Forests

16 pages, 3127 KiB

Open AccessArticle

Change Patterns of Understory Vegetation Diversity and Rhizosphere Soil Microbial Community Structure in a Chronosequence of Phellodendron chinense Plantations

by Chuan Xie, Peng Song, Zhiyu Zhang, Qiuping Gong, Jiaojiao Wu and Zhipeng Sun

Forests 2025, 16(8), 1298; https://doi.org/10.3390/f16081298 - 8 Aug 2025

Abstract

The effects of Phellodendron chinense plantations on soil properties, microbial characteristics, and the plant diversity across forest age remain poorly understood. In this study, four forest ages (2-, 5-, 8-, and 12-year-old) were examined to compare soil nutrient status, rhizosphere microbial community composition, [...] Read more.

The effects of Phellodendron chinense plantations on soil properties, microbial characteristics, and the plant diversity across forest age remain poorly understood. In this study, four forest ages (2-, 5-, 8-, and 12-year-old) were examined to compare soil nutrient status, rhizosphere microbial community composition, and plant diversity. Our results showed that understory vegetation comprised 56 plant species from 29 families, with species richness significantly increasing with forest age. Rhizosphere soils showed a marked decline in pH and a significant increase in organic carbon, while nutrient dynamics followed distinct trends: P and Mg exhibited continuous accumulation; N displayed unimodal patterns; and K and Ca initially decreased before rising. Microbial community structure shifted significantly with forest age—the dominant bacterial phylum transitioned from Proteobacteria in young stands to Acidobacteriota in mature forests, whereas fungal communities underwent a successional sequence from Basidiomycota (2a) to Ascomycota (5–8a) and finally to Rozellomycota (12a). Correlation analyses demonstrated that plant diversity (S index) was positively correlated with P, K, Ca, and Mg, whereas fungal Shannon diversity was primarily driven by soil N and pH. These findings indicate that forest age mediates plant–soil-microbe interactions through rhizosphere environmental changes. For sustainable plantation management, we recommend (1) dynamically optimizing understory vegetation composition, (2) regulating soil pH and moisture during key growth stages, and (3) selecting compatible companion plants to enhance rhizosphere conditions. Full article

(This article belongs to the Section Forest Soil)

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25 pages, 2136 KiB

Open AccessArticle

Decoding China’s Smart Forestry Policies: A Multi-Level Evaluation via LDA and PMC-TE Index

by Yafang Zhang, Yue Ren, Jiaqi Liu and Yukun Cao

Forests 2025, 16(8), 1297; https://doi.org/10.3390/f16081297 - 8 Aug 2025

Abstract

Smart forestry is gaining global prominence as countries seek to modernize forest governance through digital technologies and data-driven approaches. In China, smart forestry serves as a central pillar of ecological modernization, with policy playing a pivotal role in shaping its development. This study [...] Read more.

Smart forestry is gaining global prominence as countries seek to modernize forest governance through digital technologies and data-driven approaches. In China, smart forestry serves as a central pillar of ecological modernization, with policy playing a pivotal role in shaping its development. This study addresses these gaps by proposing an integrated evaluation framework combining thematic modeling via Latent Dirichlet Allocation (LDA) and structural assessment using the Policy Modeling Consistency–Text Encoder (PMC-TE) index. A total of 82 national and provincial policy documents (2009–2025) were analyzed to identify 13 core topics and categorize instruments into supply-side, demand-side, and environmental types. To assess structural coherence, a PMC-TE index was constructed based on a nine-variable, 32-indicator framework, with results visualized through three-dimensional PMC surfaces. Structural evaluation based on the PMC-TE index indicates that while most policies fall within the “good” or “excellent” range, notable gaps remain between policy objectives and the instruments employed to achieve them. Beyond China, the proposed framework provides a replicable tool for evaluating smart forestry governance in other countries undergoing digital transitions. The findings further highlight the need to enhance demand-side participation, strengthen closed-loop governance mechanisms, and promote cross-sectoral coordination to achieve greater policy coherence. Full article

(This article belongs to the Special Issue Innovation, Transition and Reconstruction of Forestry Oriented by Policies)

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23 pages, 17755 KiB

Open AccessArticle

Estimating Aboveground Biomass of Mangrove Forests in Indonesia Using Spatial Attention Coupled Bayesian Aggregator

by Xinyue Zhu, Zhaohui Xue, Siyu Qian and Chenrun Sun

Forests 2025, 16(8), 1296; https://doi.org/10.3390/f16081296 - 8 Aug 2025

Abstract

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human [...] Read more.

Mangroves play a crucial part in the worldwide blue carbon cycle because they store a lot of carbon in their biomass and soil. Accurate estimation of aboveground biomass (AGB) is essential for quantifying carbon stocks and understanding ecological responses to climate and human disturbances. However, regional-scale AGB mapping remains difficult due to fragmented mangrove distributions, limited field data, and cross-site heterogeneity. To address these challenges, we propose a Spatial Attention Coupled Bayesian Aggregator (SAC-BA), which integrates field measurements with multi-source remote sensing (Landsat 8, Sentinel-1), terrain data, and climate variables using advanced ensemble learning. Four machine learning models (Random Forest (RF), Cubist, Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost)) were first trained, and their outputs were fused using Bayesian model averaging with spatial attention weights and constraints based on Local Indicators of Spatial Association (LISAs), which identify spatial clusters (e.g., high–high, low–low) to improve accuracy and spatial coherence. SAC-BA achieved the highest performance (coefficient of determination (

R^{2}

) = 0.82, root mean square error = 29.90 Mg/ha), outperforming all individual models and traditional BMA. The resulting 30-m AGB map of Indonesian mangroves in 2017 estimated a total of 217.17 ×

10^{6}

Mg, with a mean of 103.20 Mg/ha. The predicted AGB map effectively captured spatial variability, reduced noise at ecological boundaries, and maintained high confidence predictions in core mangrove zones. These results highlight the advantages of incorporating spatial structure and uncertainty into ensemble modeling. SAC-BA provides a reliable and transferable framework for regional AGB estimation, supporting improved carbon assessment and mangrove conservation efforts. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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18 pages, 1904 KiB

Open AccessArticle

A Site Index-Based Approach for Arid Lands: A Multivariate Ecological Assessment for Shrubby Species

by Martín Martínez-Salvador, Alfredo Pinedo-Alvarez, Sandra Rodríguez-Piñeros, Raúl Corrales-Lerma, Ricardo D. Valdez-Cepeda, Fidel Blanco-Macias, Griselda Vazquez-Quintero, David E. Hermosillo-Rojas and Adrián Hernández-Ramos

Forests 2025, 16(8), 1295; https://doi.org/10.3390/f16081295 - 8 Aug 2025

Abstract

Development of site index models for shrubby species in arid ecosystems remains a challenge, due to the absence of dominant height–age relationships and the complexity of ecological drivers in these environments. In this study, a multivariate approach to classify site quality for Agave [...] Read more.

Development of site index models for shrubby species in arid ecosystems remains a challenge, due to the absence of dominant height–age relationships and the complexity of ecological drivers in these environments. In this study, a multivariate approach to classify site quality for Agave lechuguilla Torr, a wild non-timber species of ecological and economic importance in northern Mexico, was performed. Data were collected from 112 sampling plots where the abundance, height, basal diameter, and crown diameter for the A. lechuguilla plants were measured. Sites were grouped into three site index categories (low, medium, and high) using the Importance Value Index (IVI). Afterward a classical discriminant analysis (CDA) was applied to derive linear functions capable of classifying new sites into these predefined categories. Statistical assumptions of multivariate normality, homogeneity of covariance matrices, and low multicollinearity were met. The discriminant functions showed high classification accuracy (95.54%), with full correct classification of low and high site index categories. Additional validation through MANOVA and principal component analysis (PCA) confirmed the separation of groups and the ecological coherence of the selected variables. This approach provides a simple, practical, and replicable model for assessing shrubland site quality using field measurable features. It also offers a tool for sustainable harvesting and conservation of A. lechuguilla. Full article

(This article belongs to the Special Issue Guidelines for Sustainable Forest Management: Vegetation and Soil and Water Conservation in Arid Areas)

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19 pages, 9147 KiB

Open AccessArticle

Evaluating Forest Canopy Structures and Leaf Area Index Using a Five-Band Depth Image Sensor

by Geilebagan, Takafumi Tanaka, Takashi Gomi, Ayumi Kotani, Genya Nakaoki, Xinwei Wang and Shodai Inokoshi

Forests 2025, 16(8), 1294; https://doi.org/10.3390/f16081294 - 8 Aug 2025

Abstract

The objective of the study was to develop and validate a ground-based method using a depth image sensor equipped with depth, visible red, green, blue (RGB), and near-infrared bands to measure the leaf area index (LAI) based on the relative illuminance of foliage [...] Read more.

The objective of the study was to develop and validate a ground-based method using a depth image sensor equipped with depth, visible red, green, blue (RGB), and near-infrared bands to measure the leaf area index (LAI) based on the relative illuminance of foliage only. The method was applied in a Itajii chinkapin (Castanopsis sieboldii (Makino) Hatus. ex T.Yamaz. & Mashiba )forest in Aichi Prefecture, Japan, and validated by comparing estimates with conventional methods (LAI-2200 and fisheye photography). To apply the 5-band sensor to actual forests, a methodology is proposed for matching the color camera and near-infrared camera in units of pixels, along with a method for widening the exposure range through multi-step camera exposure. Based on these advancements, the RGB color band, near-infrared band, and depth band are converted into several physical properties. Employing these properties, each pixel of the canopy image is classified into upper foliage, lower foliage, sky, and non-assimilated parts (stems and branches). Subsequently, the LAI is calculated using the gap-fraction method, which is based on the relative illuminance of the foliage. In comparison with existing indirect LAI estimations, this technique enabled the distinction between upper and lower canopy layers and the exclusion of non-assimilated parts. The findings indicate that the plant area index (PAI) ranged from 2.23 to 3.68 m² m⁻², representing an increase from 33% to 34% compared to the LAI calculated after excluding non-assimilating parts. The findings of this study underscore the necessity of distinguishing non-assimilated components in the estimation of LAI. The PAI estimates derived from the depth image sensor exhibited moderate to strong agreement with the LAI-2200, contingent upon canopy rings (R² = 0.48–0.98), thereby substantiating the reliability of the system’s performance. The developed approaches also permit the evaluation of the distributions of leaves and branches at various heights from the ground surface to the top of the canopy. The novel LAI measurement method developed in this study has the potential to provide precise, reliable foundational data to support research in ecology and hydrology related to complex tree structures. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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28 pages, 8921 KiB

Open AccessArticle

LUNTIAN: An Agent-Based Model of an Industrial Tree Plantation for Promoting Sustainable Harvesting in the Philippines

by Zenith Arnejo, Benoit Gaudou, Mehdi Saqalli and Nathaniel Bantayan

Forests 2025, 16(8), 1293; https://doi.org/10.3390/f16081293 - 8 Aug 2025

Abstract

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation [...] Read more.

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article

(This article belongs to the Section Forest Operations and Engineering)

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14 pages, 2198 KiB

Open AccessArticle

Low-Cost Production of Brazilian Mahogany Clones Based on Indole-3-Butyric Acid Use, Clonal Mini-Hedge Nutrition and Vegetative Propagule Type

by Rafael Barbosa Diógenes Lienard, Annanda Souza de Campos, Lucas Graciolli Savian, Barbara Valentim de Oliveira, Felippe Coelho de Souza and Paulo André Trazzi

Forests 2025, 16(8), 1292; https://doi.org/10.3390/f16081292 - 7 Aug 2025

Abstract

Swietenia macrophylla King, commonly known as Brazilian mahogany, is a high-value neotropical tree species currently threatened due to intensive logging in previous decades. Technologies aimed at clonal production are essential for this species’ conservation and sustainable use at times of climate change and [...] Read more.

Swietenia macrophylla King, commonly known as Brazilian mahogany, is a high-value neotropical tree species currently threatened due to intensive logging in previous decades. Technologies aimed at clonal production are essential for this species’ conservation and sustainable use at times of climate change and increasing demand for ecological restoration. The aim of the present study is to develop a low-cost protocol for mahogany clonal propagation through mini-cutting by assessing clonal mini-hedge nutrition, vegetative propagule type and indole-3-butyric acid (IBA) application effects on rooting and early clone growth. The experiment was conducted in nursery under controlled conditions based on using basal and apical mini-cuttings rooted in a low-cost mini-greenhouse subjected to three nutrient solution concentrations (50%, 100%, and 200%) and five IBA doses (0–8000 ppm). The mini-cutting technique proved efficient and led to over 90% survival after the hardening phase. The 200% nutrient solution concentration allowed balanced performance between cutting types and optimized clonal yield. IBA concentration at 4000 ppm accounted for higher root percentages at the bottom of the tube and the trend towards higher dry biomass production at 160 days. The results highlighted mini-cutting’s potential as a viable mahogany conservation and sustainable production technique. It also supported tropical forestry sector adaptation to challenges posed by climate change. Full article

(This article belongs to the Special Issue The Influence of Environment Changes on Tree Seedlings and Clones Development)

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19 pages, 1883 KiB

Open AccessArticle

Screening and a Comprehensive Evaluation of Pinus elliottii with a High Efficiency of Phosphorus Utilization

by Huan Liu, Zhengquan He, Yuying Yang, Yazhi Zhao, Huiling Chen, Shuxin Chen, Shaoze Wu, Qifu Luan, Renying Zhuo and Xiaojiao Han

Forests 2025, 16(8), 1291; https://doi.org/10.3390/f16081291 - 7 Aug 2025

Abstract

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The [...] Read more.

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

(This article belongs to the Section Forest Ecophysiology and Biology)

17 pages, 4991 KiB

Open AccessArticle

Understory Plant Diversity in Cunninghamia lanceolata (Lamb.) Hook. Plantations Under Different Mixed Planting Patterns

by Minsi Wang, Hongting Guo and Jiang Jiang

Forests 2025, 16(8), 1290; https://doi.org/10.3390/f16081290 - 7 Aug 2025

Abstract

The composition and structure of understory plants are crucial for forest ecosystem succession and stability. This study examined the impact of various Cunninghamia lanceolata mixed plantation patterns on understory biodiversity, aiming to provide a theoretical foundation for sustainable management. Six patterns were evaluated [...] Read more.

The composition and structure of understory plants are crucial for forest ecosystem succession and stability. This study examined the impact of various Cunninghamia lanceolata mixed plantation patterns on understory biodiversity, aiming to provide a theoretical foundation for sustainable management. Six patterns were evaluated using sample plots at Guanshan Forest Farm in Jiangxi Province, China. Understory vegetation diversity, biomass, and soil properties—including total nitrogen, available nitrogen, total phosphorus, available phosphorus, total potassium, available potassium, soil organic matter, and pH—were quantitatively analyzed. Significant differences in diversity among the patterns were revealed. The ‘Cunninghamia lanceolata + Phoebe bournei (Hemsl.) Yen C. Yang + Schima superba Gardner & Champ’ mixed plantation exhibited the most pronounced enhancement of understory plant diversity, whereas the ‘C. lanceolata + Liquidambar formosana Hance’ pattern demonstrated the least significant effects among all treatments. Significant correlations were detected between soil nutrients and diversity indices. Mixed patterns enhance diversity through expanded ecological niches and optimized microenvironments, thereby strengthening ecological functions and management efficiency. Full article

(This article belongs to the Section Forest Biodiversity)

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16 pages, 7600 KiB

Open AccessArticle

Passive Long-Term Acoustic Sampling Reveals Multiscale Temporal Ecological Pattern and Anthropogenic Disturbance of Campus Forests in a High Density City

by Xiaoqing Xu, Xueyao Sun and Hanbin Xie

Forests 2025, 16(8), 1289; https://doi.org/10.3390/f16081289 - 7 Aug 2025

Abstract

Biodiversity conservation and sustainable development in high-density forest urban areas have attracted growing attention and are increasingly recognized as critical for achieving the Sustainable Development Goals (SDGs). University campus forests, functioning as ecological islands, possess unique acoustic characteristics and play a vital role [...] Read more.

Biodiversity conservation and sustainable development in high-density forest urban areas have attracted growing attention and are increasingly recognized as critical for achieving the Sustainable Development Goals (SDGs). University campus forests, functioning as ecological islands, possess unique acoustic characteristics and play a vital role in supporting urban biodiversity. In this case study, acoustic monitoring was conducted over the course of a full year to objectively reveal the ecological patterns across temporal scales of the campus sound environment, by combining acoustic indices’ visualization combined with statistical analysis. The findings indicate (1) the existence of ecological sound patterns across different temporal scales, closely associated with phenological cycles; (2) the identification of the specific timing affected by the different species‘ activities, such as the breeding season of birds, the chirping time of cicadas and other insects, as well as the fluctuations in the intensity of human activities, and (3) the development of a methodological framework integrating a visualization technique with statistical analysis to enhance the understanding of long-term ecological dynamics. The results offer a foundation for promoting the sustainable conservation of campus biodiversity in high-density urban settings. Full article

(This article belongs to the Special Issue Soundscape in Urban Forests—2nd Edition)

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19 pages, 9524 KiB

Open AccessArticle

Shrub Extraction in Arid Regions Based on Feature Enhancement and Transformer Network from High-Resolution Remote Sensing Images

by Hao Liu, Wenjie Zhang, Yong Cheng, Jiaxin He, Haoyun Shao, Sen Bai, Wei Wang, Di Zhou, Fa Zhu, Nuriddin Samatov, Bakhtiyor Pulatov and Aziz Inamov

Forests 2025, 16(8), 1288; https://doi.org/10.3390/f16081288 - 7 Aug 2025

Abstract

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains [...] Read more.

The shrubland ecosystems in arid areas are highly sensitive to global climate change and human activities. Accurate extraction of shrubs using computer vision techniques plays an essential role in monitoring ecological balance and desertification. However, shrub extraction from high-resolution GF-2 satellite images remains challenging due to their dense distribution and small size, along with complex background. Therefore, this study introduces a Feature Enhancement and Transformer Network (FETNet) by integrating the Feature Enhancement Module (FEM) and Transformer module (EdgeViT). Correspondently, they can strengthen both global and local features and enable accurate segmentation of small shrubs in complex backgrounds. The ablation experiments demonstrated that incorporation of FEM and EdgeViT can improve the overall segmentation accuracy, with 1.19% improvement of the Mean Intersection Over Union (MIOU). Comparison experiments show that FETNet outperforms the two leading models of FCN8s and SegNet, with the MIOU improvements of 7.2% and 0.96%, respectively. The spatial details of the extracted results indicated that FETNet is able to accurately extract dense, small shrubs while effectively suppressing interference from roads and building shadows in spatial details. The proposed FETNet enables precise shrub extraction in arid areas and can support ecological assessment and land management. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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27 pages, 5688 KiB

Open AccessReview

Tree Biomass Estimation in Agroforestry for Carbon Farming: A Comparative Analysis of Timing, Costs, and Methods

by Niccolò Conti, Gianni Della Rocca, Federico Franciamore, Elena Marra, Francesco Nigro, Emanuele Nigrone, Ramadhan Ramadhan, Pierluigi Paris, Gema Tárraga-Martínez, José Belenguer-Ballester, Lorenzo Scatena, Eleonora Lombardi and Cesare Garosi

Forests 2025, 16(8), 1287; https://doi.org/10.3390/f16081287 - 7 Aug 2025

Abstract

Agroforestry systems (AFSs) enhance long-term carbon sequestration through tree biomass accumulation. As the European Union’s Carbon Farming Certification (CRCF) Regulation now recognizes AFSs in carbon farming (CF) schemes, accurate tree biomass estimation becomes essential for certification. This review examines field-destructive and remote sensing [...] Read more.

Agroforestry systems (AFSs) enhance long-term carbon sequestration through tree biomass accumulation. As the European Union’s Carbon Farming Certification (CRCF) Regulation now recognizes AFSs in carbon farming (CF) schemes, accurate tree biomass estimation becomes essential for certification. This review examines field-destructive and remote sensing methods for estimating tree aboveground biomass (AGB) in AFSs, with a specific focus on their advantages, limitations, timing, and associated costs. Destructive methods, although accurate and necessary for developing and validating allometric equations, are time-consuming, costly, and labour-intensive. Conversely, satellite- and drone-based remote sensing offer scalable and non-invasive alternatives, increasingly supported by advances in machine learning and high-resolution imagery. Using data from the INNO4CFIs project, which conducted parallel destructive and remote measurements in an AFS in Tuscany (Italy), this study provides a novel quantitative comparison of the resources each method requires. The findings highlight that while destructive measurements remain indispensable for model calibration and new species assessment, their feasibility is limited by practical constraints. Meanwhile, remote sensing approaches, despite some accuracy challenges in heterogeneous AFSs, offer a promising path forward for cost-effective, repeatable biomass monitoring but in turn require reliable field data. The integration of both approaches might represent a valid strategy to optimize precision and resource efficiency in carbon farming applications. Full article

(This article belongs to the Section Forest Inventory, Modeling and Remote Sensing)

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12 pages, 2316 KiB

Open AccessArticle

Rubus magurensis (Rosaceae): A New Bramble Species from the Northern Carpathians (Poland)

by Mateusz Wolanin, Krystyna Musiał and Marcin Nobis

Forests 2025, 16(8), 1286; https://doi.org/10.3390/f16081286 - 6 Aug 2025

Abstract

Rubus magurensis Wolanin, M. Nobis & Oklej. (Rosaceae), a new species from the Northern Carpathians, described and illustrated here, is a tetraploid (2n = 28) belonging to the subgenus Rubus series Micantes. Among the most characteristic features of this species are first-year [...] Read more.

Rubus magurensis Wolanin, M. Nobis & Oklej. (Rosaceae), a new species from the Northern Carpathians, described and illustrated here, is a tetraploid (2n = 28) belonging to the subgenus Rubus series Micantes. Among the most characteristic features of this species are first-year stems that are almost glabrous, leaflets most often arched downward, and inflorescences leafy to the apex with a few simple oval leaves in the upper part, which make this species easy to recognise. This species resembles R. tabanimontanus Figert, from which it differs in having smaller primocane prickles, digitate to subpedate leaves, larger flowers, and inflorescences leafy to the apex. Rubus magurensis is currently known from 11 populations located in southeastern Poland (7 ATPOL 2 × 2 km units). Most of them were found in the central part of the Low Beskid Mts., with two populations located in the northwestern part of the Strzyżów Foothills. Full article

(This article belongs to the Section Forest Ecology and Management)

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18 pages, 4885 KiB

Open AccessArticle

Multiplication of Axillary Shoots of Adult Quercus robur L. Trees in RITA^® Bioreactors

by Paweł Chmielarz, Conchi Sánchez, João Paulo Rodrigues Martins, Juan Manuel Ley-López, Purificación Covelo, María José Cernadas, Anxela Aldrey, Saleta Rico, Jesús María Vielba, Bruce Christie and Nieves Vidal

Forests 2025, 16(8), 1285; https://doi.org/10.3390/f16081285 - 6 Aug 2025

Abstract

Adult trees of pedunculate oak (Quercus robur L.) are recalcitrant to vegetative propagation. In this study, we investigated the micropropagation of five oak genotypes corresponding to trees aged 60–800 years in a liquid medium. We used commercial RITA bioreactors to study the [...] Read more.

Adult trees of pedunculate oak (Quercus robur L.) are recalcitrant to vegetative propagation. In this study, we investigated the micropropagation of five oak genotypes corresponding to trees aged 60–800 years in a liquid medium. We used commercial RITA bioreactors to study the influence of the explant type, the culture medium, shoot support and number of immersions. Variables evaluated included the number of normal and hyperhydric shoots, shoot length, multiplication coefficient and number of rootable shoots per explant. All genotypes could be cultured in temporary immersion. Basal stem sections attached to callus grew better than apical sections and developed less hyperhydricity. For long-term cultivation, Gresshoff and Doy medium was the best of the three media evaluated. All genotypes produced vigorous shoots suitable for rooting and acclimation. This is the first protocol to proliferate adult oak trees in bioreactors, representing significant progress towards large-scale propagation of this and other related species. Full article

(This article belongs to the Section Genetics and Molecular Biology)

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10 pages, 2260 KiB

Open AccessArticle

Multi-Elemental Analysis for the Determination of the Geographic Origin of Tropical Timber from the Brazilian Legal Amazon

by Marcos David Gusmao Gomes, Fábio José Viana Costa, Clesia Cristina Nascentes, Luiz Antonio Martinelli and Gabriela Bielefeld Nardoto

Forests 2025, 16(8), 1284; https://doi.org/10.3390/f16081284 - 6 Aug 2025

Abstract

Illegal logging is a major threat to tropical forests; however, control mechanisms and efforts to combat illegal logging have not effectively curbed fraud in the production chain, highlighting the need for effective methods to verify the geographic origin of timber. This study investigates [...] Read more.

Illegal logging is a major threat to tropical forests; however, control mechanisms and efforts to combat illegal logging have not effectively curbed fraud in the production chain, highlighting the need for effective methods to verify the geographic origin of timber. This study investigates the application of multi-elemental analysis combined with Principal Component Analysis (PCA) to discriminate the provenance of tropical timber in the Brazilian Legal Amazon. Wood samples of Hymenaea courbaril L. (Jatobá), Handroanthus sp. (Ipê), and Manilkara huberi (Ducke) A. Chevalier. (Maçaranduba) were taken from multiple sites. Elemental concentrations were determined via Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and CA was applied to evaluate geographic differentiation. Significant differences in elemental profiles were found among locations, particularly when using the intermediate disk portions (25% to 75%), and especially the average of all five sampled portions, which proved most effective in geographic discrimination of the trunk. Elements such as Ca, Sr, Cr, Cu, Zn, and B were especially important for spatial discrimination. These findings underscore the forensic potential of multi-elemental wood profiling as a tool to support law enforcement and environmental monitoring by providing scientifically grounded evidence of timber origin. Full article

(This article belongs to the Section Wood Science and Forest Products)

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22 pages, 1048 KiB

Open AccessArticle

Forests and Green Transition Policy Frameworks: How Do Forest Carbon Stocks Respond to Bioenergy and Green Agricultural Technologies?

by Nguyen Hoang Dieu Linh and Liang Lizhi

Forests 2025, 16(8), 1283; https://doi.org/10.3390/f16081283 - 6 Aug 2025

Abstract

Forests play a crucial role in storing excess carbon released into the atmosphere. By mitigating climate change, forest carbon stocks play a vital role in achieving green transitions. However, limited information is available regarding the factors that affect forest carbon stocks. The primary [...] Read more.

Forests play a crucial role in storing excess carbon released into the atmosphere. By mitigating climate change, forest carbon stocks play a vital role in achieving green transitions. However, limited information is available regarding the factors that affect forest carbon stocks. The primary objective of this analysis is to investigate the impact of green agricultural technologies and bioenergy on forest carbon stocks. The empirical investigation was conducted using the method of moments quantile regression (MMQR) technique. Results using the MMQR approach indicate that bioenergy is beneficial in augmenting forest carbon stores at all levels. A 1% increase in bioenergy is associated with an increase in forest carbon stocks ranging from 3.100 at the 10th quantile to 1.599 at the 90th quantile. In the context of developing economies, similar findings are observed; however, in developed economies, bioenergy only fosters forest carbon stocks at lower and middle quantiles. In contrast, green agricultural technologies have an adverse effect on forest carbon stocks. Green agricultural technologies have a significant negative impact on forest carbon stocks, particularly between the 10th and 80th quantiles, with their influence declining in magnitude from −2.398 to −0.619. This negative connection is observed in both developed and developing countries at most quantiles, except for higher quantiles in developed economies. Gross domestic product (GDP) has an adverse effect on forest carbon stores only in developing countries, whereas human capital diminishes forest carbon stocks in both developed and developing nations. Governments should provide support for the creators of bioenergy and agroforestry technologies so that forest carbon stocks can be increased. Full article

(This article belongs to the Special Issue Economic Assessment Research in Agroforestry Products, Environmental, and Renewable Resources Issues)

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15 pages, 7500 KiB

Open AccessArticle

Large-Scale Spatiotemporal Patterns of Burned Areas and Fire-Driven Mortality in Boreal Forests (North America)

by Wendi Zhao, Qingchen Zhu, Qiuling Chen, Xiaohan Meng, Kexu Song, Diego I. Rodriguez-Hernandez, Manuel Esteban Lucas-Borja, Demetrio Antonio Zema, Tong Zhang and Xiali Guo

Forests 2025, 16(8), 1282; https://doi.org/10.3390/f16081282 - 6 Aug 2025

Abstract

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically [...] Read more.

Due to climate effects and human influences, wildfire regimes in boreal forests are changing, leading to profound ecological consequences, including shortened fire return intervals and elevated tree mortality. However, a critical knowledge gap exists concerning the spatiotemporal dynamics of fire-induced tree mortality specifically within the vast North American boreal forest, as previous studies have predominantly focused on Mediterranean and tropical forests. Therefore, in this study, we used satellite observation data obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra MCD64A1 and related database data to study the spatial and temporal variability in burned area and forest mortality due to wildfires in North America (Alaska and Canada) over an 18-year period (2003 to 2020). By calculating the satellite reflectance data before and after the fire, fire-driven forest mortality is defined as the ratio of the area of forest loss in a given period relative to the total forest area in that period, i.e., the area of forest loss divided by the total forest area. Our findings have shown average values of burned area and forest mortality close to 8000 km²/yr and 40%, respectively. Burning and tree loss are mainly concentrated between May and September, with a corresponding temporal trend in the occurrence of forest fires and high mortality. In addition, large-scale forest fires were primarily concentrated in Central Canada, which, however, did not show the highest forest mortality (in contrast to the results recorded in Northern Canada). Critically, based on generalized linear models (GLMs), the results showed that fire size and duration, but not the burned area, had significant effects on post-fire forest mortality. Overall, this study shed light on the most sensitive forest areas and time periods to the detrimental effects of forest wildfire in boreal forests of North America, highlighting distinct spatial and temporal vulnerabilities within the boreal forest and demonstrating that fire regimes (size and duration) are primary drivers of ecological impact. These insights are crucial for refining models of boreal forest carbon dynamics, assessing ecosystem resilience under changing fire regimes, and informing targeted forest management and conservation strategies to mitigate wildfire impacts in this globally significant biome. Full article

(This article belongs to the Special Issue Forest Disturbance and Management)

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16 pages, 5519 KiB

Open AccessArticle

The Performance of a Novel Automated Algorithm in Estimating Truckload Volume Based on LiDAR Data

by Mihai Daniel Niţă, Cătălin Cucu-Dumitrescu, Bogdan Candrea, Bogdan Grama, Iulian Iuga and Stelian Alexandru Borz

Forests 2025, 16(8), 1281; https://doi.org/10.3390/f16081281 - 5 Aug 2025

Abstract

Significant improvements in the forest-based industrial sector are expected due to increased digitalization; however, examples of practical implementations remain limited. This study explores the use of an automated algorithm to estimate truckload volumes based on 3D point cloud data acquired using two different [...] Read more.

Significant improvements in the forest-based industrial sector are expected due to increased digitalization; however, examples of practical implementations remain limited. This study explores the use of an automated algorithm to estimate truckload volumes based on 3D point cloud data acquired using two different LiDAR scanning platforms. This research compares the performance of a professional mobile laser scanning (MLS GeoSLAM) platform and a smartphone-based iPhone LiDAR system. A total of 48 truckloads were measured using a combination of manual, factory-based, and digital approaches. Accuracy was evaluated using standard error metrics, including the mean absolute error (MAE) and root mean square error (RMSE), with manual or factory references used as benchmarks. The results showed a strong correlation and no significant differences between the algorithmic and manual measurements when using the MLS platform (MAE = 2.06 m³; RMSE = 2.46 m³). For the iPhone platform, the results showed higher deviations and significant overestimation compared to the factory reference (MAE = 3.29 m³; RMSE = 3.60 m³). Despite these differences, the iPhone platform offers real-time acquisition and low-cost deployment. These findings highlight the trade-offs between precision and operational efficiency and support the adoption of automated measurement tools in timber supply chains. Full article

(This article belongs to the Section Forest Operations and Engineering)

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25 pages, 4069 KiB

Open AccessArticle

Forest Volume Estimation in Secondary Forests of the Southern Daxing’anling Mountains Using Multi-Source Remote Sensing and Machine Learning

by Penghao Ji, Wanlong Pang, Rong Su, Runhong Gao, Pengwu Zhao, Lidong Pang and Huaxia Yao

Forests 2025, 16(8), 1280; https://doi.org/10.3390/f16081280 - 5 Aug 2025

Abstract

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have [...] Read more.

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

(This article belongs to the Special Issue Mapping and Modeling Forests Using Geospatial Technologies)

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