Pub Date : 2024-08-31DOI: 10.1016/j.ecolind.2024.112557
Yang Zhao, Rui Qi, Bo Li, Ting Liu, Jia-hao Cao, Yi Li
The niche of plant populations is affected by the environment, species characteristics and anthropogenic disturbance. , as a major constructive species in the northeastern Qinghai-Tibetan Plateau, had been severely damaged. Although the national project for the protection of natural forests has promoted the recovery of its community, its structure, survival status, development trend, and the factors affecting it are still unclear. We selected communities in Zecha, Dayugou and Yeliguan forest zones at different altitudes and disturbance levels. We analyzed niche characteristics of the woody plants and the relationship between niche and altitude, and disturbance. has absolute advantages as a constructive species, and its population dominance and niche width in the tree layer show a decreasing trend with decreasing altitude, there is niche overlap between all species pairs. In the shrub layer, the dominant species are mostly and besides seedlings, the proportion of species pairs with niche overlap is YLG>ZC>DYG, and all appeared niche divergence and convergence species pairs. In addition, seedlings had niche overlap with most woody species, and this overlap index was the highest. The mean values of the niche overlap index between species in the tree and shrub layers were all YLG>DYG>ZC. The niche overlap index between species in the tree layer was greater than that in the shrub layer in the same forest zones, indicating that the tree layer is more stable than the shrub layer, providing evidence that niche overlap maintains community stability. Regression analyses showed that minimum temperature was the main factor affecting dominance, niche, niche overlap and shrub layer species richness of the population. Disturbance did not significantly affect dominance and niche of seedling populations, but promoted niche differentiation of shrub layer species. We conclude that the community is mainly influenced by altitude and anthropogenic disturbance. Altitude-induced climatic variation fundamentally determines the distinct community composition and population niche. Anthropogenic disturbance has altered habitat heterogeneity and enriched community structure. Furthermore, the populations show a trend towards expansion. Understanding the structure and niche characteristics of communities on different environmental gradients enriches our ideas for implementing vegetation restoration and sustainable forest management in subalpine zones in the context of climate change, and is conducive to improving the conservation capacity of this population or community type.
{"title":"Niche of woody plant populations in Picea purpurea community in the upper Taohe River","authors":"Yang Zhao, Rui Qi, Bo Li, Ting Liu, Jia-hao Cao, Yi Li","doi":"10.1016/j.ecolind.2024.112557","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112557","url":null,"abstract":"The niche of plant populations is affected by the environment, species characteristics and anthropogenic disturbance. , as a major constructive species in the northeastern Qinghai-Tibetan Plateau, had been severely damaged. Although the national project for the protection of natural forests has promoted the recovery of its community, its structure, survival status, development trend, and the factors affecting it are still unclear. We selected communities in Zecha, Dayugou and Yeliguan forest zones at different altitudes and disturbance levels. We analyzed niche characteristics of the woody plants and the relationship between niche and altitude, and disturbance. has absolute advantages as a constructive species, and its population dominance and niche width in the tree layer show a decreasing trend with decreasing altitude, there is niche overlap between all species pairs. In the shrub layer, the dominant species are mostly and besides seedlings, the proportion of species pairs with niche overlap is YLG>ZC>DYG, and all appeared niche divergence and convergence species pairs. In addition, seedlings had niche overlap with most woody species, and this overlap index was the highest. The mean values of the niche overlap index between species in the tree and shrub layers were all YLG>DYG>ZC. The niche overlap index between species in the tree layer was greater than that in the shrub layer in the same forest zones, indicating that the tree layer is more stable than the shrub layer, providing evidence that niche overlap maintains community stability. Regression analyses showed that minimum temperature was the main factor affecting dominance, niche, niche overlap and shrub layer species richness of the population. Disturbance did not significantly affect dominance and niche of seedling populations, but promoted niche differentiation of shrub layer species. We conclude that the community is mainly influenced by altitude and anthropogenic disturbance. Altitude-induced climatic variation fundamentally determines the distinct community composition and population niche. Anthropogenic disturbance has altered habitat heterogeneity and enriched community structure. Furthermore, the populations show a trend towards expansion. Understanding the structure and niche characteristics of communities on different environmental gradients enriches our ideas for implementing vegetation restoration and sustainable forest management in subalpine zones in the context of climate change, and is conducive to improving the conservation capacity of this population or community type.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1016/j.ecolind.2024.112555
Liuying Ye, Shuhe Zhao, Hong Yang, Xiaowei Chuai, Liang Zhai
As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.
{"title":"Urban land use simulation and carbon-related driving factors analysis based on RF-CA in Shanghai, China","authors":"Liuying Ye, Shuhe Zhao, Hong Yang, Xiaowei Chuai, Liang Zhai","doi":"10.1016/j.ecolind.2024.112555","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112555","url":null,"abstract":"As global climate change intensifies, climate protection is important for the sustainable development of human society. In the process of urbanization and industrialization, carbon dioxide emissions are an important factor contributing to global warming. Therefore, modelling projections of future urban land use under low-carbon scenarios are essential for sustainability policy development. Few studies have focused on the impact of carbon emissions on urban land use change in Shanghai, as current research has primarily concentrated on developing methods for urban land use modelling. This paper utilizes a cellular automata (CA) simulation model based on the Random Forest (RF) algorithm to select various spatial variables of carbon emissions as the driving factors that affect urban land use changes. These variables include traffic location factors, economic development factors, electricity consumption, and population density. In this study, remote sensing imagery of urban nighttime lighting is also used to construct a simulation model of land use types in Shanghai. The model is then used to analyze the contribution of carbon emission constraints to urban land use changes. Actual historical land use data from 2013 and 2019 are used for validation, and the prediction model is used to predict land use outcomes under different low-carbon scenarios in 2025. The model is validated by simulating multiple intra-city land use maps for 2019 (kappa = 0.88, OA=92.71 %). The method of out-of-bag error from the random forest is used to evaluate the significance of carbon emission constraints. Using the validated model, the constraints in the CA model are changed to predict the land use simulation results of Shanghai in 2025 under different low-carbon scenarios. In terms of significance, factors such as distance to power plants, distance to major roads, real GDP, and population density can all have a significant impact on changes in urban land use. By selecting the low-carbon scenario with the most appropriate thresholds for each driver, it is possible to obtain the land use simulation results of Shanghai in 2025 under the optimal low-carbon scenario, while ensuring the high accuracy of the RF-CA model and simultaneously reducing the impact of factors on the city’s overall carbon emissions. This paper provides a scientific base for urban planners and scholars to thoughtfully design urban land use while cutting down on carbon emissions. Furthermore, it can aid government agencies in establishing associated planning approaches.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-31DOI: 10.1016/j.ecolind.2024.112551
Yang Chen, Ruizhi Zhang, Sajad Asadi Alekouei, Majid Amani-Beni
Investigating the nonlinear impacts of urban landscape and climatic parameters on urban temperatures, a critical issue within urban climatology. Chengdu, characterized by its hot, rainy summers and rapid urban development, serves as an ideal model to illustrate these dynamics. Our investigation utilizes advanced analytical methods such as Random Forests (RF), SHapley additive explanation (SHAP), and Partial Dependence Plots (PDP) to analyze how landscape and climatic factors influence air temperature (AT) and land surface temperature (LST). Significant findings reveal profound thermal heterogeneity across Chengdu’s urban fabric, underscored by spatially distinct phenomena where some regions exhibit strong contrasts in temperature impacts due to varying climatic and landscape factors. The study identifies relative humidity and rainfall as key drivers of temperature variations during the summer months, reflecting Chengdu’s specific climatic idiosyncrasies. These insights are critical, as they highlight how urban planning and green infrastructure can be strategically used to mitigate adverse thermal effects. The research not only enhances understanding of the complex interplays within urban microclimates but also offers new perspectives on urban heat management. It contributes to the scientific community by providing evidence-based strategies for urban planners to counter the urban heat island effect and enhance urban resilience against climate change. This comprehensive analysis underscores the importance of incorporating multiple variables into urban climate models, lays the groundwork for more refined urban environmental policies and practices.
{"title":"Nonlinear impacts of landscape and climatological interactions on urban thermal environment during a hot and rainy summer","authors":"Yang Chen, Ruizhi Zhang, Sajad Asadi Alekouei, Majid Amani-Beni","doi":"10.1016/j.ecolind.2024.112551","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112551","url":null,"abstract":"Investigating the nonlinear impacts of urban landscape and climatic parameters on urban temperatures, a critical issue within urban climatology. Chengdu, characterized by its hot, rainy summers and rapid urban development, serves as an ideal model to illustrate these dynamics. Our investigation utilizes advanced analytical methods such as Random Forests (RF), SHapley additive explanation (SHAP), and Partial Dependence Plots (PDP) to analyze how landscape and climatic factors influence air temperature (AT) and land surface temperature (LST). Significant findings reveal profound thermal heterogeneity across Chengdu’s urban fabric, underscored by spatially distinct phenomena where some regions exhibit strong contrasts in temperature impacts due to varying climatic and landscape factors. The study identifies relative humidity and rainfall as key drivers of temperature variations during the summer months, reflecting Chengdu’s specific climatic idiosyncrasies. These insights are critical, as they highlight how urban planning and green infrastructure can be strategically used to mitigate adverse thermal effects. The research not only enhances understanding of the complex interplays within urban microclimates but also offers new perspectives on urban heat management. It contributes to the scientific community by providing evidence-based strategies for urban planners to counter the urban heat island effect and enhance urban resilience against climate change. This comprehensive analysis underscores the importance of incorporating multiple variables into urban climate models, lays the groundwork for more refined urban environmental policies and practices.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.ecolind.2024.112550
Xinrong Si, Xiaobing Chen, Zhongbo Yu, Jie Yin, Tongqing Shen, Hui Lin, Ting Nie, Wentao Hu
The release of carbon dioxide (CO) from lakes is a critical element of carbon (C) emissions from inland waters. Within the realm of climate change, the inquiries surrounding whether lakes on the Tibetan Plateau (TP) function as C sources or sinks and the magnitude of CO exchange flux from these lakes have garnered significant attentions. Nevertheless, accurately assessing the lakes’ contribution to the C budgets poses challenges due to data scarcity and methodological inaccuracies. By amalgamating data from literature reviews and field measurements for different sizes of lakes during the ice-free (IF) and ice-covered (IC) periods from 2016 to 2021, this study offers a refined estimate of the CO exchange flux and flux rate for lakes on the TP by including lakes ranging in size from 0.01 to 1 km (small lakes) in the C budgets. Findings revealed that the annual CO exchange flux of TP lakes amounted to 7.10 Tg C yr, with 6.56 Tg C yr and 0.54 Tg C yr during the IF and IC periods, respectively. Notably, small lakes contributed 0.76 Tg C yr, representing 10.65 % of the total lake CO emissions on the TP, which indicates the significant role of small lakes in estimating CO emissions from TP lakes. The CO exchange fluxes of small lakes showed significant variability during the IF period, with the origins of lake water replenishment possibly explaining this diversity, where glacial meltwater replenishment is likely a key contributing factor. In contrast, CO emissions from small lakes increased during the IC period. The view of this study is that the groundwater recharge with higher CO concentrations and the shallow nature of small lakes may be the main reasons for the increase in CO emissions from small lakes during this period. The study underscores that the contribution of small lakes to the CO budgets of TP lakes is substantial and warrants attention, particularly in elucidating the mechanisms driving CO emissions from small lakes.
湖泊释放的二氧化碳（CO）是内陆水域碳（C）排放的关键因素。在气候变化领域，围绕青藏高原（TP）上的湖泊是碳源还是碳汇以及这些湖泊的二氧化碳交换通量大小的问题引起了广泛关注。然而，由于数据稀缺和方法不准确，准确评估湖泊对碳预算的贡献面临挑战。本研究综合了 2016 年至 2021 年无冰期（IF）和覆冰期（IC）不同大小湖泊的文献综述和实地测量数据，将 0.01 至 1 千米大小的湖泊（小湖泊）纳入 C 预算，从而对大洋洲湖泊的 CO 交换通量和通量率进行了精细估算。研究结果表明，大埔湖泊的年二氧化碳交换通量为 7.10 兆吨 C/年，其中在 IF 期和 IC 期分别为 6.56 兆吨 C/年和 0.54 兆吨 C/年。值得注意的是，小湖泊贡献了 0.76 Tg C yr，占热海湖泊 CO 排放总量的 10.65%，这表明小湖泊在估算热海湖泊 CO 排放量中发挥了重要作用。小湖泊的二氧化碳交换通量在中频期间表现出显著的变化，湖泊补水的来源可能是造成这种多样性的原因，其中冰川融水补给可能是一个关键因素。相比之下，在集成电路时期，小湖的二氧化碳排放量有所增加。本研究认为，二氧化碳浓度较高的地下水补给和小湖泊的浅水特性可能是这一时期小湖泊二氧化碳排放量增加的主要原因。这项研究强调，小型湖泊对总磷量湖泊的二氧化碳预算贡献巨大，值得关注，尤其是在阐明小型湖泊二氧化碳排放的驱动机制方面。
{"title":"Carbon budgets of lakes on the Tibetan Plateau: Highlighting non-negligible carbon emissions from small lakes","authors":"Xinrong Si, Xiaobing Chen, Zhongbo Yu, Jie Yin, Tongqing Shen, Hui Lin, Ting Nie, Wentao Hu","doi":"10.1016/j.ecolind.2024.112550","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112550","url":null,"abstract":"The release of carbon dioxide (CO) from lakes is a critical element of carbon (C) emissions from inland waters. Within the realm of climate change, the inquiries surrounding whether lakes on the Tibetan Plateau (TP) function as C sources or sinks and the magnitude of CO exchange flux from these lakes have garnered significant attentions. Nevertheless, accurately assessing the lakes’ contribution to the C budgets poses challenges due to data scarcity and methodological inaccuracies. By amalgamating data from literature reviews and field measurements for different sizes of lakes during the ice-free (IF) and ice-covered (IC) periods from 2016 to 2021, this study offers a refined estimate of the CO exchange flux and flux rate for lakes on the TP by including lakes ranging in size from 0.01 to 1 km (small lakes) in the C budgets. Findings revealed that the annual CO exchange flux of TP lakes amounted to 7.10 Tg C yr, with 6.56 Tg C yr and 0.54 Tg C yr during the IF and IC periods, respectively. Notably, small lakes contributed 0.76 Tg C yr, representing 10.65 % of the total lake CO emissions on the TP, which indicates the significant role of small lakes in estimating CO emissions from TP lakes. The CO exchange fluxes of small lakes showed significant variability during the IF period, with the origins of lake water replenishment possibly explaining this diversity, where glacial meltwater replenishment is likely a key contributing factor. In contrast, CO emissions from small lakes increased during the IC period. The view of this study is that the groundwater recharge with higher CO concentrations and the shallow nature of small lakes may be the main reasons for the increase in CO emissions from small lakes during this period. The study underscores that the contribution of small lakes to the CO budgets of TP lakes is substantial and warrants attention, particularly in elucidating the mechanisms driving CO emissions from small lakes.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold , and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted (LOOCV ) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (, , , ), one canopy density variable (), three density-related variables (, and , and one vertical structural variable ( were positively correlated with AGB. The canopy height variables (, , , and ) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.
{"title":"Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data","authors":"Xiaofang Zhang, Xiaoyao Li, Ram P. Sharma, Qiaolin Ye, Huiru Zhang, Linyan Feng, Dongbo Xie, Hongchao Huang, Liyong Fu, Zefeng Zhou","doi":"10.1016/j.ecolind.2024.112517","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112517","url":null,"abstract":"Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold , and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted (LOOCV ) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (, , , ), one canopy density variable (), three density-related variables (, and , and one vertical structural variable ( were positively correlated with AGB. The canopy height variables (, , , and ) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Urbanization affects vegetation distribution by changing land cover. However, it also significantly changes urban water and heat conditions, affecting vegetation growth and development. Vegetation coverage is an effective indicator of vegetation growth. This study analyzed trends in vegetation coverage over the past 35 years, taking the Yunnan Central Urban Economic Circle as the study area. The study was based on applying the pixel dichotomy model and correlation statistical analysis on joint Landsat 5, 7, and 8 long- term remote sensing data, meteorological, and land use data. The direct and indirect effects of urbanization on vegetation coverage were also further explored by constructing an urbanization impact framework. The results revealed that: (1) The urban area during urbanization from 1986 to 2021 increased by 720.29 km. There was a continuous decline in vegetation cover in and around urban areas, which intensified with accelerating urbanization, with the effect being more pronounced in suburban areas. (2) There were consistent increasing trends in urbanization’s direct and indirect effects on vegetation over the last 35 years, with average negative and positive effects of − 0.41 and 1.59, respectively. (3) Direct effects could mainly be attributed to the expansion of impervious surfaces, whereas the main indirect effect during the late urbanization period (2011–2020) was increasing average temperature. The average temperature showed a correlation coefficient with urbanization of 0.7767, and this relationship showed seasonal heterogeneity due to the significant growth of urban vegetation in summer and winter. (4) Cities that developed faster showed better environmental planning and construction. The direct and indirect effects of urbanization on vegetation during the early and middle stages were higher in cities developing at slow and moderate rates, with this trend reversing only in the later stages of urbanization. The results of this study can increase understanding of the effect of urbanization on vegetation coverage in the Yunnan Central Urban Economic Circle. They can assist in improving urban green spaces and urban ecological resilience.
{"title":"Direct and indirect effects of urbanization on vegetation: A survey of Yunnan central urban Economic Circle, China","authors":"Jun Ma, Jinliang Wang, Suling He, Jianpeng Zhang, Lanfang Liu, Xuzheng Zhong","doi":"10.1016/j.ecolind.2024.112536","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112536","url":null,"abstract":"Urbanization affects vegetation distribution by changing land cover. However, it also significantly changes urban water and heat conditions, affecting vegetation growth and development. Vegetation coverage is an effective indicator of vegetation growth. This study analyzed trends in vegetation coverage over the past 35 years, taking the Yunnan Central Urban Economic Circle as the study area. The study was based on applying the pixel dichotomy model and correlation statistical analysis on joint Landsat 5, 7, and 8 long- term remote sensing data, meteorological, and land use data. The direct and indirect effects of urbanization on vegetation coverage were also further explored by constructing an urbanization impact framework. The results revealed that: (1) The urban area during urbanization from 1986 to 2021 increased by 720.29 km. There was a continuous decline in vegetation cover in and around urban areas, which intensified with accelerating urbanization, with the effect being more pronounced in suburban areas. (2) There were consistent increasing trends in urbanization’s direct and indirect effects on vegetation over the last 35 years, with average negative and positive effects of − 0.41 and 1.59, respectively. (3) Direct effects could mainly be attributed to the expansion of impervious surfaces, whereas the main indirect effect during the late urbanization period (2011–2020) was increasing average temperature. The average temperature showed a correlation coefficient with urbanization of 0.7767, and this relationship showed seasonal heterogeneity due to the significant growth of urban vegetation in summer and winter. (4) Cities that developed faster showed better environmental planning and construction. The direct and indirect effects of urbanization on vegetation during the early and middle stages were higher in cities developing at slow and moderate rates, with this trend reversing only in the later stages of urbanization. The results of this study can increase understanding of the effect of urbanization on vegetation coverage in the Yunnan Central Urban Economic Circle. They can assist in improving urban green spaces and urban ecological resilience.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.ecolind.2024.112559
Thanapong Chaichana, Graham Reeve, Brett Drury, Yasinee Chakrabandhu, Sutee Wangtueai, Sarat Yoowattana, Supot Sookpotharom, Nathaphon Boonnam, Charles S. Brennan, Jirapond Muangprathub
Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.
{"title":"Bespoke cultivation of seablite with digital agriculture and machine learning","authors":"Thanapong Chaichana, Graham Reeve, Brett Drury, Yasinee Chakrabandhu, Sutee Wangtueai, Sarat Yoowattana, Supot Sookpotharom, Nathaphon Boonnam, Charles S. Brennan, Jirapond Muangprathub","doi":"10.1016/j.ecolind.2024.112559","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112559","url":null,"abstract":"Climate change has driven agriculture to alter farming methods for food production. This paper presents a new concept for monitoring, acquisition, management, analysis, and synthesis of ecological data, which captures the environmental determinants and direct gradients suited to a particular requirement for specific plant cultivation and sustainable agriculture. The purpose of this study is to investigate a smart seablite cultivation system. A novel digital agricultural method was developed and applied to digitised seablite cultivation. Machine learning was used to predict the future growth conditions of plants (seablites). The study identified the illustrative maps of seablite origins, a conceptual seablite smart farming model, essential factors for growing seablite, a digital circuit for cultivating seablite, and digital data of seablite growth phases comprised the digital data. The findings indicate that: (1) An indicator of soil salinity is a quantity of sodium chloride extracted from a seablite sample indicating its origin of environmental determinants. (2) Saline soil, saline water, pH, moisture, temperature, and sunlight are essential factors for seablite development. These factors are dependent on climate change and were measured using a smart seablite cultivation system. (3) Digital circuits of seablite cultivation provide a better understanding of the relationship between the essential factors for seablite growth and seablite growth phases. (4) Deep neural networks outperformed vector machines, with 86% accuracy at predicting future growth of seablites. Therefore, this finding showed that the essential seablite development factors can be manipulated as key controllers for agriculture in response to climate change and agriculture can be planned. Basic digitisation of specific plants aids plant migration. Digital agriculture is an important practice for agroecosystems.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-30DOI: 10.1016/j.ecolind.2024.112435
Peter Holzapfel, Vanessa Bach, Florian Ansgar Jaeger, Matthias Finkbeiner
Ongoing industry initiatives like Pathfinder / Partnership for Carbon Transparency from the World Business Council for Sustainable Development, Catena-X from the automotive industry, and Together for Sustainability from the chemical industry advocate for sharing primary product carbon footprint (PCF) data along the supply chain to increase specificity. All three initiatives agree on requesting a primary data share (PDS) alongside the PCF. The PDS as an indicator of PCF specificity has not yet been addressed in scientific literature. To address this gap, this research analyzes the PDS definitions and demonstrates remaining challenges and gaps for further research by means of a hypothetical case study. While the definitions for PDS calculations with exclusively positive PCF contributions are consistent across the three initiatives, the definitions differ regarding negative PCF contributions. Further, the definitions of negative emissions do not explicitly specify the system boundaries for PDS calculations. Different system boundary choices can influence PDS results. In addition, challenges regarding the PDS calculation of multi-output processes as well as products which have been modeled using the mass balance – credit method or book and claim are identified. We provide potential solutions to these challenges which can serve as a basis for further research and specification on the PDS calculation. Primary data potentially reflects “real” emissions in a product-specific supply chain more accurately than secondary data. Thus, the PDS is a relevant indicator for the reporting company. Nevertheless, conflicts of interest can occur between achieving a low PCF and high PDS.
世界可持续发展工商理事会（World Business Council for Sustainable Development）的 "碳透明之路"（Pathfinder / Partnership for Carbon Transparency）、汽车行业的 "Catena-X "以及化工行业的 "携手实现可持续发展"（Together for Sustainability）等行业倡议都主张在供应链上共享初级产品碳足迹（PCF）数据，以提高数据的具体性。这三项倡议都同意在 PCF 的同时要求共享初级产品碳足迹数据 (PDS)。作为 PCF 特性指标的 PDS 尚未在科学文献中得到讨论。为弥补这一不足，本研究分析了 PDS 定义，并通过假设案例研究展示了有待进一步研究的挑战和差距。虽然三项计划对完全正 PCF 贡献的 PDS 计算的定义是一致的，但对负 PCF 贡献的定义却有所不同。此外，负排放的定义没有明确规定 PDS 计算的系统边界。不同的系统边界选择会影响 PDS 结果。此外，我们还发现了 PDS 计算多输出过程以及使用质量平衡-信用法或账簿和索赔法建模的产品所面临的挑战。我们为这些挑战提供了潜在的解决方案，可作为进一步研究和规范 PDS 计算的基础。原始数据可能比二手数据更准确地反映特定产品供应链中的 "真实 "排放量。因此，PDS 是报告公司的相关指标。然而，在实现低 PCF 和高 PDS 之间可能会出现利益冲突。
{"title":"The primary data share indicator for supply chain specificity in product carbon footprinting","authors":"Peter Holzapfel, Vanessa Bach, Florian Ansgar Jaeger, Matthias Finkbeiner","doi":"10.1016/j.ecolind.2024.112435","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112435","url":null,"abstract":"Ongoing industry initiatives like Pathfinder / Partnership for Carbon Transparency from the World Business Council for Sustainable Development, Catena-X from the automotive industry, and Together for Sustainability from the chemical industry advocate for sharing primary product carbon footprint (PCF) data along the supply chain to increase specificity. All three initiatives agree on requesting a primary data share (PDS) alongside the PCF. The PDS as an indicator of PCF specificity has not yet been addressed in scientific literature. To address this gap, this research analyzes the PDS definitions and demonstrates remaining challenges and gaps for further research by means of a hypothetical case study. While the definitions for PDS calculations with exclusively positive PCF contributions are consistent across the three initiatives, the definitions differ regarding negative PCF contributions. Further, the definitions of negative emissions do not explicitly specify the system boundaries for PDS calculations. Different system boundary choices can influence PDS results. In addition, challenges regarding the PDS calculation of multi-output processes as well as products which have been modeled using the mass balance – credit method or book and claim are identified. We provide potential solutions to these challenges which can serve as a basis for further research and specification on the PDS calculation. Primary data potentially reflects “real” emissions in a product-specific supply chain more accurately than secondary data. Thus, the PDS is a relevant indicator for the reporting company. Nevertheless, conflicts of interest can occur between achieving a low PCF and high PDS.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1016/j.ecolind.2024.112547
Timo Haselhoff, Moritz Schuck, Bryce T. Lawrence, André Fiebig, Susanne Moebus
The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.
{"title":"Characterizing acoustic dimensions of health-related urban greenspace","authors":"Timo Haselhoff, Moritz Schuck, Bryce T. Lawrence, André Fiebig, Susanne Moebus","doi":"10.1016/j.ecolind.2024.112547","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112547","url":null,"abstract":"The association of urban greenspace and human health and well-being is widely recognised, but the underlying mechanisms are incompletely understood. The acoustic environment (AE) is frequently proposed as a mediator between greenspace and human health. While it is commonly viewed as a negative health factor (e.g. noise pollution), there is growing evidence that it also has positive effects on human health. However, a general problem is the lack of information on the AE for greenspaces in high spatial resolution. To provide evidence-based support for research on this issue, we identify and assess acoustic properties of health-related urban greenspace by estimating the association between urban green area and selected acoustic indices.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1016/j.ecolind.2024.112553
Chuanwu Zhao, Yaozhong Pan, Peng Zhang
Frequent climate change and intense anthropogenic activity increase the risk of vegetation destruction. Remote sensing technology, known for its timely observations and wide coverage, is a crucial tool for monitoring vegetation growth. However, accurately detecting vegetation destruction events remains challenging due to their spectral diversity, particularly in complex environments. Existing spectral indices (VIs) have limitations in effectively capturing vegetation dynamics as they are only sensitive to specific physiological parameters of vegetation, such as foliage, canopy, or water content, and are prone to background interference. To address this issue, we proposed the Slope Vegetation Index (SVI) based on Sentinel-2 imagery and PROSAIL model simulation data. Five representative VIs were selected for comprehensive comparison. The results showed that, compared with other VIs, SVI had the highest sensitivity to vegetation physiological parameters, with a correlation coefficient (R) greater than 0.98. SVI performed best across all vegetation change scenes, with producer accuracy (PA), user accuracy (UA), and F1 score all exceeding 0.90. SVI proved effective in detecting various vegetation destruction events, including logging, insect infestation, landslides, and wildfires. Moreover, SVI was suitable for Landsat-8/9 imagery, achieving an F1 score of over 0.89. Overall, SVI is an effective and robust vegetation monitoring index, offering valuable insights for vegetation resource management and post-disaster ecological restoration.
{"title":"Development of a new indicator for identifying vegetation destruction events using remote sensing data","authors":"Chuanwu Zhao, Yaozhong Pan, Peng Zhang","doi":"10.1016/j.ecolind.2024.112553","DOIUrl":"https://doi.org/10.1016/j.ecolind.2024.112553","url":null,"abstract":"Frequent climate change and intense anthropogenic activity increase the risk of vegetation destruction. Remote sensing technology, known for its timely observations and wide coverage, is a crucial tool for monitoring vegetation growth. However, accurately detecting vegetation destruction events remains challenging due to their spectral diversity, particularly in complex environments. Existing spectral indices (VIs) have limitations in effectively capturing vegetation dynamics as they are only sensitive to specific physiological parameters of vegetation, such as foliage, canopy, or water content, and are prone to background interference. To address this issue, we proposed the Slope Vegetation Index (SVI) based on Sentinel-2 imagery and PROSAIL model simulation data. Five representative VIs were selected for comprehensive comparison. The results showed that, compared with other VIs, SVI had the highest sensitivity to vegetation physiological parameters, with a correlation coefficient (R) greater than 0.98. SVI performed best across all vegetation change scenes, with producer accuracy (PA), user accuracy (UA), and F1 score all exceeding 0.90. SVI proved effective in detecting various vegetation destruction events, including logging, insect infestation, landslides, and wildfires. Moreover, SVI was suitable for Landsat-8/9 imagery, achieving an F1 score of over 0.89. Overall, SVI is an effective and robust vegetation monitoring index, offering valuable insights for vegetation resource management and post-disaster ecological restoration.","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":null,"pages":null},"PeriodicalIF":6.9,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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