Forests

26 pages, 1847 KiB

Open AccessArticle

Long-Term Cumulative Effect of Management Decisions on Forest Structure and Biodiversity in Hemiboreal Forests

by Teele Paluots, Jaan Liira, Mare Leis, Diana Laarmann, Eneli Põldveer, Jerry F. Franklin and Henn Korjus

Forests 2024, 15(11), 2035; https://doi.org/10.3390/f15112035 - 18 Nov 2024

Abstract

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the [...] Read more.

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity. Full article

(This article belongs to the Special Issue Forest Diversity, Structure and Functions: Theories, Concepts and Analyses)

14 pages, 2526 KiB

Open AccessArticle

Study on the Spatial–Temporal Variation of Groundwater Depth and Its Impact on Vegetation Coverage in Ejina Oasis

by Dongyang Song, Xiaolong Pei, Lei Mao, Jiangyulong Wang, Ye Tian, Xiaoyu An and Hongyan An

Forests 2024, 15(11), 2034; https://doi.org/10.3390/f15112034 - 18 Nov 2024

Abstract

Ejina, a representative inland river basin situated in the arid region of northwest China, exhibits a delicate ecological environment and its vegetation coverage is intrinsically linked to regional ecological security. Based on MOD13Q1-NDVI data from 2018 to 2023 and groundwater depth monitoring data [...] Read more.

Ejina, a representative inland river basin situated in the arid region of northwest China, exhibits a delicate ecological environment and its vegetation coverage is intrinsically linked to regional ecological security. Based on MOD13Q1-NDVI data from 2018 to 2023 and groundwater depth monitoring data during the same period, this study analyzed the spatial–temporal variation characteristics of vegetation coverage and its relationship with groundwater depth in Ejina. It is found that the vegetation coverage in Ejina is generally low and mainly distributed along the riverbanks in the form of strips. During the study period, the overall trend of vegetation coverage showed a fluctuating pattern of first increasing and then decreasing, revealing the fragility of the regional ecology. The groundwater depth shows the characteristic of being higher in the east river than the west, and the trend of groundwater depth along the river flow is first increasing and then decreasing. The spatial groundwater depth indicates that the east river is higher than that of the west river, and the groundwater depth along the river flow first increases and then decreases. In terms of inter-annual changes, the groundwater depth experiences a process of first decreasing and then stabilizing. Further analysis indicates that vegetation growth and coverage in Ejina are significantly affected by water conditions, and areas with high Normalized Difference Vegetation Index (NDVI) values are mainly distributed along the riverbanks. In addition, there is a certain degree of correlation between groundwater depth and NDVI. When the depth of groundwater is too deep or too shallow, the positive correlation between NDVI and groundwater depth increases slightly and the negative correlation decreases slightly. The findings of this study are of great significance for understanding and predicting the response of vegetation coverage to groundwater changes in arid areas, and provide a scientific basis for water resources management and ecological protection in Ejina. Full article

(This article belongs to the Special Issue Soil Carbon Storage in Forests: Dynamics and Management)

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15 pages, 5030 KiB

Open AccessArticle

Infiltration and Hydrophobicity in Burnt Forest Soils on Mediterranean Mountains

by Jorge Mongil-Manso, Verónica Ruiz-Pérez and Aida López-Sánchez

Forests 2024, 15(11), 2033; https://doi.org/10.3390/f15112033 - 18 Nov 2024

Abstract

Forest fires are a major global environmental problem, especially for forest ecosystems and specifically in Mediterranean climate zones. These fires can seriously impact hydrologic processes and soil erosion, which can cause water pollution and flooding. The aim of this work is to assess [...] Read more.

Forest fires are a major global environmental problem, especially for forest ecosystems and specifically in Mediterranean climate zones. These fires can seriously impact hydrologic processes and soil erosion, which can cause water pollution and flooding. The aim of this work is to assess the effect of forest fire on the hydrologic processes in the soil, depending on soil properties. For this purpose, the infiltration rate has been measured by ring infiltration tester, and the hydrophobicity has been quantified by the “water drop penetration time” method in several soils of burnt and unburnt forest areas in the Mediterranean mountains. The infiltration rates obtained are higher in burnt than in unburnt soils (1130 and 891 mm·h⁻¹, respectively), which contradicts most of the research in Mediterranean climates in southeast Spain with calcareous soils. Burnt soils show no hydrophobicity on the surface, but it is there when the soil is excavated by 1 cm. Additionally, burnt soils reveal a low frequency of hydrophobicity (in less than 30% of the samples) but more severe hydrophobicity (above 300 s); whereas, in unburnt soils, the frequency is higher (50%) but the values of hydrophobicity are lower. The results obtained clearly show the infiltration processes modified by fire, and these results may be useful for land managers, hydrologists, and those responsible for decision-making regarding the forest restoration of burnt land. Full article

(This article belongs to the Section Forest Hydrology)

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21 pages, 21195 KiB

Open AccessArticle

Mapping the Future: Climate-Induced Changes in Aboveground Live-Biomass Carbon Density Across Mexico’s Coniferous Forests

by Carmela Sandoval-García, Jorge Méndez-González, Flores Andrés, Eulalia Edith Villavicencio-Gutiérrez, Fernando Paz-Pellat, Celestino Flores-López, Eladio Heriberto Cornejo-Oviedo, Alejandro Zermeño-González, Librado Sosa-Díaz, Marino García-Guzmán and José Ángel Villarreal-Quintanilla

Forests 2024, 15(11), 2032; https://doi.org/10.3390/f15112032 - 18 Nov 2024

Abstract

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass [...] Read more.

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (_cdAGB) in Mexico’s coniferous forests for 2050 and 2070. We used _cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of _cdAGB were obtained using machine learning techniques with the “caret” library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current _cdAGB prediction maps were generated using the best predictors. Future _cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m². The results indicate _cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha⁻¹, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in _cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the _cdAGB of the country’s coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives)

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17 pages, 3502 KiB

Open AccessArticle

The Driving Factors of the Tradeoff-Synergistic Relationship Among Forest Ecosystem Service Values in the Yangtze River Delta, China

by Shulin Chen and Jian Wu

Forests 2024, 15(11), 2031; https://doi.org/10.3390/f15112031 - 18 Nov 2024

Abstract

The forest ecosystem is one of the planet’s critical ecosystems. Identifying the tradeoff-synergistic relationships among forest ecosystem service values and exploring their driving factors in the Yangtze River Delta are crucial for promoting the optimal overall benefits of regional ecosystem service values and [...] Read more.

The forest ecosystem is one of the planet’s critical ecosystems. Identifying the tradeoff-synergistic relationships among forest ecosystem service values and exploring their driving factors in the Yangtze River Delta are crucial for promoting the optimal overall benefits of regional ecosystem service values and realizing a mutually beneficial scenario that harmonizes regional socio-economic development with ecological and environmental conservation. The forest ecosystem service value in the Yangtze River Delta was evaluated through the improved equivalent factor method. Furthermore, an examination of the tradeoff-synergistic relationship among these ecosystem service values, along with their driving factors, was performed utilizing both the Pearson correlation coefficient method and the Geodetector model. The findings reveal that from 2000 to 2020, the forest ecosystem service values presented a general growth trend in the Yangtze River Delta, with higher values noted in the southern areas and lower values found in the northern regions. The average annual forest ecosystem service value was 279 billion RMB. The tradeoff-synergistic relationship among forest ecosystem service values mainly showed a synergistic relationship, while a significant tradeoff relationship was observed between the values of support and cultural services. The factors influencing the tradeoff-synergistic relationship among forest ecosystem service values included precipitation, normalized difference vegetation index, and temperature. Consequently, local governments should enhance forest coverage, particularly by expanding the regions of evergreen broadleaf, deciduous broadleaf, and coniferous forests. They should also proactively seek ways to realize the value of forest ecosystem services. Full article

(This article belongs to the Special Issue Managing Forests for Multiple Ecosystem Services Under Changing Climate)

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18 pages, 5191 KiB

Open AccessArticle

A Multi-System Coupling Coordination Assessment to Achieve the Integrated Objectives of Forest Conservation, Marine Governance, and Socioeconomic Development in the Bay Area: A Case Study in the Bay Area of the Fujian River Delta

by Zhixun Huang, Yingjie Li, Xiuzhi Chen, Xiang Yu and Wei Shui

Forests 2024, 15(11), 2030; https://doi.org/10.3390/f15112030 - 18 Nov 2024

Abstract

The bay area contains terrestrial forests and coastal mangroves with vital ecosystem functions, which provide essential ecosystem services such as carbon sequestration and biodiversity maintenance. Meanwhile, the bay area usually hosts intensive socioeconomic activities. High-intensity anthropogenic activities in the bay area have threatened [...] Read more.

The bay area contains terrestrial forests and coastal mangroves with vital ecosystem functions, which provide essential ecosystem services such as carbon sequestration and biodiversity maintenance. Meanwhile, the bay area usually hosts intensive socioeconomic activities. High-intensity anthropogenic activities in the bay area have threatened the terrestrial ecosystem and marine environment. Harmonizing the relationship between terrestrial ecosystem conservation, marine environmental governance, and socioeconomic development is crucial for realizing the national “coordinated land and marine development” strategy and promoting sustainability in the bay area. This study constructed a coupling coordination assessment system of the terrestrial ecosystem, marine environmental system, and socioeconomic system. Taking the bay area of the Fujian River Delta as a case study, multiple ecological models were integrated to quantify the coupling coordination degree between these three systems and present its spatial distribution characteristics. Furthermore, the constraint types on the coupling coordination degree were spatially revealed in the bay area. The results suggested that there are significant spatial differences in the coupling coordination degree of the three systems in the bay area of the Fujian River Delta. The areas with a relatively low coupling coordination degree are mainly focused on the central part of the Xiamen Bay area and the southeastern part of the Quanzhou Bay area. Regions with high socioeconomic development tend to present weak terrestrial or marine eco-environmental conditions. The critical constraint factor of the coupling coordination degree in the Zhangzhou Bay area is its backward socioeconomic development level. The backwardness of both the terrestrial ecosystem and marine environmental system exists in most districts of the Xiamen Bay area. In addition, the marine environmental conditions in the Xiamen Bay area are worse than those in the Quanzhou Bay Area and the Zhangzhou Bay area. Full article

(This article belongs to the Special Issue Advances in Forest Cover Change and Its Ecological and Environmental Effects—2nd Edition)

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17 pages, 32903 KiB

Open AccessArticle

Prediction of Wildfire Occurrence in the Southern Forest Regions of China in the Future Scenario

by Jing Li, Duan Huang, Beiping Long, Yakui Shao, Mengwei Xiao, Linhao Sun, Xusheng Li, Aiai Wang, Xuanchi Chen and Weike Li

Forests 2024, 15(11), 2029; https://doi.org/10.3390/f15112029 - 18 Nov 2024

Abstract

In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 [...] Read more.

In the context of global climate warming, climate change is subtly reshaping the patterns of wildfires. Therefore, it is particularly urgent to conduct in-depth research on climate change, wildfires, and their management strategies. This study relies on detailed fire point data from 2001 to 2020, skillfully incorporating a spatial autocorrelation analysis to uncover the mysteries of spatial heterogeneity, while comprehensively considering the influences of multiple factors such as climate, terrain, vegetation, and socioeconomic conditions. To simulate fire conditions under future climates, we adopted the BCC-CSM2-MR climate model, presetting temperature and precipitation data for two scenarios: a sustainable low-development path and a high-conventional-development path. The core findings of the study include the following: (i) In terms of spatial heterogeneity exploration, global autocorrelation analysis reveals a striking pattern: within the southern forest region, 63 cities exhibiting a low–low correlation are tightly clustered in provinces such as Hubei, Anhui, and Zhejiang, while 48 cities with a high–high correlation are primarily distributed in Guangxi and Guangdong. Local autocorrelation analysis further refines this observation, indicating that 24 high–high correlated cities are highly concentrated in specific areas, 14 low–low correlated cities are located in Hainan, and there are only 3 sparsely distributed cities with a low–high correlation. (ii) During the model construction and validation process, this study innovatively adopted the LR-RF-SVM ensemble model, which demonstrated exceptional performance indicators: an accuracy of 91.97%, an AUC value of 97.09%, an F1 score of 92.13%, a precision of 90.75%, and a recall rate of 93.55%. These figures, significantly outperforming those of the single models SVM and RF, strongly validate the superiority of the ensemble learning approach. (iii) Regarding predictions under future climate scenarios, the research findings indicate that, compared to the current fire situation in southern forest areas, the spatial distribution of wildfires will exhibit a noticeable expansion trend. High-risk regions will not only encompass multiple cities in Hunan, Hubei, southern Anhui, all of Jiangxi, and Zhejiang but will also extend northward into southern forest areas that were previously considered low-risk, suggesting a gradual northward spread of fire risk. Notably, despite the relatively lower fire risk in some areas of Fujian Province under the SS585 scenario, overall, the probability of wildfires occurring in 2090 is slightly higher than that in 2030, further highlighting the persistent intensification of forest fire risk due to climate change. Full article

(This article belongs to the Section Natural Hazards and Risk Management)

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14 pages, 2842 KiB

Open AccessArticle

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

by Xinzhu Liu, Change Zheng, Guangyu Wang, Fengjun Zhao, Ye Tian and Hongchen Li

Forests 2024, 15(11), 2028; https://doi.org/10.3390/f15112028 - 18 Nov 2024

Abstract

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing [...] Read more.

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation. Full article

(This article belongs to the Topic Application of Remote Sensing in Forest Fire)

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20 pages, 5686 KiB

Open AccessArticle

Genome-Wide Identification and Characterization of bHLH Gene Family in Hevea brasiliensis

by Zheng Wang, Yuan Yuan, Fazal Rehman, Xin Wang, Tingkai Wu, Zhi Deng and Han Cheng

Forests 2024, 15(11), 2027; https://doi.org/10.3390/f15112027 - 18 Nov 2024

Abstract

The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and [...] Read more.

The basic helix-loop-helix (bHLH) transcription factors play crucial roles in plant growth, development, and stress responses. However, their identification and insights into the understanding of their role in rubber trees remain largely uncovered. In this study, the bHLH gene family was explored and characterized in rubber trees using systematic bioinformatics approaches. In total, 180 bHLH genes were identified in the rubber tree genome, distributed unevenly across 18 chromosomes, and phylogenetic analysis classified these genes into 23 distinct subfamilies. Promoter regions revealed a high density of cis-elements responsive to light and hormones. Enrichment analysis indicated involvement in numerous biological processes, including growth, development, hormone responses, abiotic stress resistance, and secondary metabolite biosynthesis. Protein interaction network analysis identified extensive interactions between HbbHLH genes and other functional genes, forming key clusters related to iron homeostasis, plant growth, and stomatal development. Expression profiling of HbbHLH genes have demonstrated varied responses to endogenous and environmental changes. RT-qPCR of eleven HbbHLH genes in different tissues and under ethylene, jasmonic acid, and cold treatments revealed tissue-specific expression patterns and significant responses to these stimuli, highlighting the roles of these genes in hormone and cold stress responses. These findings establish a framework for exploring the molecular functions of bHLH transcription factors in rubber trees. Full article

(This article belongs to the Special Issue Stress Resistance of Rubber Trees: From Genetics to Ecosystem, 2nd Edition)

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10 pages, 6014 KiB

Open AccessArticle

Physiological Indices of Five Hybrid Larch Seedlings Under Low-Temperature Stress

by Yajing Ning, Wenna Zhao, Chengpeng Cui, Xinxin Zhang, Xin Zhao, Yu Liu, Chen Wang, Hanguo Zhang and Shujuan Li

Forests 2024, 15(11), 2026; https://doi.org/10.3390/f15112026 - 18 Nov 2024

Abstract

Larch is a cold-temperate tree species native to the northern hemisphere and tolerant to low temperatures. It is one of the most significant timber species in Northeast China. This study examined growth changes in hybrid larch seedlings from five lines to explore the [...] Read more.

Larch is a cold-temperate tree species native to the northern hemisphere and tolerant to low temperatures. It is one of the most significant timber species in Northeast China. This study examined growth changes in hybrid larch seedlings from five lines to explore the physiological responses of these seedlings to low-temperature stress. Using 8-month-old hybrids of larch seedlings, we subjected the plants to cold stress at 4 °C and freezing stress at −20 °C over three periods of 6, 12, and 24 h, and treatment at 25 °C was used as a control. Results showed that significant correlations were found among the growth indicators, with larch line 1306 having the lowest incremental growth indicators, the largest root-to-crown ratio, and better cold tolerance than the other larch lines. The levels of soluble sugars (SSs), soluble proteins (SPs), malondialdehyde (MDA), and relative electrolyte leakage (REC) increased significantly in all lines under low-temperature stress. The activities of superoxide dismutase (SOD) and catalase (CAT) showed variation over time. Significant correlations were found between MDA and REL, SS, SR, Pro, CAT, and SOD in most of the lines; no significant correlation was found between MDA and the other indices in lines 1301 and 1309; and significant correlations were found between most of the physiological indices in line 1306. Full article

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

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17 pages, 3025 KiB

Open AccessArticle

A Spectral–Spatial Approach for the Classification of Tree Cover Density in Mediterranean Biomes Using Sentinel-2 Imagery

by Michail Sismanis, Ioannis Z. Gitas, Nikos Georgopoulos, Dimitris Stavrakoudis, Eleni Gkounti and Konstantinos Antoniadis

Forests 2024, 15(11), 2025; https://doi.org/10.3390/f15112025 - 18 Nov 2024

Abstract

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree [...] Read more.

Tree canopy cover is an important forest inventory parameter and a critical component for the in-depth mapping of forest fuels. This research examines the potential of employing single-date Sentinel-2 multispectral imagery, combined with contextual spatial information, to classify areas based on their tree cover density using Random Forest classifiers. Three spatial information extraction methods are investigated for their capacity to acutely detect canopy cover: two based on Gray-Level Co-Occurrence Matrix (GLCM) features and one based on segment statistics. The research was carried out in three different biomes in Greece, in a total study area of 23,644 km². Three tree cover classes were considered, namely, non-forest (cover < 15%), open forest (cover = 15%–70%), and closed forest (cover ≥ 70%), based on the requirements set for fuel mapping in Europe. Results indicate that the best approach identified delivers F1-scores ranging 70%–75% for all study areas, significantly improving results over the other alternatives. Overall, the synergistic use of spectral and spatial features derived from Sentinel-2 images highlights a promising approach for the generation of tree cover density information layers in Mediterranean regions, enabling the creation of additional information in support of the detailed mapping of forest fuels. Full article

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

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28 pages, 31167 KiB

Open AccessArticle

Optimizing GEDI Canopy Height Estimation and Analyzing Error Impact Factors Under Highly Complex Terrain and High-Density Vegetation Conditions

by Runbo Chen, Xinchuang Wang, Xuejie Liu and Shunzhong Wang

Forests 2024, 15(11), 2024; https://doi.org/10.3390/f15112024 - 17 Nov 2024

Abstract

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang [...] Read more.

The Global Ecosystem Dynamics Investigation (GEDI) system provides essential data for estimating forest canopy height on a global scale. However, factors such as complex topography and dense canopy can significantly reduce the accuracy of GEDI canopy height estimations. We selected the South Taihang region of Henan Province, China, as our study area and proposed an optimization framework to improve GEDI canopy height estimation accuracy. This framework includes correcting geolocation errors in GEDI footprints, screening and analyzing features that affect estimation errors, and combining two regression models with feature selection methods. Our findings reveal a geolocation error of 4 to 6 m in GEDI footprints at the orbital scale, along with an overestimation of GEDI canopy height in the South Taihang region. Relative height (RH), waveform characteristics, topographic features, and canopy cover significantly influenced the estimation error. Some studies have suggested that GEDI canopy height estimates for areas with high canopy cover lead to underestimation, However, our study found that accuracy increased with higher canopy cover in complex terrain and dense vegetation. The model’s performance improved significantly after incorporating the canopy cover parameter into the optimization model. Overall, the R² of the best-optimized model was improved from 0.06 to 0.61, the RMSE was decreased from 8.73 m to 2.23 m, and the rRMSE decreased from 65% to 17%, resulting in an accuracy improvement of 74.45%. In general, this study reveals the factors affecting the accuracy of GEDI canopy height estimation in areas with complex terrain and dense vegetation cover, on the premise of minimizing GEDI geolocation errors. Employing the proposed optimization framework significantly enhanced the accuracy of GEDI canopy height estimates. This study also highlighted the crucial role of canopy cover in improving the precision of GEDI canopy height estimation, providing an effective approach for forest monitoring in such regions and vegetation conditions. Future studies should further improve the classification of tree species and expand the diversity of sample tree species to test the accuracy of canopy height estimated by GEDI in different forest structures, consider the distortion of optical remote sensing images caused by rugged terrain, and further mine the information in GEDI waveforms so as to enhance the applicability of the optimization framework in more diverse forest environments. Full article

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

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22 pages, 5059 KiB

Open AccessArticle

Developing a Method to Estimate Above-Ground Carbon Stock of Forest Tree Species Pinus densata Using Remote Sensing and Climatic Data

by Kai Luo, Yafei Feng, Yi Liao, Jialong Zhang, Bo Qiu, Kun Yang, Chenkai Teng and Tangyan Yin

Forests 2024, 15(11), 2023; https://doi.org/10.3390/f15112023 - 16 Nov 2024

Abstract

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore [...] Read more.

Forest above-ground carbon stock (AGCS) is one of the primary ecological evaluation indicators, so it is crucial to estimate the AGCS accurately. In this research, we added the climatic and topographic factors to the estimation process by a remote sensing approach to explore their impact and to achieve more precise estimations. We hope to develop a more accurate estimation method for AGCS based on remote sensing data and climate data. The random forest (RF) method has good robustness and wide applicability. Therefore, we modeled and predicted the AGCS by RF based on sixty field sample plots of Pinus densata pure forests in southwest China and the factors extracted from Landsat 8 OLI images (source I), Sentinel-2A images (source II), and combined Landsat 8 OLI and Sentinel-2A images (source III). We added the topographic and climatic factors to establish the AGCS estimation model and compared the results. The topographic factors contain elevation, slope, and aspect. Climatic factors contain mean annual temperature, annual precipitation, annual potential evapotranspiration, and monthly mean potential evapotranspiration. It was found that the R² and RMSE of the model based on source III were better than the R² and RMSE of the models based on source I and source II. Compared to the models based on source I and source II, the model based on source III improved R² by up to 0.08, reduced RMSE by up to 2.88 t/ha, and improved P by up to 4.29%. Among the models without adding factors, the model based on source III worked the best, with an R² of 0.87, an RMSE of 10.81 t/ha, an rRMSE of 23.19%, and a P of 79.71%. Among the models that added topographic factors, the model based on source III worked best after adding elevation, with an R² of 0.89, an RMSE of 10.01 t/ha, an rRMSE of 21.47%, and a P of 82.17%. Among the models that added climatic factors, the model that added the annual precipitation factor had the best modeling result, with an R² of 0.90, an RMSE of 9.53 t/ha, an rRMSE of 20.59%, and a P of 83.00%. The prediction result exhibited that the AGCS of the Pinus densata forest in 2021 was 9,737,487.52 t. The combination of Landsat 8 OLI and Sentinel-2A could improve the prediction accuracy of the AGCS. The addition of annual precipitation can effectively improve the accuracy of AGCS estimation. Higher resolution of climate data is needed to enhance the modeling in future work. Full article

(This article belongs to the Special Issue Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation)

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18 pages, 11094 KiB

Open AccessArticle

Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau

by Jinkai Gu, Qiang He, Qingwan Li, Qinglin Li, Shengjian Xiang, Wanchi Li, Aohang Jin, Shunbin Wang, Feipeng Liu and Guoyong Tang

Forests 2024, 15(11), 2022; https://doi.org/10.3390/f15112022 - 16 Nov 2024

Abstract

Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting [...] Read more.

Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species. Full article

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

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13 pages, 1945 KiB

Open AccessArticle

A Combination of Traditional and Mechanized Logging for Protected Areas

by Natascia Magagnotti, Benno Eberhard and Raffaele Spinelli

Forests 2024, 15(11), 2021; https://doi.org/10.3390/f15112021 - 16 Nov 2024

Abstract

Teaming draught animals with modern forest machines may offer an innovative low-impact solution to biomass harvesting in protected areas. Machine traffic only occurs on pre-designated access corridors set 50 m apart, while trees are cut with chainsaws and dragged to the corridor’s edge [...] Read more.

Teaming draught animals with modern forest machines may offer an innovative low-impact solution to biomass harvesting in protected areas. Machine traffic only occurs on pre-designated access corridors set 50 m apart, while trees are cut with chainsaws and dragged to the corridor’s edge by draught horses. The operation presented in this study included one chainsaw operator, two draught horses with their driver, an excavator-based processor with its driver and a helper equipped with a chainsaw for knocking off forks and large branches, and a light forwarder (7 t) with his driver. Researchers assessed work productivity and harvesting cost through a time study repeated on 20 sample plots. Descriptive statistics were used to estimate productivity and cost benchmark figures, which were matched against the existing references for the traditional alternatives. The new system achieved a productivity in excess of 4 m³ over bark per scheduled hour (including delays). Harvesting cost averaged EUR 53 m⁻³, which was between 15% and 30% cheaper than the traditional alternatives. What is more, the new system increased labor and horse productivity by a factor of 2 and 7, respectively, which can effectively counteract the increasingly severe shortage of men and animals. Full article

(This article belongs to the Special Issue Biomass and Bioenergy from Forests: Challenges and Prospects for the Future)

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28 pages, 36390 KiB

Open AccessArticle

Scenic Influences on Walking Preferences in Urban Forest Parks from Top-View and Eye-Level Perspectives

by Jiahui Zou, Hongchao Jiang, Wenjia Ying and Bing Qiu

Forests 2024, 15(11), 2020; https://doi.org/10.3390/f15112020 - 16 Nov 2024

Abstract

Urban forest parks offer valuable spaces for walking activities that benefit both physical and mental health. However, trails in current park designs are often underutilised, and the scene layout does not fully meet the preferences of walkers. Therefore, understanding the connection between scene [...] Read more.

Urban forest parks offer valuable spaces for walking activities that benefit both physical and mental health. However, trails in current park designs are often underutilised, and the scene layout does not fully meet the preferences of walkers. Therefore, understanding the connection between scene characteristics and walking preferences is essential. This study aimed to develop an ensemble protocol to assess the role of scene characteristics in walking preferences, using Shanghai Gongqing Forest Park as an illustrative example. A walking preference heat map was created using a combination of crowdsourced GPS data. The scene characteristics were quantified using panoramic photographs, drone orthophotos, computer vision, and deep learning techniques. Taking spatial dependence into account, the key findings include the following: (1) From an overhead view, the shortest paths, waterbody density, and recreational facility selection positively influenced walking preferences, while secondary asphalt trails had a negative effect. (2) At the eye level, aesthetically pleasing landscape elements, such as flowers and bridges, attracted more pedestrians, while closed trails were less favoured. (3) Eye-level features explained 43.5% of the variation in walking preference, with a stronger influence on walking preference compared to 22.4% for overhead features. (4) Natural elements were generally more significant than artificial ones; the feature ranking of significant impact was flowers > NACHr1000 > visual perception > water body density > bridge > SVF > retail > entertainment > asphalt. This study proposes a flexible protocol that provides urban forest park managers and planners with practical tools to create a more walker-friendly environment and more accurate trail alignment, as well as a solid empirical basis for assessing the use of urban forest parks. Full article

(This article belongs to the Special Issue Enhancing Human Well-Being through Urban Forestry: Strategies for Planning, Policies and Management)

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19 pages, 3861 KiB

Open AccessArticle

Predicting Operational Events in Mechanized Weed Control Operations by Offline Multi-Modal Data and Machine Learning Provides Highly Accurate Classification in Time Domain

by Stelian Alexandru Borz and Andrea Rosario Proto

Forests 2024, 15(11), 2019; https://doi.org/10.3390/f15112019 - 15 Nov 2024

Abstract

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in [...] Read more.

Monitoring of operations has become a critical activity in forestry, aiming to provide the data required by planning and production management. Conventional methods, on the other hand, come at a high expense of resources. A neural network was trained, validated, and tested in this study based on multi-modal data to classify relevant operational events in mechanized weed control operations. The architecture of a neural network was tuned in terms of the number of hidden layers and neurons, and the regularization term was set at various values to obtain optimally tuned models for three data modalities: triaxial acceleration data coupled with speed extracted from GNSS signals (AS), triaxial acceleration (A), and speed alone (S). In the training and validation phase, the models based on AS and A achieved a very high classification accuracy, accounting for 92 to 93% when considering four relevant events. In the testing phase, which was run on unseen data, the classification accuracy reached figures of 91 to 92%, indicating a good generalization ability of the models. The results point out that multimodal data are able to provide the features for distinguishing events and add spatial context to the monitored operations, standing as a suitable solution for offline, partly automated monitoring. Future studies are required to see how the capabilities of online, real-time technologies such as deep learning coupled with computer vision can add more context and improve classification performance. Full article

(This article belongs to the Special Issue Sustainable Forest Operations Planning and Management)

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18 pages, 4511 KiB

Open AccessArticle

Spatial Variability of Soil CO₂ Emissions and Microbial Communities in a Mediterranean Holm Oak Forest

by Claudia Di Bene, Loredana Canfora, Melania Migliore, Rosa Francaviglia and Roberta Farina

Forests 2024, 15(11), 2018; https://doi.org/10.3390/f15112018 - 15 Nov 2024

Abstract

Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that [...] Read more.

Forests play a key role in the global carbon (C) cycle through multiple interactions between above-ground and soil microbial communities. Deeper insights into the soil microbial composition and diversity at different spatial scales and soil depths are of paramount importance. We hypothesized that in a homogeneous above-ground tree cover, the heterogeneous distribution of soil microbial functional diversity and processes at the small scale is correlated with the soil’s chemical properties. From this perspective, in a typical Mediterranean holm oak (Quercus ilex L.) peri-urban forest, soil carbon dioxide (CO₂) emissions were measured with soil chambers in three different plots. In each plot, to test the linkage between above-ground and below-ground communities, soil was randomly sampled along six vertical transects (0–100 cm) to investigate soil physico-chemical parameters; microbial processes, measured using Barometric Process Separation (BaPS); and structural and functional diversity, assessed using T-RFLP and qPCR Real Time analyses. The results highlighted that the high spatial variability of CO₂ emissions—confirmed by the BaPS analysis—was associated with the microbial communities’ abundance (dominated by bacteria) and structural diversity (decreasing with soil depth), measured by H′ index. Bacteria showed higher variability than fungi and archaea at all depths examined. Such an insight showed the clear ecological and environmental implications of soil in the overall sustainability of the peri-urban forest system. Full article

(This article belongs to the Special Issue Soil Organic Carbon and Nutrient Cycling in the Forest Ecosystems)

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19 pages, 15862 KiB

Open AccessArticle

Study on the Mechanical Properties and Basic Elastic Constants of Yunnan Dendrocalamus sinicus Chia et J. L. Sun

by Fengwei Zhou, Xingyu Wang, Yanrong Wang, Guofu Li and Chunlei Dong

Forests 2024, 15(11), 2017; https://doi.org/10.3390/f15112017 - 15 Nov 2024

Abstract

Yunnan Dendrocalamus sinicus Chia et J. L. Sun (YDS) is a giant bamboo species with a diameter at breast height of up to nearly 40 cm. It is endemic to Yunnan, China, and only a very small portion of it is directly used [...] Read more.

Yunnan Dendrocalamus sinicus Chia et J. L. Sun (YDS) is a giant bamboo species with a diameter at breast height of up to nearly 40 cm. It is endemic to Yunnan, China, and only a very small portion of it is directly used as load-bearing beams and columns in the dwellings of ethnic minorities, such as in Dai architecture. Due to the structural characteristics of its hollow and thin walls, systematic physical and mechanical property testing of this species faces significant challenges in terms of methods and means. This issue has become one of the main barriers to the realization of its large-scale industrial use. Therefore, this paper systematically tests and studies YDS’s three kinds of strength (tension, compression, and shear), modulus of elasticity, and six Poisson’s ratios with the help of digital image correlation (DIC) technology and self-created material testing methods. The (1) tensile, compressive, and shear strengths and moduli in longitudinal, radial, and chordal directions; (2) tensile strengths and moduli of bamboo green, flesh, and yellow layers in the thickness direction of the bamboo wall; and (3) six Poisson’s ratios under tensile and compressive stresses were obtained for YDS. It was also found that the tensile strength (378.8 MPa) of the green layer of YDS exceeded the yield strength (355 MPa) of 45# steel, making it a potential high-strength engineering material or fiber-reinforced material. Full article

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

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