ponderosae Hopkins) (526 trees), engraver beetles (Ips spp.) (169 trees), hemlock engraver (Scolytus tsugae (Swaine)) (77 trees), and Pityogenes spp. (19 trees). Higher levels of bark beetle-caused tree mortality occurred on the UC (2.9 ± 0.7%) than on the 50 UMZ (0.3 ± 0.1%) which, in general, was the relationship observed for individual bark beetle species. Higher levels of tree mortality were attributed to wind on the 100 UMZ (1.0 ± 0.2%) and UC (1.2 ± 1.5%) than on the 50 UMZ (0.2 ± 0.02%) and 75 UMZ (0.4 ± 0.1%). Higher levels of tree mortality were attributed to suppression on the UC (0.5 ± 0.3%) than on the 50 UMZ (0.003 ± 0.002%) and 75 UMZ + Gaps (0.0 ± 0.0%). Significant positive correlations were observed between measures of stand density and levels of tree mortality for most causal agents. Tree size (diameter at 1.37 m) frequently had a significant effect on tree mortality, but relationships varied by causal agent. The forest restoration and fuels reduction treatments implemented on the LMS increased resistance to multiple disturbances. The implications of these and other results to the management of fire-adapted forests are discussed. Full article

High-standing islands, such as Taiwan, offer unique opportunities to study soil organic carbon (SOC) dynamics due to their steep terrains, rapid erosion, and strong climatic gradients. In this study, we investigated 54 forest soil profiles across northern, central, and southern Taiwan to assess SOC inventories and turnover using stable carbon isotope (δ¹³C) analyses. We applied Rayleigh fractionation modeling to vertical δ¹³C enrichment patterns and derived the parameter β, which serves as a proxy for SOC turnover rates. Our findings reveal that SOC stocks increase notably with elevation, aligning with lower temperatures and reduced decomposition rates at higher altitudes. Conversely, mean annual precipitation (MAP) did not show a straightforward relationship with SOC stocks or β, highlighting the moderating effects of soil drainage, topography, and local hydrological conditions. Intriguingly, higher soil nitrogen levels were associated with a negative correlation to ln(β), underscoring the complex interplay between nutrient availability and SOC decomposition. Overall, temperature emerges as the dominant factor governing SOC turnover, indicating that ongoing and future warming could accelerate SOC losses, especially in cooler, high-elevation zones currently acting as stable carbon reservoirs. These insights underscore the need for models and management practices that account for intricate temperature, moisture, and nutrient controls on SOC stability, as well as the value of stable isotopic tools for evaluating soil carbon dynamics in mountainous environments. Full article

The connectivity of forest ecosystems is increasingly recognized as a key factor in evaluating the sustainability of forest management, with significant implications for biodiversity conservation. This study examines the impact of afforestation programs on forest evolution, fragmentation, and connectivity in León province, Spain, over the past 25 years (1996–2020). Three scenarios were modeled across two periods (1996–2006 and 2006–2020), integrating data from the national forest inventories (IFN2, IFN3, and IFN4) and afforestation program records provided by the Junta de Castilla y León. The evolution of connectivity “with” and “without” afforestation was analyzed using Graphab 2.6 and graph theory, and several connectivity metrics were calculated. The first period analyzed, influenced by the two initial afforestation programs, corresponded to the end of a forest expansion phase, followed by a decrease in tree cover. Despite this reduction, a net positive balance of up to 24% of all connectivity metrics (NC, PC, Flux, and ECA) was observed throughout the study period. Afforestation in mountain areas enhanced tree cover continuity, resulting in a more homogeneous but less diverse landscape. Conversely, afforestation in agricultural lands increased landscape heterogeneity, diversifying and extending the ecological network of connections. These programs have played a crucial role in shaping the landscape, influencing its diversity and the evolution of forest connectivity. Legislation grounded in technical and ecological principles should be prioritized as a strategic tool to address pressing land management challenges and preserve natural values. Full article

Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant challenge due to the volatile and multi-faceted nature of sales data. Existing methods often struggle to capture intricate patterns driven by seasonal trends, external factors, and consumer behavior, hindering effective inventory management and strategic decision-making. To overcome these challenges, we propose STL-DCSInformer-ETS, a hybrid model that integrates three complementary components: STL decomposition, an enhanced DCSInformer model, and the ETS model. The model uses monthly sales data from a FMCG company, with key features including sales volume, product prices, promotional activities, and regulatory factors such as holidays, geographical information, consumer behavior, product factors, etc. STL decomposition partitions time-series data into trend, seasonal, and residual components, reducing data complexity and enabling more targeted forecasting. The enhanced DCSInformer employs dilated causal convolution and a multi-scale feature extraction mechanism to capture long-term dependencies and short-term variations effectively. Meanwhile, the ETS model specializes in modeling seasonal patterns, further refining forecasting precision. To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. RF-RFE identifies key predictive factors from multiple dimensions, such as time, geography, and economy, which significantly influence forecasting accuracy. Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. Furthermore, the model effectively captures both medium- and long-term sales trends while addressing short-term fluctuations, leading to more accurate forecasting and improved decision-making for fast-moving consumer goods. This research provides new theoretical insights into hybrid forecasting models and practical solutions for optimizing inventory management and strategic planning in the FMCG industry. Full article

Natural broadleaf forests (NBFs) are the most abundant zonal vegetation type in subtropical regions. Understanding the mechanisms influencing stand productivity in NBFs is important for developing “nature-based” solutions for climate change mitigation. However, minimal research has captured the effects of nonlinearities and feature interactions that often have nonlinear impacts on stand productivity and influencing factors. To address this research gap, we used continuous forest inventory data, and a machine learning model for stand productivity of NBFs was constructed. Subsequently, through leveraging the interpretable machine learning framework of the SHapley Additive explanation (SHAP) and partial dependence plot, we determined global and local explanations of the influencing factors of stand productivity. Our findings indicate the following: (1) The Autogluon model performed the strongest based on R², RMSE, and rRMSE metrics. (2) The basal area (BA), neighborhood comparison of diameter at breast height (NC), and stand age (AGE) were the key influencing factors. Stand productivity increased with increasing BA and decreased with increasing NC and AGE. BA was maintained above 15 m²ha⁻¹ and NC was maintained below 0.45, which represent favorable conditions for NBFs to maintain optimal growth. (3) SHAP interaction values were calculated to determine the effects of the five major interactions on stand productivity. Our study provides a reference for the sustainable management of NBFs, thereby highlighting the important role of forests in mitigating climate change. Full article

Wetlands play a crucial role in carbon sequestration. The integration of wetland carbon dynamics into landscape architecture, however, has been challenging, mainly due to gaps between scientific knowledge and landscape practice norms. While the carbon performance of different wetland types is well established in the ecological sciences literature, our study pioneers the translation of this scientific understanding into actionable landscape design guidance. We achieve this through a comprehensive, spatially explicit analysis of wetland carbon dynamics using 2024 National Wetlands Inventory data and other spatial datasets. We analyze carbon flux rates across 13 distinct wetland types in Illinois to help quantify useful information related to designing for carbon outcomes. Our analysis reveals that in Illinois, bottomland forests function as primary carbon sinks (709,462 MtC/year), while perennial deepwater rivers act as significant carbon emitters (−2,573,586 MtC/year). We also identify a notable north–south gradient in sequestration capacity, that helps demonstrate how regional factors influence wetland and other stormwater management design strategies. The work provides landscape architects with evidence-based parameters for evaluating carbon sequestration potential in wetland design decisions, while also acknowledging the need to balance carbon goals with other ecosystem services. This research advances the profession’s capacity to move beyond generic sustainable design principles toward quantifiable climate-responsive solutions, helping landscape architects make informed decisions about wetland type selection and placement in the context of climate change mitigation. Full article

Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (r up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m² were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. Full article

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley’s K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks’ dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM. Full article

Moso bamboo (Phyllostachys edulis (Carrière) J. Houzeau), which is native to China, is considered to be an invasive species due to its powerful asexual reproductive capabilities that allow it to rapidly spread into neighboring ecosystems and replace existing plant communities. In the absence of human intervention, it remains poorly understood how indigenous moso bamboo forests naturally expand into surrounding areas over the long term, and whether these patterns vary with environmental changes. Using multi-year forest resource inventory data, we extracted moso bamboo patches that emerged from 2010 to 2020 and proposed a bamboo expansion index to calculate the average rate of patch expansion during this period. Using the first global 30 m land-cover dynamic monitoring product with a fine classification system, we assessed the expansion speeds of moso bamboo into various areas, particularly forests with different canopy closures and categories. Using parameter-optimized geographic detectors, we explored the significance of multi-factors in the expansion process. The results indicate that the average expansion rate of moso bamboo forests in China is 1.36 m/y, with evergreen broadleaved forests being the primary area for invasion. Moso bamboo expands faster into open forest types (0.15 < canopy closure < 0.4), shrublands, and grasslands. The importance of factors influencing the expansion rate is ranked as follows: temperature > chemical properties of soil > light > physical properties of soil > moisture > atmosphere > terrain. When considering interactions, the primary factors contributing to expansion rates include various climate factors and the combined effect of climate factors and soil factors. Our work underscores the importance of improving the quality and density of native vegetation, such as evergreen broadleaved forests. Effective management strategies, including systematic monitoring of environmental variables, as well as targeted interventions like bamboo removal and soil moisture control, are essential for mitigating the invasion of moso bamboo. Full article