2013 Vol. 23, No. 2
Display Method:
Comparison Between Reconstructions of Global Anthropogenic Land Cover Change over Past Two Millennia
2013, 23(2): 131-146.
Abstract:
Three global datasets, the History Database of the Global Environment (HYDE), Kaplan and Krumhardt (KK) and Pongratz of reconstructed anthropogenic land cover change (ALCC) were introduced and compared in this paper. The HYDE dataset was reconstructed by Goldewijk and his colleagues at the National Institute of Public Health and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pongratz and her colleagues at the Max Planck Institute for Meteorology in Germany, covering AD 800–1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the reconstructions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.
Three global datasets, the History Database of the Global Environment (HYDE), Kaplan and Krumhardt (KK) and Pongratz of reconstructed anthropogenic land cover change (ALCC) were introduced and compared in this paper. The HYDE dataset was reconstructed by Goldewijk and his colleagues at the National Institute of Public Health and the Environment in Netherland, covering the past 12 000 years. The KK dataset was reconstructed by Kaplan and his colleagues, the Soil-Vegetation-Atmosphere Research Group at the Institute of Environmental Engineering in Switzerland, covering the past 8000 years. The Pongratz dataset was reconstructed by Pongratz and her colleagues at the Max Planck Institute for Meteorology in Germany, covering AD 800–1992. The results show that the reconstructed datasets are quite different from each other due to the different methods used. The three datasets all allocated the historical ALCC according to human population density. The main reason causing the differences among the three datasets lies on the different relationships between population density and land use used in each reconstructed dataset. The KK dataset is better than the other two datasets for two important reasons. First, it used the nonlinear relationship between population density and land use, while the other two used the linear relationship. Second, Kaplan and his colleagues adopted the technological development and intensification parameters and considered the wood harvesting and the long-term fallow area resulted from shifting cultivation, which were neglected in the reconstructions of the other two datasets. Therefore, the KK dataset is more suitable as one of the anthropogenic forcing fields for climate simulation over the past two millennia that is recently concerned by two projects, the National Basic Research Program and the Strategic and Special Frontier Project of Science and Technology of the Chinese Academy of Sciences.
2013, 23(2): 147-162.
Abstract:
Objectively, a complex interactive coercing relationship exists between urbanization and eco-environment, and the research of this relationship is primarily divided into three schools, i.e., interactive coercion theory, interactive promotion theory and coupling symbiosis theory. Harmonizing the relationship between urbanization and eco-environment is not only an important proposition for the national development plan but also the only way to promote healthy urbanization. Based on an analysis of urbanization process and its relationship with the eco-environment, this article analyzes interactive coercing effects between urbanization and eco-environment from three perspectives of population urbanization, economic urbanization and spatial urbanization, respectively, and analyzes risk effects of the interactive coercion. Further, it shows six basic laws followed by interactive coercion between urbanization and eco-environment, namely, coupling fission law, dynamic hierarchy law, stochastic fluctuation law, non-linear synergetic law, threshold value law and forewarning law, and divides the interactive coercing process into five stages, namely, low-level coordinate, antagonistic, break-in, ameliorative and high-grade coordinate. Based on the geometric derivation, the interactive coercing relationship between urbanization and eco-environment is judged to be non-linear and it can be explained by a double-exponential function formed by the combination of power and exponential functions. Then, the evolutionary types of the interactive coercing relationship are divided into nine ones: rudimentary coordinating, ecology-dominated, synchronal coordinating, urbanization lagging, stepwise break-in, exorbitant urbanization, fragile ecology, rudimentary break-in and unsustainable types. Finally, based on an interactive coercion model, the degree of interactive coercion can be examined, and then, an evolutionary cycle can be divided into four phases, namely rudimentary symbiosis, harmonious development, utmost increasing and spiral type rising. The study results offer a scientific decision-making of healthy urbanization for achieving the goal of eco-environment protection and promoting urbanization.
Objectively, a complex interactive coercing relationship exists between urbanization and eco-environment, and the research of this relationship is primarily divided into three schools, i.e., interactive coercion theory, interactive promotion theory and coupling symbiosis theory. Harmonizing the relationship between urbanization and eco-environment is not only an important proposition for the national development plan but also the only way to promote healthy urbanization. Based on an analysis of urbanization process and its relationship with the eco-environment, this article analyzes interactive coercing effects between urbanization and eco-environment from three perspectives of population urbanization, economic urbanization and spatial urbanization, respectively, and analyzes risk effects of the interactive coercion. Further, it shows six basic laws followed by interactive coercion between urbanization and eco-environment, namely, coupling fission law, dynamic hierarchy law, stochastic fluctuation law, non-linear synergetic law, threshold value law and forewarning law, and divides the interactive coercing process into five stages, namely, low-level coordinate, antagonistic, break-in, ameliorative and high-grade coordinate. Based on the geometric derivation, the interactive coercing relationship between urbanization and eco-environment is judged to be non-linear and it can be explained by a double-exponential function formed by the combination of power and exponential functions. Then, the evolutionary types of the interactive coercing relationship are divided into nine ones: rudimentary coordinating, ecology-dominated, synchronal coordinating, urbanization lagging, stepwise break-in, exorbitant urbanization, fragile ecology, rudimentary break-in and unsustainable types. Finally, based on an interactive coercion model, the degree of interactive coercion can be examined, and then, an evolutionary cycle can be divided into four phases, namely rudimentary symbiosis, harmonious development, utmost increasing and spiral type rising. The study results offer a scientific decision-making of healthy urbanization for achieving the goal of eco-environment protection and promoting urbanization.
2013, 23(2): 163-172.
Abstract:
The northeastern China, the United States, and the western Europe are important agricultural regions both on the global and regional scales. The western Europe has a longer history of agricultural land development than the eastern United States. These two regions have changed from the deforestation and reclamation phase in the past to the current land abandonment and reforestation phase. Compared with the two regions, large-scale land exploitation has only been practiced in the northeastern China during the last century. After a short high-intensity deforestation and reclamation period, agricultural and forest lands are basically in a dynamic steady state. By comparing domestic and international agro-forestry development and considering the ecological environment and socio-economic benefits that can be derived from agro-forestry, this paper suggests that large area of reforestation would be inevitable in future though persistent and large agricultural demand in coming decades even more. And local reforestation at slope farmland with ecological vulnerability should be imperative at present to avoid severer damage. At the same time, from the perspective of Land Change Science, the results demonstrate that the research on land use change in the agro-forestry ecotone is typical and critical, particularly those dealing with the analysis of spatial and temporal characteristics and the simulation of climate, hydrology, and other environmental effects.
The northeastern China, the United States, and the western Europe are important agricultural regions both on the global and regional scales. The western Europe has a longer history of agricultural land development than the eastern United States. These two regions have changed from the deforestation and reclamation phase in the past to the current land abandonment and reforestation phase. Compared with the two regions, large-scale land exploitation has only been practiced in the northeastern China during the last century. After a short high-intensity deforestation and reclamation period, agricultural and forest lands are basically in a dynamic steady state. By comparing domestic and international agro-forestry development and considering the ecological environment and socio-economic benefits that can be derived from agro-forestry, this paper suggests that large area of reforestation would be inevitable in future though persistent and large agricultural demand in coming decades even more. And local reforestation at slope farmland with ecological vulnerability should be imperative at present to avoid severer damage. At the same time, from the perspective of Land Change Science, the results demonstrate that the research on land use change in the agro-forestry ecotone is typical and critical, particularly those dealing with the analysis of spatial and temporal characteristics and the simulation of climate, hydrology, and other environmental effects.
2013, 23(2): 173-181.
Abstract:
The aim of this study is to evaluate the current state of rural area development at the county level in the eastern coastal China. An evaluation index system including 18 factors was developed, and a rural development index (RDI) was constructed to evaluate rural development state in 2000, 2004, and 2008. The quantitative evaluation indicated the following results. 1) This study derived four dominating components by means of principal component analysis, which can explain 78.2% of the total information, namely agricultural production input, the basic condition of agriculture, the comparative effectiveness of grain production, and the household′s own basic conditions. 2) Since the turn of the new millennium, the rural area in the eastern coastal China has experienced a rapid development in general. Well developed, developed, moderately developed and undeveloped rural areas respectively occupied 29.32%, 22.33%, 21.91%, and 10.51% in 2008. 3) The countryside had maintained a sound momentum of developing trend between 2000 and 2008, while the rural development in the eastern coastal China lacked sustainability. And 4) industrialization, urbanization, original economic basis, and location are four major driving forces of the disparity of rural area development in the eastern coastal China. Given these results, the strategies and policies for the improvement of each rural group were put forward.
The aim of this study is to evaluate the current state of rural area development at the county level in the eastern coastal China. An evaluation index system including 18 factors was developed, and a rural development index (RDI) was constructed to evaluate rural development state in 2000, 2004, and 2008. The quantitative evaluation indicated the following results. 1) This study derived four dominating components by means of principal component analysis, which can explain 78.2% of the total information, namely agricultural production input, the basic condition of agriculture, the comparative effectiveness of grain production, and the household′s own basic conditions. 2) Since the turn of the new millennium, the rural area in the eastern coastal China has experienced a rapid development in general. Well developed, developed, moderately developed and undeveloped rural areas respectively occupied 29.32%, 22.33%, 21.91%, and 10.51% in 2008. 3) The countryside had maintained a sound momentum of developing trend between 2000 and 2008, while the rural development in the eastern coastal China lacked sustainability. And 4) industrialization, urbanization, original economic basis, and location are four major driving forces of the disparity of rural area development in the eastern coastal China. Given these results, the strategies and policies for the improvement of each rural group were put forward.
2013, 23(2): 182-193.
Abstract:
The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.
The spatial distribution of soil physical properties is essential for modeling and understanding hydrological processes. In this study, the different spatial information (the conventional soil types map-based spatial information (STMB) versus refined spatial information map (RSIM)) of soil physical properties, including field capacity, soil porosity and saturated hydraulic conductivity are used respectively as input data for Water Flow Model for Lake Catchment (WATLAC) to determine their effectiveness in simulating hydrological processes and to expound the effects on model performance in terms of estimating groundwater recharge, soil evaporation, runoff generation as well as partitioning of surface and subsurface water flow. The results show that: 1) the simulated stream flow hydrographs based on the STMB and RSIM soil data reproduce the observed hydrographs well. There is no significant increase in model accuracy as more precise soil physical properties information being used, but WATLAC model using the RSIM soil data could predict more runoff volume and reduce the relative runoff depth errors; 2) the groundwater recharges have a consistent trend for both cases, while the STMB soil data tend to produce higher groundwater recharges than the RSIM soil data. In addition, the spatial distribution of annual groundwater recharge is significantly affected by the spatial distribution of soil physical properties; 3) the soil evaporation simulated using the STMB and RSIM soil data are similar to each other, and the spatial distribution patterns are also insensitive to the spatial information of soil physical properties; and 4) although the different spatial information of soil physical properties does not cause apparent difference in overall stream flow, the partitioning of surface and subsurface water flow is distinct. The implications of this study are that the refined spatial information of soil physical properties does not necessarily contribute to a more accurate prediction of stream flow, and the selection of appropriate soil physical property data needs to consider the scale of watersheds and the level of accuracy required.
2013, 23(2): 194-202.
Abstract:
The southern sea area of the Huludao City, Liaoning Province might be polluted by heavy metals because it is close to the Jinzhou Bay, one of the heaviest sea area polluted by heavy metals in China. The undisturbed modern sediment core can be used to analyze the accumulation and source of the pollutants using 137Cs and 210Pbex. Thirty-five samples of surface sediment and two core sediments were collected from the southern sea area of Huludao City. The concentrations of copper (Cu), lead (Pb), chrome (Cr), zinc (Zn), arsenic (As) and mercury (Hg) in the surface sediments as well as Cu, Pb, Zn, Cr, 137Cs and 210Pbex in the core sediments were determined to research the spatial distribution and accumulation characteristics, and to analyze the sources and the potential risks of heavy metals. The results show that the pollution levels of Zn and Hg are serious, and 26 stations are at moderate or heavy ecological risks. The concentrations of the heavy metals increase from east to west, as well as from open sea to offshore marine area. The concentrations of heavy metals are not high in the sediments adjacent to the Jinzhou Bay, and the influence caused by the seawater exchange with the Jinzhou Bay is little. The concentrations of the heavy metals in the core sediments show low-high-low characteristic, and it coincides with the pollution history of Huludao City. The atmospheric deposition of heavy metals from the Huludao Zinc Plant is likely to be the main source of pollution without direct discharge of wastewater. The high concentrations of heavy metals appear on the upper sediment of 20 cm. The shallow sediment with high heavy metal contents might be exposed to surface when it was disturbed by the ocean engineering and big storm surge, then cause risk to the safety of aquaculture and human healthy.
The southern sea area of the Huludao City, Liaoning Province might be polluted by heavy metals because it is close to the Jinzhou Bay, one of the heaviest sea area polluted by heavy metals in China. The undisturbed modern sediment core can be used to analyze the accumulation and source of the pollutants using 137Cs and 210Pbex. Thirty-five samples of surface sediment and two core sediments were collected from the southern sea area of Huludao City. The concentrations of copper (Cu), lead (Pb), chrome (Cr), zinc (Zn), arsenic (As) and mercury (Hg) in the surface sediments as well as Cu, Pb, Zn, Cr, 137Cs and 210Pbex in the core sediments were determined to research the spatial distribution and accumulation characteristics, and to analyze the sources and the potential risks of heavy metals. The results show that the pollution levels of Zn and Hg are serious, and 26 stations are at moderate or heavy ecological risks. The concentrations of the heavy metals increase from east to west, as well as from open sea to offshore marine area. The concentrations of heavy metals are not high in the sediments adjacent to the Jinzhou Bay, and the influence caused by the seawater exchange with the Jinzhou Bay is little. The concentrations of the heavy metals in the core sediments show low-high-low characteristic, and it coincides with the pollution history of Huludao City. The atmospheric deposition of heavy metals from the Huludao Zinc Plant is likely to be the main source of pollution without direct discharge of wastewater. The high concentrations of heavy metals appear on the upper sediment of 20 cm. The shallow sediment with high heavy metal contents might be exposed to surface when it was disturbed by the ocean engineering and big storm surge, then cause risk to the safety of aquaculture and human healthy.
2013, 23(2): 203-215.
Abstract:
The deteriorating water quality in the Taihu Lake Basin has attracted widespread attention for many years, and is correlated with a sharp increase in the quantity of pollutant discharge such as agricultural fertilizers and industrial wastewater. In this study, several factors were selected for evaluating and regionalizing the water environmental capacity by ArcGIS spatial analysis, including geomorphologic characteristics, water quality goals, water body accessibility, water-dilution channels, and current water quality. Then, the spatial optimization of agriculture and industry was adjusted through overlay analysis, based on the balance between industrial space and water environmental capacity. The results show that the water environmental capacity gradually decreases from the west to the east, in contrast, the pollution caused by industrial and agricultural clustering is distributes along Taihu Lake, Gehu Lake and urban districts. The analysis of the agricultural space focuses on optimizing key protected areas of the Taihu Lake Basin, and the shores of Gehu Lake, optimally adjusting the second protected areas of the Taihu Lake Basin, and generally adjusting the urban areas of Changzhou and Wuxi cities. The analysis of industrial space focuses on optimizing the downtowns of Changzhou and Wuxi cities, optimally adjusting key protected areas and second protected areas of the Taihu Lake Basin, and generally adjusting the south and southwest of Gehu Lake. Lastly, some schemes of industrial and agricultural layouts and policies for the direction of industrial and agricultural development were proposed, reflecting a correlation between industry and agriculture and the water environment.
The deteriorating water quality in the Taihu Lake Basin has attracted widespread attention for many years, and is correlated with a sharp increase in the quantity of pollutant discharge such as agricultural fertilizers and industrial wastewater. In this study, several factors were selected for evaluating and regionalizing the water environmental capacity by ArcGIS spatial analysis, including geomorphologic characteristics, water quality goals, water body accessibility, water-dilution channels, and current water quality. Then, the spatial optimization of agriculture and industry was adjusted through overlay analysis, based on the balance between industrial space and water environmental capacity. The results show that the water environmental capacity gradually decreases from the west to the east, in contrast, the pollution caused by industrial and agricultural clustering is distributes along Taihu Lake, Gehu Lake and urban districts. The analysis of the agricultural space focuses on optimizing key protected areas of the Taihu Lake Basin, and the shores of Gehu Lake, optimally adjusting the second protected areas of the Taihu Lake Basin, and generally adjusting the urban areas of Changzhou and Wuxi cities. The analysis of industrial space focuses on optimizing the downtowns of Changzhou and Wuxi cities, optimally adjusting key protected areas and second protected areas of the Taihu Lake Basin, and generally adjusting the south and southwest of Gehu Lake. Lastly, some schemes of industrial and agricultural layouts and policies for the direction of industrial and agricultural development were proposed, reflecting a correlation between industry and agriculture and the water environment.
Improvement of Glacial Lakes Detection under Shadow Environment Using ASTER Data in Himalayas, Nepal
2013, 23(2): 216-226.
Abstract:
The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive area of the global climate changes. In the Himalayas, some of glacial lakes are covered by the dark mountains′ shadow because of their location. Therefore, these lakes can not be detected by conventional method such as Normalized Difference Water Index (NDWI), because the reflectance feature of shadowed glacial lake is different comparing to the ones which are located in the open flat area. The shadow causes two major problems: 1) glacial lakes which are covered by shadow completely result in underestimation of the number of glacial lakes; 2) glacial lakes which are partly identified are considered to undervalue the area of glacial lakes. The aim of this study is to develop a new model, named Detection of Shadowed Glacial Lakes (DSGL) model, to identify glacial lakes under the shadow environment by using Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data in the Himalayas, Nepal. The DSGL model is based on integration of two different modifications of NDWI, namely NDWIs model and NDWIshe model. NDWIs is defined as integration of the NDWI and slope analysis and used for detecting non-shadowed lake in the mountain area. The NDWIshe is proposed as a new methodology to overcome the weakness of NDWIs on identifying shadowed lakes in highly elevated mountainous area such as the Himalayas. The first step of the NDWIshe is to enhance the data from ASTER 1B using the histogram equalization (HE) method, and its outcome product is named ASTERhe. We used the ASTERhe for calculating the NDWIhe and the NDWIshe. Integrated with terrain analysis using Digital Elevation Model (DEM) data, the NDWIshe can be used to identify the shadowed glacial lakes in the Himalayas. NDWIs value of 0.41 is used to identify the glacier lake (NDWIs ≥ 0.41), and 0.3 of NDWIshe is used to identify the shadowed glacier lake (NDWIshe ≤ 0.3). The DSGL model was proved to be able to classify the glacial lakes more accurately, while the NDWI model had tendency to underestimate the presence of actual glacial lakes. Correct classification rate regarding the products from NDWI model and DSGL model were 57% and 99%, respectively. The results of this paper demonstrated that the DSGL model is promising to detect glacial lakes in the shadowed environment at high mountains.
The detection of glacial lake change in the Himalayas, Nepal is extremely significant since the glacial lake change is one of the crucial indicators of global climate change in this area, where is the most sensitive area of the global climate changes. In the Himalayas, some of glacial lakes are covered by the dark mountains′ shadow because of their location. Therefore, these lakes can not be detected by conventional method such as Normalized Difference Water Index (NDWI), because the reflectance feature of shadowed glacial lake is different comparing to the ones which are located in the open flat area. The shadow causes two major problems: 1) glacial lakes which are covered by shadow completely result in underestimation of the number of glacial lakes; 2) glacial lakes which are partly identified are considered to undervalue the area of glacial lakes. The aim of this study is to develop a new model, named Detection of Shadowed Glacial Lakes (DSGL) model, to identify glacial lakes under the shadow environment by using Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data in the Himalayas, Nepal. The DSGL model is based on integration of two different modifications of NDWI, namely NDWIs model and NDWIshe model. NDWIs is defined as integration of the NDWI and slope analysis and used for detecting non-shadowed lake in the mountain area. The NDWIshe is proposed as a new methodology to overcome the weakness of NDWIs on identifying shadowed lakes in highly elevated mountainous area such as the Himalayas. The first step of the NDWIshe is to enhance the data from ASTER 1B using the histogram equalization (HE) method, and its outcome product is named ASTERhe. We used the ASTERhe for calculating the NDWIhe and the NDWIshe. Integrated with terrain analysis using Digital Elevation Model (DEM) data, the NDWIshe can be used to identify the shadowed glacial lakes in the Himalayas. NDWIs value of 0.41 is used to identify the glacier lake (NDWIs ≥ 0.41), and 0.3 of NDWIshe is used to identify the shadowed glacier lake (NDWIshe ≤ 0.3). The DSGL model was proved to be able to classify the glacial lakes more accurately, while the NDWI model had tendency to underestimate the presence of actual glacial lakes. Correct classification rate regarding the products from NDWI model and DSGL model were 57% and 99%, respectively. The results of this paper demonstrated that the DSGL model is promising to detect glacial lakes in the shadowed environment at high mountains.
2013, 23(2): 227-236.
Abstract:
Xiamen is an economically competitive and highly urbanized city along the coastal area of Fujian Province, China. The research on spatio-temporal variation of landscape heterogeneity under the influence of human activities is of great importance to the further study on the relationship of landscape pattern and ecological process. It is also crucial to the discovery of spatial variation and intensity distribution of human activities. The research analyzed the intensity of human impacts and the spatial variation features and dynamics of landscape patterns by introducing statistical theories and approaches. We analyzed spatio-temporal variation of landscape heterogeneity using the geostatistical techniques, such as semivariogram and Kriging interpolation．Results show that there is a higher correlation between landscape heterogeneity indexes and human impact index. Both the indexes show a moderate spatial autocorrelation as well as an obvious characteristic of anisotropy. From 1998 to 2008, the spatial differentiation of the changes in the intensity of human activities and the changes in landscape heterogeneity shows that the landscape patterns in Xiamen are closely related with the urban land utilization methods, the condition of traffic and geographical location and the physical geographical condition such as the terrain and the ecological environment. The process of urbanization has a significant impact on the urban landscape pattern.
Xiamen is an economically competitive and highly urbanized city along the coastal area of Fujian Province, China. The research on spatio-temporal variation of landscape heterogeneity under the influence of human activities is of great importance to the further study on the relationship of landscape pattern and ecological process. It is also crucial to the discovery of spatial variation and intensity distribution of human activities. The research analyzed the intensity of human impacts and the spatial variation features and dynamics of landscape patterns by introducing statistical theories and approaches. We analyzed spatio-temporal variation of landscape heterogeneity using the geostatistical techniques, such as semivariogram and Kriging interpolation．Results show that there is a higher correlation between landscape heterogeneity indexes and human impact index. Both the indexes show a moderate spatial autocorrelation as well as an obvious characteristic of anisotropy. From 1998 to 2008, the spatial differentiation of the changes in the intensity of human activities and the changes in landscape heterogeneity shows that the landscape patterns in Xiamen are closely related with the urban land utilization methods, the condition of traffic and geographical location and the physical geographical condition such as the terrain and the ecological environment. The process of urbanization has a significant impact on the urban landscape pattern.
2013, 23(2): 237-248.
Abstract:
Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.
Mechanism and modeling of the land subsidence are complex because of the complicate geological background in Beijing, China. This paper analyzed the spatial relationship between land subsidence and three factors, including the change of groundwater level, the thickness of compressible sediments and the building area by using remote sensing and GIS tools in the upper-middle part of alluvial-proluvial plain fan of the Chaobai River in Beijing. Based on the spatial analysis of the land subsidence and three factors, there exist significant non-linear relationship between the vertical displacement and three factors. The Back Propagation Neural Network (BPN) model combined with Genetic Algorithm (GA) was used to simulate regional distribution of the land subsidence. Results showed that at field scale, the groundwater level and land subsidence showed a significant linear relationship. However, at regional scale, the spatial distribution of groundwater depletion funnel did not overlap with the land subsidence funnel. As to the factor of compressible strata, the places with the biggest compressible strata thickness did not have the largest vertical displacement. The distributions of building area and land subsidence have no obvious spatial relationships. The BPN-GA model simulation results illustrated that the accuracy of the trained model during fifty years is acceptable with an error of 51% of verification data less than 20 mm and the average of the absolute error about 32 mm. The BPN model could be utilized to simulate the general distribution of land subsidence in the study area. Overall, this work contributes to better understand the complex relationship between the land subsidence and three influencing factors. And the distribution of the land subsidence can be simulated by the trained BPN-GA model with the limited available dada and acceptable accuracy.
2013, 23(2): 249-260.
Abstract:
Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.
Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.
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