Mi Luo, S. A. Anees, Qiuyan Huang, Xin Qin, Zhihao Qin, Jianlong Fan, Guangping Han, Liguo Zhang, H. Shafri
The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.
{"title":"Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models","authors":"Mi Luo, S. A. Anees, Qiuyan Huang, Xin Qin, Zhihao Qin, Jianlong Fan, Guangping Han, Liguo Zhang, H. Shafri","doi":"10.3390/f15060975","DOIUrl":"https://doi.org/10.3390/f15060975","url":null,"abstract":"The accurate estimation of forest above-ground biomass (AGB) is crucial for sustainable forest management and tracking the carbon cycle of forest ecosystem. Machine learning algorithms have been proven to have great potential in forest AGB estimation with remote sensing data. Though many studies have demonstrated that a single machine learning model can produce highly accurate estimations of forest AGB in many situations, efforts are still required to explore the possible improvement in forest AGB estimation for a specific scenario under study. This study aims to investigate the performance of novel ensemble machine learning methods for forest AGB estimation and analyzes whether these methods are affected by forest types, independent variables, and spatial autocorrelation. Four well-known machine learning models (CatBoost, LightGBM, random forest (RF), and XGBoost) were compared for forest AGB estimation in the study using eight scenarios devised on the basis of two study regions, two variable types, and two validation strategies. Subsequently, a hybrid model combining the strengths of these individual models was proposed for forest AGB estimation. The findings indicated that no individual model outperforms the others in all scenarios. The RF model demonstrates superior performance in scenarios 5, 6, and 7, while the CatBoost model shows the best performance in the remaining scenarios. Moreover, the proposed hybrid model consistently has the best performance in all scenarios in spite of some uncertainties. The ensemble strategy developed in this study for the hybrid model substantially improves estimation accuracy and exhibits greater stability, effectively addressing the challenge of model selection encountered in the forest AGB forecasting process.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jun Jin, Yiyun Luo, Chengyu Liu, Jiajia Zhang, Mengxi Gao, Lingchen Yuan, Bin Hu, D. Feng, Wei Li
Afforestation is an effective approach for restoring degraded ecological functions in the dry-hot valleys of southwest China. Afforestation can affect soil carbon and nitrogen storage; however, how it affects soil P fractions, and their driving factors. is poorly understood in this region. To address these questions, we conducted a field study of Leucaena leucocephala plantations at three different stand age sites (3, 10, and 20 years) and an adjacent natural shrub-grass community control site to investigate changes in soil total phosphorus (Pt), Pi (inorganic phosphorus), Po (organic phosphorus), and phosphorus (P) fractions and their driving factors. Soil Pt, Po, labile P, and moderately labile P significantly increased in the Leucaena leucocephala plantation compared with the natural shrub grass site, and the Leucaena leucocephala plantation increased soil Pt content by significantly increasing soil Po. Soil Pt, Po, Pi, labile P, moderately labile P and non-labile P were not significantly different among the different stages of the Leucaena leucocephala plantation, and soil Pt and its fractions were all significantly higher in the middle-age forest stage of the Leucaena leucocephala plantation. These results indicate that Leucaena leucocephala plantations increased the soil P transformation ability, and soil Po played a critical role in sustaining soil P availability. The middle-age forest stage of Leucaena leucocephala plantations had the best conditions for P stocks and P conversion capacity. The abundance of actinomycetes and fungi showed significant positive relationships with soil Pi fractions (NaHCO3-Pi, NaOH-Pi, and NaOHu.s.-Pi); soil Pt and moderately labile P were significantly and directly influenced by fungal abundance. Soil organic carbon (SOC), NH4+-N, and NO3−-N showed significant and positive relationships with the soil Pi fractions (NaHCO3-Pi, NaHCO3-Po, and HCl-Po). SOC and NO3−-N were the key drivers of soil Pt, labile P, moderately labile P and non-labile fractions. These results indicate that abiotic and biotic factors differently affected the soil P fractions and Pt in Leucaena leucocephala plantations in the dry-hot valley.
{"title":"Phosphorous Fractions in Soils of Natural Shrub-Grass Communities and Leucaena leucocephala Plantations in a Dry-Hot Valley","authors":"Jun Jin, Yiyun Luo, Chengyu Liu, Jiajia Zhang, Mengxi Gao, Lingchen Yuan, Bin Hu, D. Feng, Wei Li","doi":"10.3390/f15060974","DOIUrl":"https://doi.org/10.3390/f15060974","url":null,"abstract":"Afforestation is an effective approach for restoring degraded ecological functions in the dry-hot valleys of southwest China. Afforestation can affect soil carbon and nitrogen storage; however, how it affects soil P fractions, and their driving factors. is poorly understood in this region. To address these questions, we conducted a field study of Leucaena leucocephala plantations at three different stand age sites (3, 10, and 20 years) and an adjacent natural shrub-grass community control site to investigate changes in soil total phosphorus (Pt), Pi (inorganic phosphorus), Po (organic phosphorus), and phosphorus (P) fractions and their driving factors. Soil Pt, Po, labile P, and moderately labile P significantly increased in the Leucaena leucocephala plantation compared with the natural shrub grass site, and the Leucaena leucocephala plantation increased soil Pt content by significantly increasing soil Po. Soil Pt, Po, Pi, labile P, moderately labile P and non-labile P were not significantly different among the different stages of the Leucaena leucocephala plantation, and soil Pt and its fractions were all significantly higher in the middle-age forest stage of the Leucaena leucocephala plantation. These results indicate that Leucaena leucocephala plantations increased the soil P transformation ability, and soil Po played a critical role in sustaining soil P availability. The middle-age forest stage of Leucaena leucocephala plantations had the best conditions for P stocks and P conversion capacity. The abundance of actinomycetes and fungi showed significant positive relationships with soil Pi fractions (NaHCO3-Pi, NaOH-Pi, and NaOHu.s.-Pi); soil Pt and moderately labile P were significantly and directly influenced by fungal abundance. Soil organic carbon (SOC), NH4+-N, and NO3−-N showed significant and positive relationships with the soil Pi fractions (NaHCO3-Pi, NaHCO3-Po, and HCl-Po). SOC and NO3−-N were the key drivers of soil Pt, labile P, moderately labile P and non-labile fractions. These results indicate that abiotic and biotic factors differently affected the soil P fractions and Pt in Leucaena leucocephala plantations in the dry-hot valley.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141281373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.
{"title":"The Influence of Spatial Heterogeneity of Urban Green Space on Surface Temperature","authors":"Mengru Zhang, Jianguo Wang, Fei Zhang","doi":"10.3390/f15050878","DOIUrl":"https://doi.org/10.3390/f15050878","url":null,"abstract":"Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140962460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Youngjin Ko, Cholho Song, Max Fellows, Moon-Jun Kim, Mi-Seon Hong, Werner A. Kurz, Juha Metsaranta, Jiwon Son, Woo-Kyun Lee
Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha−1, and the net primary productivity was 8.30 tC ha−1 year−1. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites.
{"title":"Generic Carbon Budget Model for Assessing National Carbon Dynamics toward Carbon Neutrality: A Case Study of South Korea","authors":"Youngjin Ko, Cholho Song, Max Fellows, Moon-Jun Kim, Mi-Seon Hong, Werner A. Kurz, Juha Metsaranta, Jiwon Son, Woo-Kyun Lee","doi":"10.3390/f15050877","DOIUrl":"https://doi.org/10.3390/f15050877","url":null,"abstract":"Forests play a crucial role in South Korea’s carbon neutrality goal and require sustainable management strategies to overcome age-class imbalances. The Generic Carbon Budget Model (GCBM) offers a spatially explicit approach to simulate carbon dynamics at a regional scale. In this study, we utilized the GCBM to analyze the carbon budget of forests in South Korea and produce spatiotemporal maps for distribution of the forest biomass. The growth parameters of five representative tree species (Pinus densiflora Siebold & Zucc., Larix kaempferi Carr., Pinus koraiensis Siebold & Zucc., Quercus mongolica Fisch. ex Ledeb., Quercus variabilis Blume), which are the main species in South Korea, were used to operate the model. In addition, spatial data for harvest and thinning management activities were used to analyze the effects of anthropogenic activities. In 2020, the aboveground and belowground biomass were 112.98 and 22.84 tC ha−1, and the net primary productivity was 8.30 tC ha−1 year−1. These results were verified using comparison with statistics, a literature review, and MODIS NPP. In particular, broadleaf is higher than conifer forest in net primary production. The Canadian GCBM with Korean forest inventory data and yield curves successfully estimated the aboveground and belowground biomass of forests in South Korea. Our study demonstrates that these estimates can be mapped in detail, thereby supporting decision-makers and stakeholders in analyzing the carbon budget of the forests in South Korea and developing novel schemes that can serve regional and national aims related to forest management, wood utilization, and ecological preservation. Further studies are needed to improve the initialization of dead organic matter pools, given the large-scale afforestation efforts in recent decades that have established South Korea’s forests on predominantly non-forest sites.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Exploring the combined effects of multisensory interactions in open spaces can help improve the comfort of campus environments. Nine typical spaces on a university campus in Fuzhou were selected for this study. Subjects perceived the environment and then completed an on-site subjective questionnaire. At the same time, meteorological data (global radiation, air temperature, globe temperature, wind speed, relative humidity, and illumination intensity) were measured to determine the interactions between visual and acoustic and thermal perceptions. Differences in the meteorological parameters between the measuring points were described using a one-way ANOVA and Tukey’s post hoc test, and a chi-square test of independence was used to determine significant associations between thermal, acoustic, and visual comfort, which in turn led to the study of interactions between visual, acoustic, and thermal comfort using a two-way ANOVA. The following conclusions were drawn: (1) the Thermal Comfort Vote (TCV) increased with the increasing Acoustic Comfort Vote (ACV) at all levels of thermal stress. (2) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type were derived from either “slightly cold” or “warm” conditions. Both the Thermal Comfort Vote (TCV) and the Acoustic Comfort Vote (ACV) were positively correlated. (3) When “neutral”, the Thermal Sensation Vote (TSV) increased with increasing illumination intensity (LUX). (4) The Sunlight Sensation Vote (SSV) increased with the increasing Universal Thermal Climate Index (UTCI) when illumination intensity (LUX) was moderate and bright. (5) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type came from either “slightly cold” or “warm” conditions.
{"title":"Exploring the Multi-Sensory Coupling Relationship of Open Space on a Winter Campus","authors":"Shumin Li, Yijing Zhang, Qiqi Zhang, Pingting Xue, Hao Wu, Wenjian Xu, Jing Ye, Lingyan Chen, Tianyou He, Yushan Zheng","doi":"10.3390/f15050876","DOIUrl":"https://doi.org/10.3390/f15050876","url":null,"abstract":"Exploring the combined effects of multisensory interactions in open spaces can help improve the comfort of campus environments. Nine typical spaces on a university campus in Fuzhou were selected for this study. Subjects perceived the environment and then completed an on-site subjective questionnaire. At the same time, meteorological data (global radiation, air temperature, globe temperature, wind speed, relative humidity, and illumination intensity) were measured to determine the interactions between visual and acoustic and thermal perceptions. Differences in the meteorological parameters between the measuring points were described using a one-way ANOVA and Tukey’s post hoc test, and a chi-square test of independence was used to determine significant associations between thermal, acoustic, and visual comfort, which in turn led to the study of interactions between visual, acoustic, and thermal comfort using a two-way ANOVA. The following conclusions were drawn: (1) the Thermal Comfort Vote (TCV) increased with the increasing Acoustic Comfort Vote (ACV) at all levels of thermal stress. (2) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type were derived from either “slightly cold” or “warm” conditions. Both the Thermal Comfort Vote (TCV) and the Acoustic Comfort Vote (ACV) were positively correlated. (3) When “neutral”, the Thermal Sensation Vote (TSV) increased with increasing illumination intensity (LUX). (4) The Sunlight Sensation Vote (SSV) increased with the increasing Universal Thermal Climate Index (UTCI) when illumination intensity (LUX) was moderate and bright. (5) The highest and lowest Acoustic Sensation Vote (ASV) values for each sound type came from either “slightly cold” or “warm” conditions.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forest ecosystems play a dominant role in regulating climate change and sustainable development [...]
森林生态系统在调节气候变化和可持续发展方面发挥着主导作用[......］
{"title":"The Ecological Management and Sustainable Development of Forests","authors":"Chao Wang, Fan Zhang, Wei Liu","doi":"10.3390/f15050871","DOIUrl":"https://doi.org/10.3390/f15050871","url":null,"abstract":"Forest ecosystems play a dominant role in regulating climate change and sustainable development [...]","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Supply chain security is a major prerequisite for China’s successful industrial modernization, while the digital economy has significantly contributed to industrial transformation and upgrading. This study considers China’s wooden furniture industry as its research object, constructing an evaluation index system of the digital economy and supply chain security of the wooden furniture industry. Then, it studies the impact of the digital economy on supply chain security through theoretical analysis and empirical methods using the two-way fixed model of provinces and time. The findings demonstrate that the digital economy effectively enhances the level of supply chain security in China’s wooden furniture industry, further validating the digital economy’s positive externality impact on the traditional real economy. The impact mechanism test shows that inventory turnover capacity is the focal point for the digital economy to improve the supply chain security of the wood furniture industry, specifying the starting point for that industry’s digital transformation. The heterogeneity findings show that the role of the digital economy in improving the wood furniture industry’s level of supply chain security is more significant in inland areas than in coastal areas. Additional analyses found a threshold effect of the digital economy’s impact on supply chain security, indicating its limitations. This study explores the impact of the digital economy on the real economy from a traditional manufacturing industry, enriching research on the positive externalities of the digital economy as well as providing a reference for traditional manufacturing industries, such as that of wooden furniture, to probe the embedding points of the digital economy and appropriate digital transformation.
{"title":"The Impact of the Digital Economy on Supply Chain Security: Evidence from China’s Wooden Furniture Industry","authors":"Yiyi Luo, Yilin Chen, Chenlu Tao, Chao Yang, Futao Xiang, Chang Xu, Fanli Lin","doi":"10.3390/f15050879","DOIUrl":"https://doi.org/10.3390/f15050879","url":null,"abstract":"Supply chain security is a major prerequisite for China’s successful industrial modernization, while the digital economy has significantly contributed to industrial transformation and upgrading. This study considers China’s wooden furniture industry as its research object, constructing an evaluation index system of the digital economy and supply chain security of the wooden furniture industry. Then, it studies the impact of the digital economy on supply chain security through theoretical analysis and empirical methods using the two-way fixed model of provinces and time. The findings demonstrate that the digital economy effectively enhances the level of supply chain security in China’s wooden furniture industry, further validating the digital economy’s positive externality impact on the traditional real economy. The impact mechanism test shows that inventory turnover capacity is the focal point for the digital economy to improve the supply chain security of the wood furniture industry, specifying the starting point for that industry’s digital transformation. The heterogeneity findings show that the role of the digital economy in improving the wood furniture industry’s level of supply chain security is more significant in inland areas than in coastal areas. Additional analyses found a threshold effect of the digital economy’s impact on supply chain security, indicating its limitations. This study explores the impact of the digital economy on the real economy from a traditional manufacturing industry, enriching research on the positive externalities of the digital economy as well as providing a reference for traditional manufacturing industries, such as that of wooden furniture, to probe the embedding points of the digital economy and appropriate digital transformation.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The soil aggregate is the fundamental unit of soil structure. The fractionation characteristics and influencing factors of phosphorus (P) in soil aggregates inherently link its geochemical characteristics and recycling mechanism. This work investigated the fractionation characteristics of inorganic P in cold temperate forest soils and studied the impacts of recovery periods after forest fires and soil aggregate protection mechanisms on P fractionation. Our results showed that the TP, active P, stable P, and total organic carbon (TOC) contents varied with increasing recovery years after forest fire disturbance. The TP content in the coarse particulate organic matter fraction (cPOM) exhibited an increasing trend with the number of recovery years. Redundancy analysis (RDA) and correlation analysis indicated that TOC played a crucial role in influencing the dynamics of P fractionation during the recovery process. The order of TP levels in different soil aggregate fractions was as follows: μClay > dClay > LF > cPOM > dSilt > μSilt > iPOM, with significant contributions from the cPOM and dSilt fractions. The ranking of P fractions in bulk soils was as follows: ACa-P > Fe-P > Oc-P > Or-P > De-P > Al-P > Ex-P. The protective mechanism of soil aggregates had a more significant effect on TOC than TP, with the order of protective abilities being: Phy×biochem-protected > Biochem-protected > Phy-protected > Non-protected mechanism. TOC and recovery years emerged as critical factors influencing the dynamics of different P fractions during post-fire recovery. Soil aggregate protection mechanisms demonstrated significantly higher effects on TOC than on TP. This study provides insights into the fractionation mechanisms of P in the soil–forest ecosystem of the Greater Khingan Mountains, contributing to the sustainable development and utilization of cold temperate forest ecosystems.
{"title":"Fractionation of Inorganic Phosphorus in Cold Temperate Forest Soils: Associating Mechanisms of Soil Aggregate Protection and Recovery Periods after Forest Fire Disturbance","authors":"Bing Wang, Ruihua Li, Zihao Wang, Rula Sa","doi":"10.3390/f15050875","DOIUrl":"https://doi.org/10.3390/f15050875","url":null,"abstract":"The soil aggregate is the fundamental unit of soil structure. The fractionation characteristics and influencing factors of phosphorus (P) in soil aggregates inherently link its geochemical characteristics and recycling mechanism. This work investigated the fractionation characteristics of inorganic P in cold temperate forest soils and studied the impacts of recovery periods after forest fires and soil aggregate protection mechanisms on P fractionation. Our results showed that the TP, active P, stable P, and total organic carbon (TOC) contents varied with increasing recovery years after forest fire disturbance. The TP content in the coarse particulate organic matter fraction (cPOM) exhibited an increasing trend with the number of recovery years. Redundancy analysis (RDA) and correlation analysis indicated that TOC played a crucial role in influencing the dynamics of P fractionation during the recovery process. The order of TP levels in different soil aggregate fractions was as follows: μClay > dClay > LF > cPOM > dSilt > μSilt > iPOM, with significant contributions from the cPOM and dSilt fractions. The ranking of P fractions in bulk soils was as follows: ACa-P > Fe-P > Oc-P > Or-P > De-P > Al-P > Ex-P. The protective mechanism of soil aggregates had a more significant effect on TOC than TP, with the order of protective abilities being: Phy×biochem-protected > Biochem-protected > Phy-protected > Non-protected mechanism. TOC and recovery years emerged as critical factors influencing the dynamics of different P fractions during post-fire recovery. Soil aggregate protection mechanisms demonstrated significantly higher effects on TOC than on TP. This study provides insights into the fractionation mechanisms of P in the soil–forest ecosystem of the Greater Khingan Mountains, contributing to the sustainable development and utilization of cold temperate forest ecosystems.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140965788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land use and land cover (LULC) change is the variable with the maximum influence on carbon storage in terrestrial ecosystems, due to a fundamental alteration of the ecosystem, structure, function, and variability over time. Understanding the dynamics of aboveground carbon stocks in underway constructions and urban expansions is crucial to provide a basis for land use management and planning. The objective of this study was to analyze the spatiotemporal dynamics of aboveground carbon storage and assess how the LULC change is affected by human intervention, as well as how aboveground carbon stocks respond to these changes in the tropical highland landscape of Jatigede. In this study, changes in aboveground carbon stocks were investigated between 2014 and 2021 by using the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The results revealed that the total aboveground carbon stock decreased between 2014 and 2021. Forests showed the greatest decline in the aboveground carbon stock in terms of space. The primary cause of the reduction in the aboveground carbon stock was the conversion of vegetated land to agricultural and urban land cover. The aboveground carbon stock change was also caused by the continuing construction, which resulted in the extension of construction zones. However, an increase in the aboveground carbon stock was mostly observed in mixed gardens that were close to forest areas. The preservation of mixed gardens as a tree-based agroforestry system can be suggested for enhancing the aboveground carbon stock, as mixed gardens play a significant role in carbon storage in the midst of the increasingly massive deforestation due to the expansion of urban areas.
{"title":"Aboveground Spatiotemporal Carbon Storage Model in the Changing Landscape of Jatigede, West Java, Indonesia","authors":"S. Withaningsih, Annas Dwitri Malik, P. Parikesit","doi":"10.3390/f15050874","DOIUrl":"https://doi.org/10.3390/f15050874","url":null,"abstract":"Land use and land cover (LULC) change is the variable with the maximum influence on carbon storage in terrestrial ecosystems, due to a fundamental alteration of the ecosystem, structure, function, and variability over time. Understanding the dynamics of aboveground carbon stocks in underway constructions and urban expansions is crucial to provide a basis for land use management and planning. The objective of this study was to analyze the spatiotemporal dynamics of aboveground carbon storage and assess how the LULC change is affected by human intervention, as well as how aboveground carbon stocks respond to these changes in the tropical highland landscape of Jatigede. In this study, changes in aboveground carbon stocks were investigated between 2014 and 2021 by using the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The results revealed that the total aboveground carbon stock decreased between 2014 and 2021. Forests showed the greatest decline in the aboveground carbon stock in terms of space. The primary cause of the reduction in the aboveground carbon stock was the conversion of vegetated land to agricultural and urban land cover. The aboveground carbon stock change was also caused by the continuing construction, which resulted in the extension of construction zones. However, an increase in the aboveground carbon stock was mostly observed in mixed gardens that were close to forest areas. The preservation of mixed gardens as a tree-based agroforestry system can be suggested for enhancing the aboveground carbon stock, as mixed gardens play a significant role in carbon storage in the midst of the increasingly massive deforestation due to the expansion of urban areas.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140962276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chunhui Leng, Jiawei Wang, Leiming Dong, Min Yi, Hai Luo, Lu Zhang, Tingxuan Chen, Wenlei Xie, Haiping Xie, Meng Lai
To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine.
{"title":"Radial Variation and Early Prediction of Wood Properties in Pinus elliottii Engelm. Plantation","authors":"Chunhui Leng, Jiawei Wang, Leiming Dong, Min Yi, Hai Luo, Lu Zhang, Tingxuan Chen, Wenlei Xie, Haiping Xie, Meng Lai","doi":"10.3390/f15050870","DOIUrl":"https://doi.org/10.3390/f15050870","url":null,"abstract":"To explore the radial variation in wood properties of slash pine (Pinus elliottii Engelm.) during its growth process and to achieve the early prediction of these properties, our study was carried out in three slash pine harvest-age plantations in Ganzhou, Jian, and Jingdezhen, Jiangxi province of South China. Wood core samples were collected from 360 sample trees from the three plantations. SilviScan technology was utilized to acquire wood property parameters, such as tangential fiber widths (TFWs), radial fiber widths (RFWs), fiber wall thickness (FWT), fiber coarseness (FC), microfibril angle (MFA), modulus of elasticity (MOE), wood density (WD) and ring width (RD). Subsequent systematic analysis focused on the phenotypic and radial variation patterns of wood properties, aiming to establish a clear boundary between juvenile and mature wood. Based on determining the boundary between juvenile and mature wood, a regression equation was used to establish the relationship between the properties of juvenile wood and the ring ages. This relationship was then extended to the mature wood section to predict the properties of mature wood. Our results indicated significant differences in wood properties across different locations. The coefficients of variation for RD and MOE were higher than other properties, suggesting a significant potential for selective breeding. Distinct radial variation patterns in wood properties from the pith to the bark were observed. The boundary between juvenile and mature wood was reached at the age of 22. The prediction models developed for each wood property showed high accuracy, with determination coefficients exceeding 0.87. Additionally, the relative and standard errors between the measured and predicted values were kept below 10.15%, indicating robust predictability. Mature wood exhibited greater strength compared to juvenile wood. The approach of using juvenile wood properties to predict those of mature wood is validated. This method provides a feasible avenue for the early prediction of wood properties in slash pine.","PeriodicalId":12339,"journal":{"name":"Forests","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140968935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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