Pub Date : 2024-07-01DOI: 10.3389/ffgc.2024.1432739
D. Diaconu, Ion Andronache, Andrei-Rafael Gruia, Titus Bazac, Aurel-Mihail BãloI
Logging causes the fragmentation of areas with direct implications for hydrological processes, landslides, or habitats. The assessment of this fragmentation process plays an important role in the planning of future logging, reconstruction, and protection measures for the whole ecosystem. The methodology used includes imaging techniques applying two fractal indices: the Fractal Fragmentation Index (FFI) and the Fractal Fragmentation and Disorder Index (FFDI). The results showed the annual evolution and disposition of deforested areas. Only 3% of deforestation resulted in the fragmentation and splitting of forest plots. The remaining 97% resulted in the reduction of existing compact stands without fragmentation. The method has many advantages in quantifying the spatial evolution of forests, estimating the capture of carbon emissions and establishing sustainability of bird and animal habitats. The analysis took place in the Eastern Carpathians, in Romania, in the time period of 2001–2022.
{"title":"Evaluation of forest loss data using fractal algorithms: case study Eastern Carpathians–Romania","authors":"D. Diaconu, Ion Andronache, Andrei-Rafael Gruia, Titus Bazac, Aurel-Mihail BãloI","doi":"10.3389/ffgc.2024.1432739","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1432739","url":null,"abstract":"Logging causes the fragmentation of areas with direct implications for hydrological processes, landslides, or habitats. The assessment of this fragmentation process plays an important role in the planning of future logging, reconstruction, and protection measures for the whole ecosystem. The methodology used includes imaging techniques applying two fractal indices: the Fractal Fragmentation Index (FFI) and the Fractal Fragmentation and Disorder Index (FFDI). The results showed the annual evolution and disposition of deforested areas. Only 3% of deforestation resulted in the fragmentation and splitting of forest plots. The remaining 97% resulted in the reduction of existing compact stands without fragmentation. The method has many advantages in quantifying the spatial evolution of forests, estimating the capture of carbon emissions and establishing sustainability of bird and animal habitats. The analysis took place in the Eastern Carpathians, in Romania, in the time period of 2001–2022.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"14 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141700218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-14DOI: 10.3389/ffgc.2024.1405430
Kristiina Palm-Hellenurm, Endijs Bāders, Lee E. Frelich, Kajar Köster, Marek Metslaid, Olga Polyachenko, Meelis Seedre, Ekaterina Shorohova, John A. Stanturf, Floortje Vodde, Kalev Jõgiste
Trees that survive disturbances are important biological legacies that facilitate forests’ recovery and enhance their structural and species diversity, substantially contributing to the resilience of these ecosystems. The dynamic pattern of legacy syndromes sets the understudied aspects of survivors of wind disturbance into focus. Several factors at tree, stand, and landscape scales alter the susceptibility of the remnant trees, and affect their potential to recover and survive subsequent disturbances. The characteristics of the survivors interact with direct stress and mortality drivers such as changed environmental conditions and pressure by pests and pathogens. Climate change further enhances the post-storm vulnerability of the remaining stand. This literature review analyzes the impact of disturbance parameters (e.g., severity, seasonal timing) and characteristics of the affected forest (e.g., tree species composition, successional stage of a forest stand) on the conditions of survivors through post-windthrow stand development. We attempted to reveal the main agents and processes driving the fate of remnant trees and linked delayed mortality patterns to the main stand-scale wind disturbance regimes in Eurasian and North American boreal and temperate forests: (1) stand-replacing, (2) partially stand-replacing, and (3) fine-scale gap disturbance. We found that after stand-replacing wind disturbance, the spatial location of the remaining trees largely determines their onward fate, whereas these survivors are generally more susceptible to subsequent mortality compared to trees that survived less severe events. After partially stand-replacing wind disturbance, the structure of the remnant stand as well as characteristics of the individual remnant trees (e.g., species, age, size) largely determine their survival probability. Following a fine-scale gap disturbance, the trees at the gap edge are more likely to die, compared to the trees situated in the stand interior, but the mortality-causing processes usually operate on a longer time scale. Our findings contribute to the current knowledge on post-windthrow stand development and offer insights into temporal stability of these increasingly important biological legacies.
{"title":"The fate of remnant trees after wind disturbances in boreal and temperate forests","authors":"Kristiina Palm-Hellenurm, Endijs Bāders, Lee E. Frelich, Kajar Köster, Marek Metslaid, Olga Polyachenko, Meelis Seedre, Ekaterina Shorohova, John A. Stanturf, Floortje Vodde, Kalev Jõgiste","doi":"10.3389/ffgc.2024.1405430","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1405430","url":null,"abstract":"Trees that survive disturbances are important biological legacies that facilitate forests’ recovery and enhance their structural and species diversity, substantially contributing to the resilience of these ecosystems. The dynamic pattern of legacy syndromes sets the understudied aspects of survivors of wind disturbance into focus. Several factors at tree, stand, and landscape scales alter the susceptibility of the remnant trees, and affect their potential to recover and survive subsequent disturbances. The characteristics of the survivors interact with direct stress and mortality drivers such as changed environmental conditions and pressure by pests and pathogens. Climate change further enhances the post-storm vulnerability of the remaining stand. This literature review analyzes the impact of disturbance parameters (e.g., severity, seasonal timing) and characteristics of the affected forest (e.g., tree species composition, successional stage of a forest stand) on the conditions of survivors through post-windthrow stand development. We attempted to reveal the main agents and processes driving the fate of remnant trees and linked delayed mortality patterns to the main stand-scale wind disturbance regimes in Eurasian and North American boreal and temperate forests: (1) stand-replacing, (2) partially stand-replacing, and (3) fine-scale gap disturbance. We found that after stand-replacing wind disturbance, the spatial location of the remaining trees largely determines their onward fate, whereas these survivors are generally more susceptible to subsequent mortality compared to trees that survived less severe events. After partially stand-replacing wind disturbance, the structure of the remnant stand as well as characteristics of the individual remnant trees (e.g., species, age, size) largely determine their survival probability. Following a fine-scale gap disturbance, the trees at the gap edge are more likely to die, compared to the trees situated in the stand interior, but the mortality-causing processes usually operate on a longer time scale. Our findings contribute to the current knowledge on post-windthrow stand development and offer insights into temporal stability of these increasingly important biological legacies.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"71 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pinus is a genus of great economic and ecological importance, and its members are dominant components of forests throughout the world. During the long evolutionary “arms race,” plants have developed complex and diverse systemic defense mechanisms to strategically and intelligently compete with herbivores. To study the alteration pattern and defensive response mechanism triggered by herbivorous feeding stimuli, we firstly built a biological model of the interrelationship between the Chinese pine (Pinus tabuliformis Carr.) and the Chinese pine caterpillar (Dendrolimus tabulaeformis Tsai et Liu). This model integrated proteomic and phosphoproteomic data, which were then normalized and combined with bioinformatics tools to evaluate and analyze changes in the phosphoproteomic profile in response to the caterpillar’s feeding stimulus on pine needles. Systematic identification of differentially significant phosphorylated proteins implicated in the pine’s defense mechanism against caterpillar stress was conducted. Furthermore, we predicted upstream kinases of phosphorylation sites and their activities. Through an analysis of Motif patterns of phosphorylated proteins, Mfuzz clustering of phosphorylation sites, and kinase regulatory networks, we explored the functional modules of phosphorylated protein interaction networks in response to stress within pine. In general, our study emphasized the significant role of kinase METK2, PTI12, PGK, as well as At3g59480 for the first time. The identification of these phosphorylated proteins was additionally confirmed through parallel reaction monitoring technology. Furthermore, genes associated with differentially expressed proteins were validated through real-time quantitative polymerase chain reaction detection. This investigation aids in understanding the mechanisms behind resistance formation and regulation of caterpillar feeding incentives in pine. Breeding more resistant pine varieties may benefit from a fuller understanding of these defense strategies in the future.
{"title":"Defense response to caterpillar feeding stress in wild Pinus tabuliformis unveiled by quantitative integrated proteomic and phosphoproteomic analyses","authors":"Tianhua Sun, Yanan Zhao, Guona Zhou, Suhong Gao, Junxia Liu, Baojia Gao","doi":"10.3389/ffgc.2024.1356511","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1356511","url":null,"abstract":"Pinus is a genus of great economic and ecological importance, and its members are dominant components of forests throughout the world. During the long evolutionary “arms race,” plants have developed complex and diverse systemic defense mechanisms to strategically and intelligently compete with herbivores. To study the alteration pattern and defensive response mechanism triggered by herbivorous feeding stimuli, we firstly built a biological model of the interrelationship between the Chinese pine (Pinus tabuliformis Carr.) and the Chinese pine caterpillar (Dendrolimus tabulaeformis Tsai et Liu). This model integrated proteomic and phosphoproteomic data, which were then normalized and combined with bioinformatics tools to evaluate and analyze changes in the phosphoproteomic profile in response to the caterpillar’s feeding stimulus on pine needles. Systematic identification of differentially significant phosphorylated proteins implicated in the pine’s defense mechanism against caterpillar stress was conducted. Furthermore, we predicted upstream kinases of phosphorylation sites and their activities. Through an analysis of Motif patterns of phosphorylated proteins, Mfuzz clustering of phosphorylation sites, and kinase regulatory networks, we explored the functional modules of phosphorylated protein interaction networks in response to stress within pine. In general, our study emphasized the significant role of kinase METK2, PTI12, PGK, as well as At3g59480 for the first time. The identification of these phosphorylated proteins was additionally confirmed through parallel reaction monitoring technology. Furthermore, genes associated with differentially expressed proteins were validated through real-time quantitative polymerase chain reaction detection. This investigation aids in understanding the mechanisms behind resistance formation and regulation of caterpillar feeding incentives in pine. Breeding more resistant pine varieties may benefit from a fuller understanding of these defense strategies in the future.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"35 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141340465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As one of the efforts to combat climate change and the biodiversity crisis, an interest in nature-based solutions (NbS) has been growing. Although there have been diverse discussions on NbS, it is still insufficient to find a quantitative evaluation of the global research trends in which field and to what extent NbS has been studied. In this regard, this study employed latent Dirichlet allocation (LDA) topic modeling and keyword analysis to quantitatively evaluate the research trends of NbS. Among research papers on NbS searched on SCOPUS, 2,625 studies (2009–2022) were analyzed by LDA. As a result, NbS-related articles were classified into seven topics (i.e., Urban governance, Urban green infrastructure, Wastewater treatment, Coastal protection, Flood mitigation, Carbon sequestration, and Sustainable agriculture). Of these, the urban and water-related topics accounted for the largest proportion. In contrast, the carbon sequestration and sustainable agriculture topics accounted for a smaller proportion, but the proportion significantly increased over time. As a result of keyword analysis, the frequency of urban and water keywords remained high. In contrast, the frequency of climate change and carbon keywords was low and has recently increased. In the keyword network, the co-occurrence frequency and connection of urban and water keywords were initially high. Still, over time, keywords related to climate change and carbon increased similarly to those related to urban and water. The findings of this study imply that NbS has mainly focused on urban and water-related researches. Still, climate change and carbon-related researches may also be actively handled in relation to NbS in the near future.
{"title":"Research trends of nature-based solutions: from urban to climate change","authors":"Hyunyoung Yang, Jeongyeon Chae, Cholho Song, Eunho Choi","doi":"10.3389/ffgc.2024.1351189","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1351189","url":null,"abstract":"As one of the efforts to combat climate change and the biodiversity crisis, an interest in nature-based solutions (NbS) has been growing. Although there have been diverse discussions on NbS, it is still insufficient to find a quantitative evaluation of the global research trends in which field and to what extent NbS has been studied. In this regard, this study employed latent Dirichlet allocation (LDA) topic modeling and keyword analysis to quantitatively evaluate the research trends of NbS. Among research papers on NbS searched on SCOPUS, 2,625 studies (2009–2022) were analyzed by LDA. As a result, NbS-related articles were classified into seven topics (i.e., Urban governance, Urban green infrastructure, Wastewater treatment, Coastal protection, Flood mitigation, Carbon sequestration, and Sustainable agriculture). Of these, the urban and water-related topics accounted for the largest proportion. In contrast, the carbon sequestration and sustainable agriculture topics accounted for a smaller proportion, but the proportion significantly increased over time. As a result of keyword analysis, the frequency of urban and water keywords remained high. In contrast, the frequency of climate change and carbon keywords was low and has recently increased. In the keyword network, the co-occurrence frequency and connection of urban and water keywords were initially high. Still, over time, keywords related to climate change and carbon increased similarly to those related to urban and water. The findings of this study imply that NbS has mainly focused on urban and water-related researches. Still, climate change and carbon-related researches may also be actively handled in relation to NbS in the near future.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"26 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-12DOI: 10.3389/ffgc.2024.1296100
K. V. Suresh Babu, Swati Singh, G. Kabdulova, Kabzhanova Gulnara, G. Baktybekov
Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.
哈萨克斯坦经常发生森林火灾或野火，尤其是在 6 月至 9 月，对森林资源造成破坏。绘制烧毁面积图对于火灾管理者在火灾发生后采取适当的缓解措施和开展恢复活动非常重要。本研究利用 KazEOSat-1 高分辨率卫星数据集绘制哈萨克斯坦各地区的烧毁面积图。KazEOSat-1 卫星位于太阳同步轨道，由四个波段组成，即蓝光、绿光、红光和近红外多光谱波段，空间分辨率为 4 米，全色数据的空间分辨率为 1 米。本研究根据 KazEOSat-1 号卫星数据集的近红外、蓝、红和绿四个光谱波段，研究了绘制烧毁区域图的三个光谱指数，即 AVI、BAI 和 GEMI。每个波段的 DN 值用于确定 TOA 反射率，然后以此为基础得出上述光谱指数。光谱指数、AVI、BAI 和 GEMI 的结果根据判别指数（M）进行比较，以量化每个指数在从 KazEOSat-1 数据集得出的烧毁面积基础上的有效性。光谱指数 BAI 的 M 值高于其他指数；因此，与 AVI 和 GEMI 相比，BAI 指数提取烧毁面积的能力更强。精确度根据发生在燃烧区和未燃烧区的森林火灾事故数量进行计算，结果表明 BAI 的精确度最高，而 AVI 的精确度最低。因此，利用 KazEOSat-1 卫星数据集提取烧毁面积的能力最强的是 BAI。由于 KazEOSat 的重访周期为 3 天，这项研究将有助于绘制哈萨克斯坦的烧毁面积和火灾进程图。
{"title":"Geospatial assessment of forest fire impacts utilizing high-resolution KazEOSat-1 satellite data","authors":"K. V. Suresh Babu, Swati Singh, G. Kabdulova, Kabzhanova Gulnara, G. Baktybekov","doi":"10.3389/ffgc.2024.1296100","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1296100","url":null,"abstract":"Forest fires or wildfires frequently occur in Kazakhstan, especially in the months from June to September, damaging the forest resources. Burnt area mapping is important for fire managers to take appropriate mitigation steps and carry out restoration activities after the fire event. In this study, KazEOSat-1 high-resolution satellite datasets are used to map the burnt area in the regions of Kazakhstan. KazEOSat-1 satellite is in a Sun-synchronous orbit, consisting of four bands, namely blue, green, red, and NIR multispectral bands, in 4 m spatial resolution, while panchromatic data are in 1 m spatial resolution. This study examined three spectral indices, namely AVI, BAI, and GEMI, for mapping the burnt area based on the four spectral bands NIR, blue, red, and green of the KazEOSat-1 satellite datasets. The DN values for each band are used to determine TOA reflectance, which is then used as a basis for deriving the aforementioned spectral indices. The results of spectral indices, AVI, BAI, and GEMI are compared based on a discriminative index (M) for quantifying the effectiveness of each index based on burned area derived from KazEOSat-1 datasets. The spectral index BAI shows higher M values than other indices; therefore, the index BAI has the higher capability to extract the burned area as compared with AVI and GEMI. Accuracy was calculated based on the number of forest fire incidents that fell in burned and unburned areas, and the results indicate that BAI shows the highest accuracy, whereas AVI shows the lowest accuracy among them. Therefore, the BAI has the highest ability for extracting the burned area using the KazEOSat-1 satellite datasets. As the revisit time period of KazEOSat is 3 days, this study will be useful to map the burnt area and fire progression in Kazakhstan.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"122 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141351706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3389/ffgc.2024.1332708
S. Ribeiro, Adelaide Cerveira, Paula Soares, Nuno Almeida Ribeiro, C. Camilo-Alves, Teresa Fidalgo Fonseca
The sustainability of forest species is directly related to the success of stand regeneration. Assuring success is particularly critical in stands where perpetuity relies on natural regeneration, as is often the case with cork oak forests. However, 59% of the stand in Portugal have no natural regeneration, and climate change could further worsen the sustainability of the system. The study summarizes the factors that affect the natural regeneration of cork oak (Quercus suber L.) based on current knowledge and presents a case study on a forest in Northeast Portugal, where the natural regeneration of Quercus suber under the effect of climate change have been monitored and analyzed. The present work focuses on the effect of stand density, i.e., tree cover, on the production of acorns, the establishment and survival of seedlings, and the impact of the summer season on seedling mortality. The monitoring was carried out in February, June, September 2022, and January 2023 in two stands with distinct stand canopy cover, when the region was under extreme drought. Data analysis was performed using the analysis of variance for repeated measures and the Mann–Whitney-Wilcoxon test. The study showed that cork oak regeneration is influenced by stand density, which promoted the establishment success and survival of natural regeneration in a period of reduced precipitation, despite possible competition for water resources. The mean number of seedlings differed significantly between the two stands. However, there were no significant differences in the mean number of seedlings throughout the field measurements. Additionally, the percentage of dead seedlings was low even after the summer season (9.5% of the total seedlings) in the denser stand. These results indicate that high canopy cover can have a protective effect for extreme climatic events and should be considered in forestry management to promote regeneration of the cork oak forests.
{"title":"Natural regeneration of cork oak forests under climate change: a case study in Portugal","authors":"S. Ribeiro, Adelaide Cerveira, Paula Soares, Nuno Almeida Ribeiro, C. Camilo-Alves, Teresa Fidalgo Fonseca","doi":"10.3389/ffgc.2024.1332708","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1332708","url":null,"abstract":"The sustainability of forest species is directly related to the success of stand regeneration. Assuring success is particularly critical in stands where perpetuity relies on natural regeneration, as is often the case with cork oak forests. However, 59% of the stand in Portugal have no natural regeneration, and climate change could further worsen the sustainability of the system. The study summarizes the factors that affect the natural regeneration of cork oak (Quercus suber L.) based on current knowledge and presents a case study on a forest in Northeast Portugal, where the natural regeneration of Quercus suber under the effect of climate change have been monitored and analyzed. The present work focuses on the effect of stand density, i.e., tree cover, on the production of acorns, the establishment and survival of seedlings, and the impact of the summer season on seedling mortality. The monitoring was carried out in February, June, September 2022, and January 2023 in two stands with distinct stand canopy cover, when the region was under extreme drought. Data analysis was performed using the analysis of variance for repeated measures and the Mann–Whitney-Wilcoxon test. The study showed that cork oak regeneration is influenced by stand density, which promoted the establishment success and survival of natural regeneration in a period of reduced precipitation, despite possible competition for water resources. The mean number of seedlings differed significantly between the two stands. However, there were no significant differences in the mean number of seedlings throughout the field measurements. Additionally, the percentage of dead seedlings was low even after the summer season (9.5% of the total seedlings) in the denser stand. These results indicate that high canopy cover can have a protective effect for extreme climatic events and should be considered in forestry management to promote regeneration of the cork oak forests.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"29 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141355695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-11DOI: 10.3389/ffgc.2024.1385687
Aurora Bozzini, Stefano Brugnaro, G. Morgante, Giacomo Santoiemma, Luca Deganutti, V. Finozzi, Andrea Battisti, Massimo Faccoli
European forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is the European Spruce Bark Beetle (Ips typographus L.). Effective management of I. typographus outbreaks necessitates the timely detection of recently attacked spruce trees, which is challenging given the difficulty in spotting symptoms on infested tree crowns. Bark beetle population density is one of many factors that can affect infestation rate and symptoms development. This study compares the appearance of early symptoms in endemic and epidemic bark beetle populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery.In spring of 2022, host colonization by bark beetles was induced on groups of spruce trees growing in 10 sites in the Southern Alps, characterized by different population density (5 epidemic and 5 endemic). A multispectral sensor mounted on a drone captured images once every 2 weeks, from May to August 2022. The analyses of a set of vegetational indices allowed the actual infested trees’ reflectance features and symptoms appearance to be observed at each site, comparing them with those of unattacked trees.Results show that high bark beetles population density triggers a more rapid and intense response regarding the emergence of symptoms. Infested trees were detected at least 1 month before symptoms became evident to the human eye (red phase) in epidemic sites, while this was not possible in endemic sites. Key performing vegetation indices included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Vegetation Index, with a correction factor of 0.44), and NDRE (Normalized Difference Red Edge index).This early-detection approach could allow automatic diagnosis of bark beetles’ infestations and provide useful guidance for the management of areas suffering pest outbreaks.
{"title":"Drone-based early detection of bark beetle infested spruce trees differs in endemic and epidemic populations","authors":"Aurora Bozzini, Stefano Brugnaro, G. Morgante, Giacomo Santoiemma, Luca Deganutti, V. Finozzi, Andrea Battisti, Massimo Faccoli","doi":"10.3389/ffgc.2024.1385687","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1385687","url":null,"abstract":"European forests face increasing threats due to climate change-induced stressors, which create the perfect conditions for bark beetle outbreaks. The most important spruce forest pest in Europe is the European Spruce Bark Beetle (Ips typographus L.). Effective management of I. typographus outbreaks necessitates the timely detection of recently attacked spruce trees, which is challenging given the difficulty in spotting symptoms on infested tree crowns. Bark beetle population density is one of many factors that can affect infestation rate and symptoms development. This study compares the appearance of early symptoms in endemic and epidemic bark beetle populations using highresolution Unmanned Aerial Vehicles (UAV) multispectral imagery.In spring of 2022, host colonization by bark beetles was induced on groups of spruce trees growing in 10 sites in the Southern Alps, characterized by different population density (5 epidemic and 5 endemic). A multispectral sensor mounted on a drone captured images once every 2 weeks, from May to August 2022. The analyses of a set of vegetational indices allowed the actual infested trees’ reflectance features and symptoms appearance to be observed at each site, comparing them with those of unattacked trees.Results show that high bark beetles population density triggers a more rapid and intense response regarding the emergence of symptoms. Infested trees were detected at least 1 month before symptoms became evident to the human eye (red phase) in epidemic sites, while this was not possible in endemic sites. Key performing vegetation indices included NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjust Vegetation Index, with a correction factor of 0.44), and NDRE (Normalized Difference Red Edge index).This early-detection approach could allow automatic diagnosis of bark beetles’ infestations and provide useful guidance for the management of areas suffering pest outbreaks.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"54 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141358241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rhododendron delavayi Franch. predominantly thrives in broad-leaved evergreen forests and bushes at altitudes of 1,200–3,200 meters. It favors a cool, moist climate and thrives in acidic soils. Due to its high ornamental, medicinal, and scientific values, understanding its ecological needs and optimal cultivation ranges is crucial. This study analyzing the key environmental factors and their thresholds affecting its distribution and predicts the future potential habitats of R. delavayi using existing distribution data alongside current and projected climate data. The results indicate that the primary environmental influencers are soil pH (4.9–5.4), precipitation in the driest month (10–20 mm), and altitude, contributing 41.8, 24.1, and 18.3%, respectively. It also shows a declining trend in suitable habitats: from 27.75 × 104 km2 under current conditions, to 3.69 × 104 km2 by the 2050s, and further to 2.65 × 104 km2 by the 2070s.
{"title":"Prediction of potential habitat areas of Rhododendron delavayi in China based on maximum entropy model MaxEnt","authors":"Jian Dong, Tian Bai, Zhao-sheng Gao, Song-wei Yang, Jing-li Zhang, Ya-wen Wu","doi":"10.3389/ffgc.2024.1360823","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1360823","url":null,"abstract":"Rhododendron delavayi Franch. predominantly thrives in broad-leaved evergreen forests and bushes at altitudes of 1,200–3,200 meters. It favors a cool, moist climate and thrives in acidic soils. Due to its high ornamental, medicinal, and scientific values, understanding its ecological needs and optimal cultivation ranges is crucial. This study analyzing the key environmental factors and their thresholds affecting its distribution and predicts the future potential habitats of R. delavayi using existing distribution data alongside current and projected climate data. The results indicate that the primary environmental influencers are soil pH (4.9–5.4), precipitation in the driest month (10–20 mm), and altitude, contributing 41.8, 24.1, and 18.3%, respectively. It also shows a declining trend in suitable habitats: from 27.75 × 104 km2 under current conditions, to 3.69 × 104 km2 by the 2050s, and further to 2.65 × 104 km2 by the 2070s.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"20 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141360432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.3389/ffgc.2024.1409985
Lina Ke, Yao Lu, Qin Tan, Yu Zhao, Quanming Wang
Mapping coastal wetlands' spatial distribution and spatiotemporal dynamics is crucial for ecological conservation and restoration efforts. However, the high hydrological dynamics and steep environmental gradients pose challenges for precise mapping. This study developed a new method for mapping coastal wetlands using time-series remote sensing images and a deep learning model. Precise mapping and change analysis were conducted in the Liaohe Estuary Reserve in 2017 and 2022. The results demonstrated the superiority of Temporal Optimize Features (TOFs) in feature importance and classification accuracy. Incorporating TOFs into the ResNet model effectively combined temporal and spatial information, enhancing coastal wetland mapping accuracy. Comparative analysis revealed ecological restoration trends, emphasizing artificial restoration's predominant role in salt marsh vegetation rehabilitation. These findings provide essential technical support for coastal wetland ecosystem monitoring and contribute to the study of sustainability under global climate change.
{"title":"Precise mapping of coastal wetlands using time-series remote sensing images and deep learning model","authors":"Lina Ke, Yao Lu, Qin Tan, Yu Zhao, Quanming Wang","doi":"10.3389/ffgc.2024.1409985","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1409985","url":null,"abstract":"Mapping coastal wetlands' spatial distribution and spatiotemporal dynamics is crucial for ecological conservation and restoration efforts. However, the high hydrological dynamics and steep environmental gradients pose challenges for precise mapping. This study developed a new method for mapping coastal wetlands using time-series remote sensing images and a deep learning model. Precise mapping and change analysis were conducted in the Liaohe Estuary Reserve in 2017 and 2022. The results demonstrated the superiority of Temporal Optimize Features (TOFs) in feature importance and classification accuracy. Incorporating TOFs into the ResNet model effectively combined temporal and spatial information, enhancing coastal wetland mapping accuracy. Comparative analysis revealed ecological restoration trends, emphasizing artificial restoration's predominant role in salt marsh vegetation rehabilitation. These findings provide essential technical support for coastal wetland ecosystem monitoring and contribute to the study of sustainability under global climate change.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":" 854","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-06DOI: 10.3389/ffgc.2024.1382557
K. Sur, V. Verma, Pankaj Panwar, G. Shukla, S. Chakravarty, Arun Jyoti Nath
Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.
植被退化通常是一个复杂的现象，与气候变异和人类活动密切相关。保护生物多样性非常重要，因为数百万人直接或间接依赖植被（森林和作物）及其相关副产品。联合国可持续发展目标（SDGs）建议量化植被占各国土地总面积的比例。卫星图像是捕捉细微季节变化的主要准确信息来源之一，因此可以准确评估长期植被退化情况。本研究采用了多传感器、多时空和多尺度（MMM）方法来估算植被退化的脆弱性。开源云计算系统谷歌地球引擎（GEE）被用来系统监测植被退化情况，并评估不同空间分辨率的多种卫星数据的潜力。利用机器学习技术划定了热点地区，使用粗分辨率的 MODIS 归一化差异植被指数（NDVI）来识别植被的绿化和褐化效应。此外，还使用了 2000-2022 年期间气候灾害组红外降水与站点数据（CHIRPS）的降雨数据集，以发现该地区的降雨异常。此外，根据长期的植被和降雨分析，利用高分辨率数据集确定了主要植被退化地区的热点区域，以了解和验证人为或气候性质的变化原因。这项研究对于一些邦/中央政府用户部门、大学和非政府组织制定保护印度植被/森林的管理计划非常重要。
{"title":"Monitoring vegetation degradation using remote sensing and machine learning over India – a multi-sensor, multi-temporal and multi-scale approach","authors":"K. Sur, V. Verma, Pankaj Panwar, G. Shukla, S. Chakravarty, Arun Jyoti Nath","doi":"10.3389/ffgc.2024.1382557","DOIUrl":"https://doi.org/10.3389/ffgc.2024.1382557","url":null,"abstract":"Vegetation cover degradation is often a complex phenomenon, exhibiting strong correlation with climatic variation and anthropogenic actions. Conservation of biodiversity is important because millions of people are directly and indirectly dependent on vegetation (forest and crop) and its associated secondary products. United Nations Sustainable Development Goals (SDGs) propose to quantify the proportion of vegetation as a proportion of total land area of all countries. Satellite images form as one of the main sources of accurate information to capture the fine seasonal changes so that long-term vegetation degradation can be assessed accurately. In the present study, Multi-Sensor, Multi-Temporal and Multi-Scale (MMM) approach was used to estimate vulnerability of vegetation degradation. Open source Cloud computing system Google Earth Engine (GEE) was used to systematically monitor vegetation degradation and evaluate the potential of multiple satellite data with variable spatial resolutions. Hotspots were demarcated using machine learning techniques to identify the greening and the browning effect of vegetation using coarse resolution Normalized Difference Vegetation Index (NDVI) of MODIS. Rainfall datasets of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) for the period 2000–2022 were also used to find rainfall anomaly in the region. Furthermore, hotspot areas were identified using high-resolution datasets in major vegetation degradation areas based on long-term vegetation and rainfall analysis to understand and verify the cause of change whether anthropogenic or climatic in nature. This study is important for several State/Central Government user departments, Universities, and NGOs to lay out managerial plans for the protection of vegetation/forests in India.","PeriodicalId":507254,"journal":{"name":"Frontiers in Forests and Global Change","volume":"27 34","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141379926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：