2019年 第29卷 第5期
2019, 20(5): 725-740.
doi: 10.1007/s11769-019-1063-x
摘要:
Understanding the net primary productivity (NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach (CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia (1737.23×104 km2), while the grassland area in Europe was relatively small (202.83×104 km2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas (560.10 g C/(m2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation (AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
Understanding the net primary productivity (NPP) of grassland is crucial to evaluate the terrestrial carbon cycle. In this study, we investigated the spatial distribution and the area of global grassland across the globe. Then, we used the Carnegie-Ames-Stanford Approach (CASA) model to estimate global grassland NPP and explore the spatio-temporal variations of grassland NPP in response to climate change from 1982 to 2008. Results showed that the largest area of grassland distribution during the study period was in Asia (1737.23×104 km2), while the grassland area in Europe was relatively small (202.83×104 km2). Temporally, the total NPP increased with fluctuations from 1982 to 2008, with an annual increase rate of 0.03 Pg C/yr. The total NPP experienced a significant increasing trend from 1982 to 1995, while a decreasing trend was observed from 1996 to 2008. Spatially, the grassland NPP in South America and Africa were higher than the other regions, largely as a result of these regions are under warm and wet climatic conditions. The highest mean NPP was recorded for savannas (560.10 g C/(m2·yr)), whereas the lowest was observed in open shrublands with an average NPP of 162.53 g C/(m2·yr). The relationship between grassland NPP and annual mean temperature and annual precipitation (AMT, AP, respectively) varies with changes in AP, which indicates that, grassland NPP is more sensitive to precipitation than temperature.
2019, 20(5): 741-755.
doi: 10.1007/s11769-019-1069-4
摘要:
Lake monitoring by remote sensing is of significant importance to understanding the lake and ambient ecological and environmental processes. In particular, whether lake water storage variation could predict lake surfacial temperature or vice versa has long fascinated the research community, in that it would greatly benefit the monitoring missions and scientific interpretation of the lake change processes. This study attempted to remotely detect the dynamics of the Aral Sea and pursue the relationships between varying lake water storage attributes and surface water temperature by using MODIS LST (Moderate-resolution Imaging Spectroradiometer Land Surface Temperature) 8-day composite products, satellite altimeter data, and actual meteorological measurements. Their associations with lake Surface Water Temperatures (SWT) were then analyzed. Results showed the lake water surface areas and elevations of the North Aral Sea tended to increasing trend from 2001 (2793.0 km2, 13.6 m) to 2015 (6997.8 km2, 15.9 m), while those of the South Aral Sea showed a decreasing trend during 2001 (20 434.6 km2, 3.9 m) and 2015 (3256.1 km2, 0.9 m). In addition, the annual daytime and nighttime lake SWT both decreased in the North Aral Sea, while only the daytime SWT in the South Aral Sea exhibited an increase, indicating a rising deviation of diurnal temperatures in the South Aral Sea during the past 15 yr. Moreover, a lower correlation was found between variations in the daytime SWT and storage capacity in the South Aral Sea (R2=0.33; P<0.05), no fair correlations were tested between lake water storage and daytime SWT in the North Aral Sea nor between lake water storage and nighttime SWT in either part of the sea. These results implied that climate change, if any at least during the research period, has no significant effects on lake dynamics over the two sectors of the Aral Sea with anthropogenic disturbances. However, climate change and human activities may overlap to explain complex consequences in the lake storage variations. Our results may provide a reference for monitoring the spatiotemporal variations of lakes, increasing understanding of the lake water storage changes in relation to the lake SWT, which may benefit the ecological management of the Aral Sea region, in the effort to face the likely threats from climate change and human activities to the region.
Lake monitoring by remote sensing is of significant importance to understanding the lake and ambient ecological and environmental processes. In particular, whether lake water storage variation could predict lake surfacial temperature or vice versa has long fascinated the research community, in that it would greatly benefit the monitoring missions and scientific interpretation of the lake change processes. This study attempted to remotely detect the dynamics of the Aral Sea and pursue the relationships between varying lake water storage attributes and surface water temperature by using MODIS LST (Moderate-resolution Imaging Spectroradiometer Land Surface Temperature) 8-day composite products, satellite altimeter data, and actual meteorological measurements. Their associations with lake Surface Water Temperatures (SWT) were then analyzed. Results showed the lake water surface areas and elevations of the North Aral Sea tended to increasing trend from 2001 (2793.0 km2, 13.6 m) to 2015 (6997.8 km2, 15.9 m), while those of the South Aral Sea showed a decreasing trend during 2001 (20 434.6 km2, 3.9 m) and 2015 (3256.1 km2, 0.9 m). In addition, the annual daytime and nighttime lake SWT both decreased in the North Aral Sea, while only the daytime SWT in the South Aral Sea exhibited an increase, indicating a rising deviation of diurnal temperatures in the South Aral Sea during the past 15 yr. Moreover, a lower correlation was found between variations in the daytime SWT and storage capacity in the South Aral Sea (R2=0.33; P<0.05), no fair correlations were tested between lake water storage and daytime SWT in the North Aral Sea nor between lake water storage and nighttime SWT in either part of the sea. These results implied that climate change, if any at least during the research period, has no significant effects on lake dynamics over the two sectors of the Aral Sea with anthropogenic disturbances. However, climate change and human activities may overlap to explain complex consequences in the lake storage variations. Our results may provide a reference for monitoring the spatiotemporal variations of lakes, increasing understanding of the lake water storage changes in relation to the lake SWT, which may benefit the ecological management of the Aral Sea region, in the effort to face the likely threats from climate change and human activities to the region.
2019, 20(5): 756-767.
doi: 10.1007/s11769-019-1070-y
摘要:
Vegetation indices (VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI (normalized difference vegetation index) and SR (simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date (SOS) and end date (EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.
Vegetation indices (VIs) from satellite remote sensing have been extensively applied to analyze the trends of vegetation phenology. In this paper, the NDVI (normalized difference vegetation index) and SR (simple ration), which are calculated from the same spectral bands of MODIS data with different mathematical expressions, were used to extract the start date (SOS) and end date (EOS) of the growing season in northern China and Mongolia from 2000 to 2015. The results show that different vegetation indices would lead to differences in vegetation phenology especially in their trends. The mean SOS from NDVI is 15.5 d earlier than that from SR, and the mean EOS from NDVI is 13.4 d later than that from SR. It should be noted that 16.3% of SOS and 17.2% of EOS derived from NDVI and SR exhibit opposite trends. The phenology dates and trends from NDVI are also inconsistent with those of SR among various vegetation types. These differences based on different mathematical expressions in NDVI and SR result from different resistances to noise and sensitivities to spectral signal at different stage of growing season. NDVI is prone to be effected more by low noise and is less sensitive to dense vegetation. While SR is affected more by high noise and is less sensitive to sparse vegetation. Therefore, vegetation indices are one of the uncertainty sources of remote sensing-based phenology, and appropriate indices should be used to detect vegetation phenology for different growth stages and estimate phenology trends.
2019, 20(5): 768-783.
doi: 10.1007/s11769-019-1066-7
摘要:
In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July.~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to the other years of 2010-2014; 2) the drought had a more severe impact on agroecosystems (crop and grassland) than on forests; 3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence (SIF) and gross primary production (GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest (1.76 Pg C/yr) during 2010-2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.
In the summer of 2012, the US Midwest, the most productive agricultural region in the world, experienced the most intense and widespread drought on record for the past hundred years. The 2012 drought, characterized as ‘flash drought’, developed in May with a rapid intensification afterwards, and peaked in mid-July.~76% of crop region and 60% of grassland and pasture regions have been under moderate to severe dry conditions. This study used multiple lines of evidences, i.e., in-situ AmeriFlux measurements, spatial satellite observations, and scaled ecosystem modeling, to provide independent and complementary analysis on the impact of 2012 flash drought on the US Midwest vegetation greenness and photosynthesis carbon uptake. Three datasets consistently showed that 1) phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to the other years of 2010-2014; 2) the drought had a more severe impact on agroecosystems (crop and grassland) than on forests; 3) the growth of crop and grassland was suppressed from June with significant reduction of vegetation index, sun-induced fluorescence (SIF) and gross primary production (GPP), and did not recover until the end of growing season. The modeling results showed that regional total GPP in 2012 was the lowest (1.76 Pg C/yr) during 2010-2014, and decreased by 63 Tg C compared with the other-year mean. Agroecosystems, accounting for 84% of regional GPP assimilation, were the most impacted by 2012 drought with total GPP reduction of 9%, 7%, 6%, and 29% for maize, soybean, cropland, and grassland, respectively. The frequency and severity of droughts have been predicted to increase in future. The results imply the importance to investigate the influences of flash droughts on vegetation productivity and terrestrial carbon cycling.
2019, 20(5): 784-797.
doi: 10.1007/s11769-019-1071-x
摘要:
Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity (EC) estimation systematically. Additionally, the performance of Multiple Linear Regression (MLR), Geographically Weighted Regression (GWR), and Random Forest regression (RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that:1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.
Timely monitoring and early warning of soil salinity are crucial for saline soil management. Environmental variables are commonly used to build soil salinity prediction model. However, few researches have been done to summarize the environmental sensitive variables for soil electrical conductivity (EC) estimation systematically. Additionally, the performance of Multiple Linear Regression (MLR), Geographically Weighted Regression (GWR), and Random Forest regression (RFR) model, the representative of current main methods for soil EC prediction, has not been explored. Taking the north of Yinchuan plain irrigation oasis as the study area, the feasibility and potential of 64 environmental variables, extracted from the Landsat 8 remote sensed images in dry season and wet season, the digital elevation model, and other data, were assessed through the correlation analysis and the performance of MLR, GWR, and RFR model on soil salinity estimation was compared. The results showed that:1) 10 of 15 imagery texture and spectral band reflectivity environmental variables extracted from Landsat 8 image in dry season were significantly correlated with soil EC, while only 3 of these indices extracted from Landsat 8 image in wet season have significant correlation with soil EC. Channel network base level, one of the terrain attributes, had the largest absolute correlation coefficient of 0.47 and all spatial location factors had significant correlation with soil EC. 2) Prediction accuracy of RFR model was slightly higher than that of the GWR model, while MLR model produced the largest error. 3) In general, the soil salinization level in the study area gradually increased from south to north. In conclusion, the remote sensed imagery scanned in dry season was more suitable for soil EC estimation, and topographic factors and spatial location also play a key role. This study can contribute to the research on model construction and variables selection for soil salinity estimation in arid and semiarid regions.
2019, 20(5): 798-808.
doi: 10.1007/s11769-019-1050-2
摘要:
Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature (LST) are, therefore, valuable for studying the dynamics of wetland systems. With the development of thermal remote sensing technology, LST retrieval with satellite images is a practicable way to detect a wetland and its neighboring area's thermal environment from a non-point visual angle rather than the traditional detection from a point visual angle. The mono-windows (MW) method of retrieving LST was validated. On the basis of estimated LST, we used Geographical Information System (GIS) technology to study the impact of wetland reclamation on local temperatures at a regional scale. Following that, correlations between LST and the wetland were analyzed. The results show that:1) It is feasible to retrieve the LST from Landsat 8 OLI satellite images with MW model. The model was validated with the land surface temperature observed in four meteorological stations when the satellite scanned the study region. The satellite retrieval error was approximately 1.01℃. 2) The relationship between the spatial distribution of land surface temperatures and the Zhalong wetland was analyzed based on GIS technology. The results show that wetland has an obvious influence on LST, and that this influence decreases with increasing distance from the wetland. When the distance from the wetland was less than 500 m, its influence on LST was significant. Results also illustrated that the effect of the wetland's different land use/land cover's LST distribution varied with different seasons.
Wetlands play a key role in regulating local climate as well as reducing impacts caused by climate change. Rapid observations of the land surface temperature (LST) are, therefore, valuable for studying the dynamics of wetland systems. With the development of thermal remote sensing technology, LST retrieval with satellite images is a practicable way to detect a wetland and its neighboring area's thermal environment from a non-point visual angle rather than the traditional detection from a point visual angle. The mono-windows (MW) method of retrieving LST was validated. On the basis of estimated LST, we used Geographical Information System (GIS) technology to study the impact of wetland reclamation on local temperatures at a regional scale. Following that, correlations between LST and the wetland were analyzed. The results show that:1) It is feasible to retrieve the LST from Landsat 8 OLI satellite images with MW model. The model was validated with the land surface temperature observed in four meteorological stations when the satellite scanned the study region. The satellite retrieval error was approximately 1.01℃. 2) The relationship between the spatial distribution of land surface temperatures and the Zhalong wetland was analyzed based on GIS technology. The results show that wetland has an obvious influence on LST, and that this influence decreases with increasing distance from the wetland. When the distance from the wetland was less than 500 m, its influence on LST was significant. Results also illustrated that the effect of the wetland's different land use/land cover's LST distribution varied with different seasons.
2019, 20(5): 809-819.
doi: 10.1007/s11769-018-0983-1
摘要:
Since the industrial revolution, human activities have both expanded and intensified across the globe resulting in accelerated land use change. Land use change driven by China's development has put pressure on the limited arable land resources, which has affected grain production. Competing land use interests are a potential threat to food security in China. Therefore, studying arable land use changes is critical for ensuring future food security and maintaining the sustainable development of arable land. Based on data from several major sources, we analyzed the spatio-temporal differences of arable land among different agricultural regions in China from 2000 to 2010 and identified the drivers of arable land expansion and loss. The results revealed that arable land decreased by 5.92 million ha or 3.31%. Arable land increased in the north and decreased in the south of China. Urbanization and ecological restoration programs were the main drivers of arable land loss, while the reclamation of other land cover types (e.g., forest, grassland, and wetland) was the primary source of the increased arable land. The majority of arable land expansion occurred in the Northwest, but the centroid for grain production moved to northeast, which indicated that new arable land was of poor quality and did not significantly contribute to the grain production capacity. When combined with the current ‘Red Line of Arable Land Policy’ (RAL) and ‘Ecological Redline Policy’ (EPR), this study can provide effective information for arable land policymaking and help guide the sustainable development of arable land.
Since the industrial revolution, human activities have both expanded and intensified across the globe resulting in accelerated land use change. Land use change driven by China's development has put pressure on the limited arable land resources, which has affected grain production. Competing land use interests are a potential threat to food security in China. Therefore, studying arable land use changes is critical for ensuring future food security and maintaining the sustainable development of arable land. Based on data from several major sources, we analyzed the spatio-temporal differences of arable land among different agricultural regions in China from 2000 to 2010 and identified the drivers of arable land expansion and loss. The results revealed that arable land decreased by 5.92 million ha or 3.31%. Arable land increased in the north and decreased in the south of China. Urbanization and ecological restoration programs were the main drivers of arable land loss, while the reclamation of other land cover types (e.g., forest, grassland, and wetland) was the primary source of the increased arable land. The majority of arable land expansion occurred in the Northwest, but the centroid for grain production moved to northeast, which indicated that new arable land was of poor quality and did not significantly contribute to the grain production capacity. When combined with the current ‘Red Line of Arable Land Policy’ (RAL) and ‘Ecological Redline Policy’ (EPR), this study can provide effective information for arable land policymaking and help guide the sustainable development of arable land.
2019, 20(5): 820-833.
doi: 10.1007/s11769-019-1034-2
摘要:
Based on geographic information system (GIS) spatial analysis technology, the spatial pattern of raster grid transport accessibility for the Guangdong-Hong Kong-Macao Greater Bay area was studied and the states of spatial connectedness were simulated using highway passenger transport, railway passenger transport, port passenger transport and aviation passenger transport data. The result shows that transport accessibility within the Guangdong-Hong Kong-Macao Greater Bay area costs ‘one hour’ and the spatial distribution of accessibility in the area presents clear ‘core-periphery’ spatial characteristics, with Guangzhou, Foshan, Shenzhen constituting the core. The transport accessibility of Guangdong-Hong Kong-Macao is high. Average accessibility of urban nodes as measured by travel time is 0.99 h, and the areas accessible within 1.42 h occupy 79.14% of the total area. Most of the areas with the lowest accessibility are found in the peripheral area, with the worst accessibility being 4.73 h. Compared with the west-side cities, the economically developed east-side cities of the Guangdong-Hong Kong-Macao Greater Bay area have higher connectivity with roads, railways, ports, and aviation transport. Guangzhou, Foshan, Zhuhai, Shenzhen, Hong Kong and Macao are closely linked. The higher the accessibility, the closer the intercity connectedness.
Based on geographic information system (GIS) spatial analysis technology, the spatial pattern of raster grid transport accessibility for the Guangdong-Hong Kong-Macao Greater Bay area was studied and the states of spatial connectedness were simulated using highway passenger transport, railway passenger transport, port passenger transport and aviation passenger transport data. The result shows that transport accessibility within the Guangdong-Hong Kong-Macao Greater Bay area costs ‘one hour’ and the spatial distribution of accessibility in the area presents clear ‘core-periphery’ spatial characteristics, with Guangzhou, Foshan, Shenzhen constituting the core. The transport accessibility of Guangdong-Hong Kong-Macao is high. Average accessibility of urban nodes as measured by travel time is 0.99 h, and the areas accessible within 1.42 h occupy 79.14% of the total area. Most of the areas with the lowest accessibility are found in the peripheral area, with the worst accessibility being 4.73 h. Compared with the west-side cities, the economically developed east-side cities of the Guangdong-Hong Kong-Macao Greater Bay area have higher connectivity with roads, railways, ports, and aviation transport. Guangzhou, Foshan, Zhuhai, Shenzhen, Hong Kong and Macao are closely linked. The higher the accessibility, the closer the intercity connectedness.
2019, 20(5): 834-847.
doi: 10.1007/s11769-019-1072-9
摘要:
Future or smart community, which mainly refers to the development of community information and communication technology (ICT) platforms or devices, has received considerable attention from urban governments and scholars. However, only a few studies have been conducted to test the actual effects of using these community ICT platforms or devices on the community satisfaction of residents. Therefore, the present study conducts a survey in 40 communities in Nanjing, China and uses a mixed linear regression model to determine the relationship between community ICT usage and community satisfaction. Results indicate that residents with high-level community ICT usage are more satisfied with their community than those with low-level community ICT usage. Moreover, evident differences are observed regarding the influence of new commodity, old commodity and affordable housing communities in Nanjing. These findings are meaningful for the construction and development of future communities.
Future or smart community, which mainly refers to the development of community information and communication technology (ICT) platforms or devices, has received considerable attention from urban governments and scholars. However, only a few studies have been conducted to test the actual effects of using these community ICT platforms or devices on the community satisfaction of residents. Therefore, the present study conducts a survey in 40 communities in Nanjing, China and uses a mixed linear regression model to determine the relationship between community ICT usage and community satisfaction. Results indicate that residents with high-level community ICT usage are more satisfied with their community than those with low-level community ICT usage. Moreover, evident differences are observed regarding the influence of new commodity, old commodity and affordable housing communities in Nanjing. These findings are meaningful for the construction and development of future communities.
2019, 20(5): 848-860.
doi: 10.1007/s11769-019-1073-8
摘要:
A study of the accessibility of a city's scenic spots via different travel modes can contribute to optimization of tourism-related transportation while improving tourists' travel-related satisfaction levels and advancing tourism. We systematically analyzed the accessibility of 56 scenic spots in Xi'an City, China, via car and public transport travel modes using the real-time travel function of the Baidu Maps API (Application Programming Interface) along with spatial analysis methods and the modal accessibility gap index of scenic spots. We obtained the following results. First, maximum and minimum travel times using public transport exceeded those using cars. Moreover, the accessibility of scenic spots via cars and public transport presented a circular spatial pattern of increasing travel time from the center to the periphery. Contrasting with travel by public transport, car travel showed a clear time-space compression effect. Second, accessibility of the scenic spots via cars and public transport showed some spatial heterogeneity, with no clear advantages of car accessibility in the central urban area. However, advantages of car accessibility were increasingly evident moving from the center to the periphery. Third, whereas the correlation of the modal accessibility gap index of scenic spots in Xi'an with global space was significantly positive, local spatial interdependence was only evident in some inner city areas and in marginal areas. Moreover, spatial heterogeneity was evident in two regions but was insignificant in other areas, indicating that the spatial interdependence of the modal accessibility gap index in most scenic spots was not apparent in terms of the overall effect of public transport routes, road networks, and the distribution of scenic spots. The improvement of public transport coverage in marginal areas and the optimization of public transport routes in central urban areas are essential tasks for improving travel using public transport in the future.
A study of the accessibility of a city's scenic spots via different travel modes can contribute to optimization of tourism-related transportation while improving tourists' travel-related satisfaction levels and advancing tourism. We systematically analyzed the accessibility of 56 scenic spots in Xi'an City, China, via car and public transport travel modes using the real-time travel function of the Baidu Maps API (Application Programming Interface) along with spatial analysis methods and the modal accessibility gap index of scenic spots. We obtained the following results. First, maximum and minimum travel times using public transport exceeded those using cars. Moreover, the accessibility of scenic spots via cars and public transport presented a circular spatial pattern of increasing travel time from the center to the periphery. Contrasting with travel by public transport, car travel showed a clear time-space compression effect. Second, accessibility of the scenic spots via cars and public transport showed some spatial heterogeneity, with no clear advantages of car accessibility in the central urban area. However, advantages of car accessibility were increasingly evident moving from the center to the periphery. Third, whereas the correlation of the modal accessibility gap index of scenic spots in Xi'an with global space was significantly positive, local spatial interdependence was only evident in some inner city areas and in marginal areas. Moreover, spatial heterogeneity was evident in two regions but was insignificant in other areas, indicating that the spatial interdependence of the modal accessibility gap index in most scenic spots was not apparent in terms of the overall effect of public transport routes, road networks, and the distribution of scenic spots. The improvement of public transport coverage in marginal areas and the optimization of public transport routes in central urban areas are essential tasks for improving travel using public transport in the future.
2019, 20(5): 861-871.
doi: 10.1007/s11769-019-1074-7
摘要:
Mongolia is an important country in the Economic Corridor of China-Mongolia-Russia, a deep understanding of the coupling relationship between urbanization and the eco-environment in Mongolia is meaningful to achieve green development of the Belt and Road. The entropy method and coupling coordination degree model were integrated to evaluate the coupling coordination degree between urbanization and the eco-environment in Mongolia during 2000-2016. The results showed that the coupling coordination degree between urbanization and the eco-environment in Mongolia was generally at the stage of seriously unbalanced development, and that the main contributor of the urbanization and the eco-environment subsystem were demographic urbanization and eco-environment endowment, respectively. The southern part of Mongolia central zone should be paid more attention due to the lower degree of coupling coordination between urbanization and the eco-environment. To promote the healthy urbanization development in Mongolia, six-layer eco-city establishing green development pattern is proposed to provide scientific support for Mongolia.
Mongolia is an important country in the Economic Corridor of China-Mongolia-Russia, a deep understanding of the coupling relationship between urbanization and the eco-environment in Mongolia is meaningful to achieve green development of the Belt and Road. The entropy method and coupling coordination degree model were integrated to evaluate the coupling coordination degree between urbanization and the eco-environment in Mongolia during 2000-2016. The results showed that the coupling coordination degree between urbanization and the eco-environment in Mongolia was generally at the stage of seriously unbalanced development, and that the main contributor of the urbanization and the eco-environment subsystem were demographic urbanization and eco-environment endowment, respectively. The southern part of Mongolia central zone should be paid more attention due to the lower degree of coupling coordination between urbanization and the eco-environment. To promote the healthy urbanization development in Mongolia, six-layer eco-city establishing green development pattern is proposed to provide scientific support for Mongolia.
2019, 20(5): 872-886.
doi: 10.1007/s11769-019-1064-9
摘要:
Under the background of ‘the Belt and Road’ initiative, the economic cooperation has great potential between China and Russia. The railway accessibility has an important influence on the economic connections of cities along the railway line. This paper studied the Sino-Russian transnational economic connection based on the railway class accessibility along Trans-Siberian railway (the transnational China railway branch line). The results are as following. First, the railway accessibility of the Chinese nodes is stronger than that of the Russian nodes, which in general displays a tendency of space attenuation from China to the Sino-Russian border, then to Russia. Spatially, the railway accessibility within the study area shows a ‘High East, Low West’ and ‘High South, Low North’ spatial pattern. The railway accessibility of the nodes, which are located at the beginning and end of the railway line, is weaker than those nodes located in the middle of the line. Second, the railway accessibility and external economic connection intensity summation of the nodes show a positive relationship along the railway line. The economic connection intensity summation of different nodes presents obvious regional differentiation. Finally, as economic connection network has evolved, the small world effect of Sino-Russian railway economic connection network becomes strong.
Under the background of ‘the Belt and Road’ initiative, the economic cooperation has great potential between China and Russia. The railway accessibility has an important influence on the economic connections of cities along the railway line. This paper studied the Sino-Russian transnational economic connection based on the railway class accessibility along Trans-Siberian railway (the transnational China railway branch line). The results are as following. First, the railway accessibility of the Chinese nodes is stronger than that of the Russian nodes, which in general displays a tendency of space attenuation from China to the Sino-Russian border, then to Russia. Spatially, the railway accessibility within the study area shows a ‘High East, Low West’ and ‘High South, Low North’ spatial pattern. The railway accessibility of the nodes, which are located at the beginning and end of the railway line, is weaker than those nodes located in the middle of the line. Second, the railway accessibility and external economic connection intensity summation of the nodes show a positive relationship along the railway line. The economic connection intensity summation of different nodes presents obvious regional differentiation. Finally, as economic connection network has evolved, the small world effect of Sino-Russian railway economic connection network becomes strong.
2019, 20(5): 887-904.
doi: 10.1007/s11769-019-1051-1
摘要:
Russia is the largest neighboring country of China. Between the two countries, the resources and industry are complemented, the political mutual trust is at a high level, and trade cooperation has a broad prospect. Choosing the best regions and the best industries to strengthen investment in Russia has a major strategic significance in promoting ‘the Belt and Road Initiative’ and China-Mongolia-Russia Economic Corridor Construction. However, the related researches are extremely limited. The investment environment is unclear, and the investment risk is unknown, which seriously restrict the investment in Russia and the trade cooperation with Russia. Our research team carried out scientific expedition, government visits and scientific research cooperation in Russia for several years, and obtained a great number of first-hand valuable data. According to the analysis on the data and Russian regional policies, this study constructed an investment environment evaluation model (ESI-PRA model), scientific assessed the investment environment for 83 subjects of federation in Russia, in terms of economic, social, infrastructure, policy, resource and accessibility, classified 4 types of investment regions, chose 3 investment priority regions, revealed the investment priority industries, demonstrated the main investment risks, and proposed the strategic policies. The research results provide direct scientific and technological support for strategic decisions, such as investment in Russia, bilateral economic and trade cooperation, and overseas layout of Chinese-funded enterprises. Moreover, it has an important practical and strategic significance for improving overseas geo-strategic interests of China and ensuring the construction of China-Mongolia-Russia Economic Corridor.
Russia is the largest neighboring country of China. Between the two countries, the resources and industry are complemented, the political mutual trust is at a high level, and trade cooperation has a broad prospect. Choosing the best regions and the best industries to strengthen investment in Russia has a major strategic significance in promoting ‘the Belt and Road Initiative’ and China-Mongolia-Russia Economic Corridor Construction. However, the related researches are extremely limited. The investment environment is unclear, and the investment risk is unknown, which seriously restrict the investment in Russia and the trade cooperation with Russia. Our research team carried out scientific expedition, government visits and scientific research cooperation in Russia for several years, and obtained a great number of first-hand valuable data. According to the analysis on the data and Russian regional policies, this study constructed an investment environment evaluation model (ESI-PRA model), scientific assessed the investment environment for 83 subjects of federation in Russia, in terms of economic, social, infrastructure, policy, resource and accessibility, classified 4 types of investment regions, chose 3 investment priority regions, revealed the investment priority industries, demonstrated the main investment risks, and proposed the strategic policies. The research results provide direct scientific and technological support for strategic decisions, such as investment in Russia, bilateral economic and trade cooperation, and overseas layout of Chinese-funded enterprises. Moreover, it has an important practical and strategic significance for improving overseas geo-strategic interests of China and ensuring the construction of China-Mongolia-Russia Economic Corridor.
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