In this paper, the development process and characteristics of restoration ecology research in the arid regions of China were reviewed, and the research frontiers and development trends were analyzed systemically. The study on restoration ecology in arid regions has been promoted by national demands. Revegetation was the main method and approach for ecological recovery and restoration. Future study should focus on vegetation zonal distribution, soil habitat restoration, interaction of arid land ecology and hydrology, plant water relation, biological soil crust, projection based on models and modeling, and so on. The interaction and integration of geography and biology provide a new way of thinking and approach for both theoretical and practical innovations in the development of restoration ecology.
Risk analysis method for natural disasters is one of the key questions of risk research, and it directly affects the data needed for analysis, the selection of mathematical models, and the reliability of analysis results. There have been numerous published research on risk analysis methods. The applicability and reliability of these methods may determine whether the result of risk analysis is useful and may influence risk management. Among existing research, relative level of risk has been examined more often than absolute risk. However, the choice of method should be based on objective of the analysis and risk categories. This article reviews the advantages and disadvantages of various existing quantitative risk analysis methods and analyzes their applications and suitability. The following are found through this review. First, relative risk analysis methods may be divided into three categories, that is, probability analysis, expected loss analysis, and scenario simulation. Among the three types of methods, probability analysis methods have been commonly used in long time series data and macro-scale analysis. The limitation to their application is that historical disaster data may not be easily available. Expected loss analysis methods may be easily applied and are mostly used in analysis at medium -spatial scales, but their predictive power is weak. The precision of the methods based on scenario analysis is high, and they are widely applied in micro-scale analysis where basic data are sufficient. Secondly, for absolute risk, loss of life been the focus of attention and may be examined through historical data analysis, mathematical models, and scenario simulation. Those methods based on historical data analysis need to be improved. Usually the methods for analyzing the risk of loss of life from international studies cannot be directly applied in research for China. Mathematical models for analyzing the risk of loss of life still need to be tested because quantification of qualitative data require greater scrutiny. Scenario analysis methods are most promising and represent the future direction of analyzing the risk of loss of life from natural disasters. In addition, economic risk analysis based on expected losses and land use using remote sensing and GIS techniques are commonly practiced. Ecological-environmental risk analysis is relatively weak among the three types of risks due to the difficulty in quantifying ecological-environmental values. In the future, more attention should be paid to scenario analysis and the role of land use for the absolute risk. Last, similarities and differences are found among the analysis methods of both relative risks and absolute risks. No matter what types of risks are being assessed, the choice of method should be based on risk mechanism, scale of study, and application of modern technologies in order to improve the usability of risk analysis methods and the reliability of the result of analysis.
Vulnerability is a concept that evolved out of the social sciences and was introduced as a response to the purely hazard-oriented perception of disaster risk in the 1970s. Recently, the study about social vulnerability of human system and coupled human-environmental system has become increasingly more popular in the field of vulnerability research and sustainability science. The concept of social vulnerability aims at identifying and understanding which groups of people may be more sensitive and susceptible to the impacts of natural disasters and why. In this article, we discuss the progress of social vulnerability research and review the concept, characteristics, analytical frameworks, and assessment methods of social vulnerability. This article first reviews definitions of social vulnerability from different types of stresses including risk and hazard, climate change, demographic characteristics and inequality, resource exploitation, land use change, and environmental pollution. Social vulnerability is one dimension of vulnerability. Social vulnerability refers to negative impacts of social system exposed to natural or human factors due to its own sensitivity characteristics and lack of ability to cope with adverse disturbance. Second, this article introduces several analytical frameworks of social vulnerability from political economy, social-ecological system, and comprehensive perspectives. These frameworks include the Pressure and Release Model, Sustainable Livelihood Framework, Hazards-of-Place Model, Coupled Human-Environmental System Framework, BBC Conceptual Framework, and MOVE Framework. Strictly speaking, these frameworks are not specialized social vulnerability framework, and most of them were derived from vulnerability frameworks. Some researchers improved and applied them to explain elements, process, and mechanism of social vulnerability. The article compares critical rationale of different frameworks and reviews their merits and defects from three perspectives. At last, the article summarizes assessment methods, computational formulas, and evaluation index systems of social vulnerability. The methods include comprehensive index, function model, back propagation artificial neural network, decision tree, object-oriented analysis, spatial multi criteria evaluation, and GIS methods. Every method has different features and advantages and disadvantages. The choice of method should be based on research purposes. There are also many kinds of evaluation index systems and social vulnerability indexes such as the SoVI, CVI, CCSVI, SV, and so on. Most studies use population and relevant social and economic indicators to establish evaluation index systems. However, there are many questions about the validity and reliability of index systems. Generally, current studies on social vulnerability have the following problems: a unified conceptual and analytical framework has not been formed; assessment methods of social vulnerability are too simplistic; a comprehensive evaluation index system is absent; studies on mitigation and countermeasures are insufficient. In the future, social vulnerability study needs to establish a unified concept and analytical framework, expand the research contents, improve theoretical system, and promote multi-disciplinary integration. At the same time, it is necessary to improve evaluation index system and method of social vulnerability, strengthen research on social vulnerability mitigation and countermeasures, and integrate social vulnerability and social adaptation. We hope social vulnerability research can provide the scientific basis for social adaptability and sustainable development.
Urbanization is not only a worldwide phenomenon, but also the trend of historical development. As the impact of global climate change on people and the environment grows increasingly profound, the impact of urbanization on greenhouse gas emissions, especially carbon dioxide emissions has received growing academic attention. This article examines a large number of international and Chinese literature on the impact of urbanization on carbon emissions and systematically summarizes and reviews these studies from the following three aspects: the origin and development of research on the impact of urbanization on carbon emissions, research contents, and research methods. The review shows that, current studies focus on the relationship between urbanization and carbon emissions; influencing factors of urbanization on carbon emissions, which include macro- and micro-level factors, and the impact mechanism of urbanization on carbon emissions. The main research methods are quantitative. Varies methods, such as IPAT, STIRPAT, GTWR, IDA, SDA, EBA, have been applied. Although Chinese research on the topic lags behind international studies, rapid progress has been made over recent year. Overall, the impact of urbanization on carbon emissions is a long and complex process, which involves population density, technological progress, industrial structure, level of economic development, and stage and level of urbanization in various countries and regions, as well as many other factors. Although the content of the current research is becoming more detailed and the issues have been addressed more thoroughly, and the research methods have become more scientific and diverse, the study on this problem still needs to be improved. For example, research perspectives should be broadened and research methods should be refined in order to provide a scientific basis for urban development strategies and lay the foundation for the development of low-carbon cities.
Based on the Gross Domestic Product (GDP) and 1 km×1 km land use data of 1980, 1990, 2000, and 2012, 2853 counties or districts of China, the rates of change in ecological service value (ESV) and GDP were calculated. Spatial analysis were performed in ArcGIS to examine coordinated development of ecological-economic system and its spatial variation and temporal change. The results are as follows. (1) Since 1980, county level GDP growth in China has been very rapid. The rate of GDP growth in China's southeast coastal area is higher than that in the central part, followed by counties in western China. (2) The total ecological service value of China increased from 85302.15 billion yuan in 1980 to 94876.82 billion yuan in 2012, with a 11.22% increase. Spatially, county level ESV is higher in northern and western counties, and lower in eastern counties. (3) The coordination between economic development and quality of the ecological -environment (EEH) has gradually improved. At the county level, EEH in northern China is better than that in western China followed by central and eastern counties. But the sustainable development challenges of the ecological-economic system is still high. The degree of spatial correlation of EEH is relatively stable. The development trend of coordination between socioeconomic development and ecological environment tend to be positive, but ecological-economic system coordination in different counties varies significantly. The results of this study suggest that the regional economy of China's western counties should be further developed when efforts are made to protect the ecological environment of the region, while in China's central and eastern counties, greater attention should be paid to the protection of the ecological environment and ecological restoration, when the economy continues to grow. Achieving sound and fast economic development and maintaining ecological integrity is a balancing act that requires much greater effort.
Many studies have demonstrated that land use and cover change (LUCC) has played a key role in global environmental change. The contemporary land cover is a result of human land use in the history. In order to simulate the LUCC's influence in climate and ecosystem, it is important to have a historical LUCC dataset, especially high-resolution land cover dataset. However, in China, such national coverage dataset is still missing, and this has limited the national environmental change simulations. So there is an urgent need to develop an effective way to reconstruct historical cropland distribution with high-resolution grids. Considering the complexity of the natural environment in China, in this study we developed a separate reconstruction method. First, we divided China into four regions based on a qualitative analysis: the traditional cultivated region, the northeastern region, the northwestern region, and the Qinghai-Tibet Plateau. This division is mostly consistent with other recent studies except for the northwestern region, which differs slightly from common delineation. Second, in every region we examined the relationship between cropland distribution and various natural and human factors and built a reconstruction model. In the traditional cultivated region and the northeastern region, we found that elevation, slope, and population density were the main contributing factors to cropland distribution. In other regions, however, population density was the sole significant contributing factor. This model was then used to reconstruct the cropland distribution of China in 1913, 1933, 1950, 1970, 1990 and 2000 at a spatial resolution of 10 km×10 km. By comparing the reconstruction result with remote sensing data interpretation for 1990, we found that the reconstructed cropland distribution data are reliable not only at the county scale, but also at the grid scale. The comparison between the reconstructed change and the remote sensing data-derived change from 1990 to 2000 also supports this view, that is, the separate reconstruction method developed in this study is effective for capturing cropland change over time. The reconstructed dataset indicates the follows. (1) In the northeastern region, the cropland area slightly decreased at the beginning of the People's Republic of China in 1949; up to 1970, the cropland area had recovered and the modern distribution pattern formed; thereafter, the Sanjiang Plain was brought into agricultural development gradually. (2) In Xinjiang in western China, the first cropland development climax appeared in the republican period influenced by the agricultural policy; the second climax appeared between the 1950s and the 1970s, but most of the cropland was distributed in the area of the Tianshan Mountains. (3) Change in cropland distribution of the Qinghai-Tibet Plateau was not notable, but the area had increased much; the spatial distribution of cropland in the traditional cultivated region also did not change significantly, but the reclamation ratio has increased. In conclusion, cropland area in China had increased in the early 20th century and then decreased, and the inflection point was likely in the late 20th century. This trend occurred not only in cropland area, but also in reclamation ratio. However, the change varies in different regions and is more pronounced in the northeastern and northwestern regions.
With intensifying human activities global ecological and environmental problems have become increasingly pressing. Therefore the study of global change has become more prominent. Obtaining accurate land cover information in a timely fashion is critically important for such research. With the development of remote sensing science and technology and application, numerous studies have the issue of land cover classification using remote sensing image. In this study, the Beijing-Tianjin-Hebei region was selected as a study area for land cover classification using MODIS data. The 16-day MOD13Q1/EVI data in 2013, MOD09Q1 (Band1, 2) and MOD09A1(Band3-7) products in May 2013 were used as the basic data. Harmonic analysis method was employed to remove clouds and noises of the whole year's EVI, so that it can better reflect plant phenology. Then, the processed MOD13Q1/EVI data, surface albedo of MODIS/Ref1-7, modified normalized difference water index (MNDWI), normalized difference soil brightness index (NDSI), and normalized difference water index (NDWI) were integrated to construct three schemes of CART decision tree to investigate the land cover classification of the Beijing-Tianjin-Hebei region. The three band combinations are scheme1: 23 phases of EVI of 2013; scheme2: 23 phases of EVI of 2013 plus B1-B7 of MOD09; and scheme3: scheme2+MNDWI+NDSI+NDWI. The results show that the overall classification accuracy of the three schemes reaches 86.70%, 89.98%, and 89.98% respectively. Their Kappa coefficients are 84.94%, 88.66%, and 90.20% respectively. Therefore, MODIS images with various classification characteristics, combined with the decision trees can achieve higher precision land cover classification. This proves the feasibility of the proposed method for land cover classification at the regional scale.
At present, farmland abandonment is a widespread problem in many poor mountainous areas of China. This type of land use change in sensitive environment has positive impact on the ecological security of these areas, but affects negatively on grain production and food security. For national food security reasons, large amounts of basic farmland in mountainous areas are still required. Therefore the contradiction between ecological security and food security is hard to resolve. Farmland transfer, however, may offer a solution to this problem. Understanding the characteristics and determinants of farmland transfer-in provides insights for decision making on the rational distribution of basic farmland and grain for green projects, but existing research is insufficient for mountainous areas. Based on a field survey of 1015 households in the "two wings" of Chongqing using participatory rural appraisal (PRA) tool, this study analyzes the basic characteristics of farmland transfer-in at plot level. It quantitatively analyzes the influencing factors on the farmland area that farming households transfer in by using the Tobit model. The results show that: (1) As free-of-compensation transfer is common in the study area, the surveyed households are unwilling to transfer out large plots located close to residence. However, farming households who transfer in farmland tend to choose plots with relatively good quality or convenient for farming, and have a low demand for plots with relatively poor production conditions. Thus these plots are more likely to be abandoned. (2) The farming households who transfer in farmland are often small farmers with low cropping income and their plots are generally small. With regard to farmland management, the surveyed farming households tend to grow cash crops on the plots that they transfer in, while labor and fertilizer input to these plots are lower than that of own plots. (3) The Tobit model analysis suggests that farm labor force, household farming income, quantity of farming equipment, and cultivated area have significant positive impacts on the farmland area that households transfer in. On the other hand, age of household head, average plot area, and Wulong County have significant negative impacts. Other variables, such as off-farm income, quantity of household livestock, distance from market, road, and Wushan County, have no significant impact on the farmland area that farmers transfer in. (4) In order to foster land transfer market in the mountainous areas, targeted measures need to be adopted by the government, such as developing land consolidation projects, adjusting grain production subsidies and cropping structure, increasing the amount of agricultural machinery purchase allowance, and so on. This study also identified issues that require further research, for example, why free-of-compensation transfer is common in the mountainous areas and what is the role of the government in farmland transfer.
