Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. ...Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. In recent years, researchers’ research on government credibility has mostly focused on exploring theories and mechanisms, with little empirical research on this topic. This article intends to apply variable selection models in the field of social statistics to the issue of government credibility, in order to achieve empirical research on government credibility and explore its core influencing factors from a statistical perspective. Specifically, this article intends to use four regression-analysis-based methods and three random-forest-based methods to study the influencing factors of government credibility in various provinces in China, and compare the performance of these seven variable selection methods in different dimensions. The research results show that there are certain differences in simplicity, accuracy, and variable importance ranking among different variable selection methods, which present different importance in the study of government credibility issues. This study provides a methodological reference for variable selection models in the field of social science research, and also offers a multidimensional comparative perspective for analyzing the influencing factors of government credibility.展开更多
The world is losing its forest. The study described the multi-sectoral initiatives done to protect the Dupinga watershed. By using Community Based Forest Management (CBFM) as theoretical framework and analytic descrip...The world is losing its forest. The study described the multi-sectoral initiatives done to protect the Dupinga watershed. By using Community Based Forest Management (CBFM) as theoretical framework and analytic descriptive method of research, issues and concerns related to watershed protection are discussed. The study argued that the multi-sectoral collaboration of the Local Government Unit of Gabaldon, the Department of Environment and Natural Resources (DENR), Peoples Organizations, Non Government Organizations (NGO) and Community Organization is a CBFM in progress. Alternative source of income and community tourism may strengthen and sustain the multi-stake-holders collaboration existing in the area while capacity building program for community participation and forest management may provide insights for a sustainable watershed protection and management.展开更多
The focus of the study is to measure the level of awareness of Indigenous People on Climate Variation. It inquired into their observations and organizing strategy to cope with the early impacts of climate change on th...The focus of the study is to measure the level of awareness of Indigenous People on Climate Variation. It inquired into their observations and organizing strategy to cope with the early impacts of climate change on their socio-economic and cultural beliefs. The organization’s adaptation and mitigation practices to protect the environment are also discussed using as basis of analysis the multi-stakeholders framework of forest protection. The study documented and recognized the Indigenous Peoples contributions to the preservation and protection of forest resources in Caraballo mountain and a shift in paradigm to Indigenous People’s centered forest resources management is recommended.展开更多
Finland’s national aim for annual consumption of forest chips is 25 terawatt hours (TWh) (equivalent to 13.5 million solid cubic metres) in combined heat and power (CHP) production and heat production in 2020. On ave...Finland’s national aim for annual consumption of forest chips is 25 terawatt hours (TWh) (equivalent to 13.5 million solid cubic metres) in combined heat and power (CHP) production and heat production in 2020. On average, the techno-economic potential of forest chips enables reaching the target at the national level. However, there is a geographical mismatch between the supply and demand regions. In this study, the regional balance of potential and demand from 2012 until 2020 was assessed using GIS-based methods. Economical, technical and ecological constraints were taken into account when different scenarios for municipality-level potentials were calculated. The forest chips’ consumption scenarios for plant-level were determined statistically (2012) or predicted (2020) by assuming that the total consumption of forest chips will reach the 13.5 Mm<sup>3</sup>. With help of procurement model, the use of different forest energy fuel types (stumps, logging residues and small-sized thinning wood) was spread to the procurement ring with the help of GIS coding. The forest chips’ regional balance map was made by subtracting the use of heat and combined heat and power plants’ (CHP) forest chips’ consumption from the municipality level potential data. The GIS-based method for balance calculation requires a significant amount of computer power but works well for local, municipality, regional and national-level balance calculations. The study showed that there are enough forest chips to supply the current and future demand when all forest energy assortments are used efficiently and in a sustainable manner. However, the results indicate that already at the present rate of forest chip consumption, in some areas there will not be any extra potential left. When consumption increases, the zero-potential area, in particular on the coast, expands. The highest free potential can be found in eastern and northern areas of Finland while the western and southern areas lack free potential.展开更多
Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classificat...Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.展开更多
Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remot...Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.展开更多
为深入了解国内外林下经济研究现状和趋势,以2002-2022年Web of Science核心数据库为数据来源,结合Citespace和VOSviewer等软件对国内外林下经济相关文献研究数量、研究机构、研究主题及关键词进行统计分析。结果表明:林下经济累积发文...为深入了解国内外林下经济研究现状和趋势,以2002-2022年Web of Science核心数据库为数据来源,结合Citespace和VOSviewer等软件对国内外林下经济相关文献研究数量、研究机构、研究主题及关键词进行统计分析。结果表明:林下经济累积发文量和累积被引频次呈指数增长的趋势;该领域发文量学者主要分布在美国、巴西、印度、德国、中国等地;世界农林研究中心、法国农业国际合作研究发展中心、中国科学院等机构在该领域发文量最大;林下经济学是一门主要以林学、农学、环境科学、生态学、植物学、土壤学等学科交叉而成的新型学科。此外,基于关键文献和关键词分析,低速发展期(2002-2007年)考虑林木生长、作物产量的影响研究,快速增长期(2008-2017年)考虑间作模式、优良品种选育、土壤理化性质及养分、分析方法、种间关系等机制研究,急速增长期(2018-2022年)作物化学成分(及内含物)和营养价值、植物根际和土壤微生物群落活性的响应机理、遥感技术的运用以及稳定同位素的溯源技术是主要研究趋势。展开更多
文摘Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. In recent years, researchers’ research on government credibility has mostly focused on exploring theories and mechanisms, with little empirical research on this topic. This article intends to apply variable selection models in the field of social statistics to the issue of government credibility, in order to achieve empirical research on government credibility and explore its core influencing factors from a statistical perspective. Specifically, this article intends to use four regression-analysis-based methods and three random-forest-based methods to study the influencing factors of government credibility in various provinces in China, and compare the performance of these seven variable selection methods in different dimensions. The research results show that there are certain differences in simplicity, accuracy, and variable importance ranking among different variable selection methods, which present different importance in the study of government credibility issues. This study provides a methodological reference for variable selection models in the field of social science research, and also offers a multidimensional comparative perspective for analyzing the influencing factors of government credibility.
文摘The world is losing its forest. The study described the multi-sectoral initiatives done to protect the Dupinga watershed. By using Community Based Forest Management (CBFM) as theoretical framework and analytic descriptive method of research, issues and concerns related to watershed protection are discussed. The study argued that the multi-sectoral collaboration of the Local Government Unit of Gabaldon, the Department of Environment and Natural Resources (DENR), Peoples Organizations, Non Government Organizations (NGO) and Community Organization is a CBFM in progress. Alternative source of income and community tourism may strengthen and sustain the multi-stake-holders collaboration existing in the area while capacity building program for community participation and forest management may provide insights for a sustainable watershed protection and management.
文摘The focus of the study is to measure the level of awareness of Indigenous People on Climate Variation. It inquired into their observations and organizing strategy to cope with the early impacts of climate change on their socio-economic and cultural beliefs. The organization’s adaptation and mitigation practices to protect the environment are also discussed using as basis of analysis the multi-stakeholders framework of forest protection. The study documented and recognized the Indigenous Peoples contributions to the preservation and protection of forest resources in Caraballo mountain and a shift in paradigm to Indigenous People’s centered forest resources management is recommended.
文摘Finland’s national aim for annual consumption of forest chips is 25 terawatt hours (TWh) (equivalent to 13.5 million solid cubic metres) in combined heat and power (CHP) production and heat production in 2020. On average, the techno-economic potential of forest chips enables reaching the target at the national level. However, there is a geographical mismatch between the supply and demand regions. In this study, the regional balance of potential and demand from 2012 until 2020 was assessed using GIS-based methods. Economical, technical and ecological constraints were taken into account when different scenarios for municipality-level potentials were calculated. The forest chips’ consumption scenarios for plant-level were determined statistically (2012) or predicted (2020) by assuming that the total consumption of forest chips will reach the 13.5 Mm<sup>3</sup>. With help of procurement model, the use of different forest energy fuel types (stumps, logging residues and small-sized thinning wood) was spread to the procurement ring with the help of GIS coding. The forest chips’ regional balance map was made by subtracting the use of heat and combined heat and power plants’ (CHP) forest chips’ consumption from the municipality level potential data. The GIS-based method for balance calculation requires a significant amount of computer power but works well for local, municipality, regional and national-level balance calculations. The study showed that there are enough forest chips to supply the current and future demand when all forest energy assortments are used efficiently and in a sustainable manner. However, the results indicate that already at the present rate of forest chip consumption, in some areas there will not be any extra potential left. When consumption increases, the zero-potential area, in particular on the coast, expands. The highest free potential can be found in eastern and northern areas of Finland while the western and southern areas lack free potential.
基金financially supported by grant from National Natural Science Foundation of China(No.31300533)
文摘Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.
文摘Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.
文摘为深入了解国内外林下经济研究现状和趋势,以2002-2022年Web of Science核心数据库为数据来源,结合Citespace和VOSviewer等软件对国内外林下经济相关文献研究数量、研究机构、研究主题及关键词进行统计分析。结果表明:林下经济累积发文量和累积被引频次呈指数增长的趋势;该领域发文量学者主要分布在美国、巴西、印度、德国、中国等地;世界农林研究中心、法国农业国际合作研究发展中心、中国科学院等机构在该领域发文量最大;林下经济学是一门主要以林学、农学、环境科学、生态学、植物学、土壤学等学科交叉而成的新型学科。此外,基于关键文献和关键词分析,低速发展期(2002-2007年)考虑林木生长、作物产量的影响研究,快速增长期(2008-2017年)考虑间作模式、优良品种选育、土壤理化性质及养分、分析方法、种间关系等机制研究,急速增长期(2018-2022年)作物化学成分(及内含物)和营养价值、植物根际和土壤微生物群落活性的响应机理、遥感技术的运用以及稳定同位素的溯源技术是主要研究趋势。
