2018 Vol. 28, No. 1
Display Method:
2018, 28(1): 1-11.
doi: 10.1007/s11769-018-0930-1
Abstract:
It is more difficult to retrieve land surface temperature (LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.
It is more difficult to retrieve land surface temperature (LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2 (AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies (ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.
2018, 28(1): 12-24.
doi: 10.1007/s11769-017-0899-1
Abstract:
Soil respiration (SR) is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO2 emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests (Populus tomentosa (PT) and Robinia pseudoacacia (RP)), three artificial plantations (Armeniaca sibirica (AS), Punica granatum (PG) and Ziziphus jujuba (ZJ)) and one natural grassland (GR), to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct seasonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation (CV) of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen (STN), soil organic carbon (SOC), net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q10 (temperature sensitivity of soil respiration) values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO2 emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying CO2 emissions via SR at regional scales.
Soil respiration (SR) is the second-largest flux in ecosystem carbon cycling. Due to the large spatio-temporal variability of environmental factors, SR varied among different vegetation types, thereby impeding accurate estimation of CO2 emissions via SR. However, studies on spatio-temporal variation of SR are still scarce for semi-arid regions of North China. In this study, we conducted 12-month SR measurements in six land-use types, including two secondary forests (Populus tomentosa (PT) and Robinia pseudoacacia (RP)), three artificial plantations (Armeniaca sibirica (AS), Punica granatum (PG) and Ziziphus jujuba (ZJ)) and one natural grassland (GR), to quantify spatio-temporal variation of SR and distinguish its controlling factors. Results indicated that SR exhibited distinct seasonal patterns for the six sites. Soil respiration peaked in August 2012 and bottomed in April 2013. The temporal coefficient of variation (CV) of SR for the six sites ranged from 76.98% to 94.08%, while the spatial CV of SR ranged from 20.28% to 72.97% across the 12-month measurement. Soil temperature and soil moisture were the major controlling factors of temporal variation of SR in the six sites, while spatial variation in SR was mainly caused by the differences in soil total nitrogen (STN), soil organic carbon (SOC), net photosynthesis rate, and fine root biomass. Our results show that the annual average SR and Q10 (temperature sensitivity of soil respiration) values tended to decrease from secondary forests and grassland to plantations, indicating that the conversion of natural ecosystems to man-made ecosystems may reduce CO2 emissions and SR temperature sensitivity. Due to the high spatio-temporal variation of SR in our study area, care should be taken when converting secondary forests and grassland to plantations from the point view of accurately quantifying CO2 emissions via SR at regional scales.
2018, 28(1): 25-37.
doi: 10.1007/s11769-017-0928-0
Abstract:
Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire activity in Mexico using georeferenced fire records for the period of 2005-2015 and examined its spatial and temporal relationships with a multiscalar drought index, the Standardized Precipitation-Evapotranspiration Index (SPEI). A total of 47 975 fire counts were recorded in the 11-year long study period, with the peak in fire frequency occurring in 2011. We identified four fire clusters, i.e., regions where there is a high density of fire records in Mexico using the Getis-Ord G spatial statistic. Then, we examined fire frequency data in the clustered regions and assessed how fire activity related to the SPEI for the entire study period and also for the year 2011. Associations between the SPEI and fire frequency varied across Mexico and fire-SPEI relationships also varied across the months of major fire occurrence and related SPEI temporal scales. In particular, in the two fire clusters located in northern Mexico (Chihuahua, northern Baja California), drier conditions over the previous 5 months triggered fire occurrence. In contrast, we did not observe a significant relationship between drought severity and fire frequency in the central Mexico cluster, which exhibited the highest fire frequency. We also found moderate fire-drought associations in the cluster situated in the tropical southern Chiapas where agriculture activities are the main causes of forest fire occurrence. These results are useful for improving our understanding of the spatiotemporal patterns of fire occurrence as related to drought severity in megadiverse countries hosting many forest types as Mexico.
Understanding the spatiotemporal links between drought and forest fire occurrence is crucial for improving decision-making in fire management under current and future climatic conditions. We quantified forest fire activity in Mexico using georeferenced fire records for the period of 2005-2015 and examined its spatial and temporal relationships with a multiscalar drought index, the Standardized Precipitation-Evapotranspiration Index (SPEI). A total of 47 975 fire counts were recorded in the 11-year long study period, with the peak in fire frequency occurring in 2011. We identified four fire clusters, i.e., regions where there is a high density of fire records in Mexico using the Getis-Ord G spatial statistic. Then, we examined fire frequency data in the clustered regions and assessed how fire activity related to the SPEI for the entire study period and also for the year 2011. Associations between the SPEI and fire frequency varied across Mexico and fire-SPEI relationships also varied across the months of major fire occurrence and related SPEI temporal scales. In particular, in the two fire clusters located in northern Mexico (Chihuahua, northern Baja California), drier conditions over the previous 5 months triggered fire occurrence. In contrast, we did not observe a significant relationship between drought severity and fire frequency in the central Mexico cluster, which exhibited the highest fire frequency. We also found moderate fire-drought associations in the cluster situated in the tropical southern Chiapas where agriculture activities are the main causes of forest fire occurrence. These results are useful for improving our understanding of the spatiotemporal patterns of fire occurrence as related to drought severity in megadiverse countries hosting many forest types as Mexico.
2018, 28(1): 38-46.
doi: 10.1007/s11769-017-0929-z
Abstract:
The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35×105 km2/yr, twice as fast as the rate between1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32×107 km2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54×105 km2 annually over the past 11 years.
The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35×105 km2/yr, twice as fast as the rate between1979 and 2006, and slightly slower than from 2002 to 2011. Area with >90% sea-ice concentration decreased by 1.32×107 km2/yr, while locations with >50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54×105 km2 annually over the past 11 years.
2018, 28(1): 47-60.
doi: 10.1007/s11769-018-0931-0
Abstract:
Interactions between surface water and groundwater are dynamic and complex in large endorheic river watersheds in Northwest China due to the influence of both irrigation practices and the local terrain. These interactions interchange numerous times throughout the middle reaches, making streamflow simulation a challenge in endorheic river watersheds. In this study, we modified the linear-reservoir groundwater module in SWAT (Soil and Water Assessment Tools, a widely used hydrological model) with a new nonlinear relationship to better represent groundwater processes; we then applied the original SWAT and modified SWAT to the Heihe River Watershed, the second largest endorheic river watershed in Northwest China, to simulate streamflow. After calibrating both the original SWAT model and the modified SWAT model, we analyzed model performance during two periods:an irrigation period and a non-irrigation period. Our results show that the modified SWAT model with the nonlinear groundwater module performed significantly better during both the irrigation and non-irrigation periods. Moreover, after comparing different runoff components simulated by the two models, the results show that, after the implementation of the new nonlinear groundwater module in SWAT, proportions of runoff components changed-and the groundwater flow had significantly increased, dominating the discharge season. Therefore, SWAT coupled with the non-linear groundwater module repre-sents the complex hydrological process in the study area more realistically. Moreover, the results for various runoff components simulated by the modified SWAT models can be used to describe the hydrological characteristics of lowland areas. This indicates that the modified SWAT model is applicable to simulate complex hydrological process of arid endorheic rivers.
Interactions between surface water and groundwater are dynamic and complex in large endorheic river watersheds in Northwest China due to the influence of both irrigation practices and the local terrain. These interactions interchange numerous times throughout the middle reaches, making streamflow simulation a challenge in endorheic river watersheds. In this study, we modified the linear-reservoir groundwater module in SWAT (Soil and Water Assessment Tools, a widely used hydrological model) with a new nonlinear relationship to better represent groundwater processes; we then applied the original SWAT and modified SWAT to the Heihe River Watershed, the second largest endorheic river watershed in Northwest China, to simulate streamflow. After calibrating both the original SWAT model and the modified SWAT model, we analyzed model performance during two periods:an irrigation period and a non-irrigation period. Our results show that the modified SWAT model with the nonlinear groundwater module performed significantly better during both the irrigation and non-irrigation periods. Moreover, after comparing different runoff components simulated by the two models, the results show that, after the implementation of the new nonlinear groundwater module in SWAT, proportions of runoff components changed-and the groundwater flow had significantly increased, dominating the discharge season. Therefore, SWAT coupled with the non-linear groundwater module repre-sents the complex hydrological process in the study area more realistically. Moreover, the results for various runoff components simulated by the modified SWAT models can be used to describe the hydrological characteristics of lowland areas. This indicates that the modified SWAT model is applicable to simulate complex hydrological process of arid endorheic rivers.
2018, 28(1): 61-73.
doi: 10.1007/s11769-018-0932-z
Abstract:
The ground ice content in permafrost serves as one of the dominant properties of permafrost for the study of global climate change, ecology, hydrology and engineering construction in cold regions. This paper initially attempts to assess the ground ice volume in permafrost layers on the Qinghai-Tibet Plateau by considering landform types, the corresponding lithological composition, and the measured water content in various regions. An approximation demonstrating the existence of many similarities in lithological composition and water content within a unified landform was established during the calculations. Considerable knowledge of the case study area, here called the Source Area of the Yellow (Huanghe) River (SAYR) in the northeastern Qinghai-Tibet Plateau, has been accumulated related to permafrost and fresh water resources during the past 40 years. Considering the permafrost distribution, extent, spatial distribution of landform types, the ground ice volume at the depths of 3.0-10.0 m below the ground surface was estimated based on the data of 101 boreholes from field observations and geological surveys in different types of landforms in the permafrost region of the SAYR. The total ground ice volume in permafrost layers at the depths of 3.0-10.0 m was approximately (51.68 ±18.81) km3, and the ground ice volume per unit volume was (0.31 ±0.11) m3/m3. In the horizontal direction, the ground ice content was higher in the landforms of lacustrine-marshland plains and alluvial-lacustrine plains, and the lower ground ice content was distributed in the erosional platforms and alluvial-proluvial plains. In the vertical direction, the volume of ground ice was relatively high in the top layers (especially near the permafrost table) and at the depths of 7.0-8.0 m. This calculation method will be used in the other areas when the necessary information is available, including landform type, borehole data, and measured water content.
The ground ice content in permafrost serves as one of the dominant properties of permafrost for the study of global climate change, ecology, hydrology and engineering construction in cold regions. This paper initially attempts to assess the ground ice volume in permafrost layers on the Qinghai-Tibet Plateau by considering landform types, the corresponding lithological composition, and the measured water content in various regions. An approximation demonstrating the existence of many similarities in lithological composition and water content within a unified landform was established during the calculations. Considerable knowledge of the case study area, here called the Source Area of the Yellow (Huanghe) River (SAYR) in the northeastern Qinghai-Tibet Plateau, has been accumulated related to permafrost and fresh water resources during the past 40 years. Considering the permafrost distribution, extent, spatial distribution of landform types, the ground ice volume at the depths of 3.0-10.0 m below the ground surface was estimated based on the data of 101 boreholes from field observations and geological surveys in different types of landforms in the permafrost region of the SAYR. The total ground ice volume in permafrost layers at the depths of 3.0-10.0 m was approximately (51.68 ±18.81) km3, and the ground ice volume per unit volume was (0.31 ±0.11) m3/m3. In the horizontal direction, the ground ice content was higher in the landforms of lacustrine-marshland plains and alluvial-lacustrine plains, and the lower ground ice content was distributed in the erosional platforms and alluvial-proluvial plains. In the vertical direction, the volume of ground ice was relatively high in the top layers (especially near the permafrost table) and at the depths of 7.0-8.0 m. This calculation method will be used in the other areas when the necessary information is available, including landform type, borehole data, and measured water content.
2018, 28(1): 74-85.
doi: 10.1007/s11769-017-0917-3
Abstract:
The aim of this study was to better understand the mechanisms of regional climate variation in mountain ranges with contrasting aspects as mediated by changes in global climate. It may help predict trends of vegetation variations in native ecosystems in natural reserves. As measures of climate response, temperature and precipitation data from the north, east, and south-facing mountain ranges of Shennongjia Massif in the coldest and hottest months (January and July), different seasons (spring, summer, autumn, and winter) and each year were analyzed from a long-term dataset (1960 to 2003) to tested variations characteristics, temporal and spatial quantitative relationships of climates. The results showed that the average seasonal temperatures and precipitation in the north, east, and south aspects of the mountain ranges changed at different rates. The average seasonal temperatures change rate ranges in the north, east, and south-facing mountain ranges were from -0.0210℃/yr to 0.0143℃/yr, -0.0166℃/yr to 0.0311℃/yr, and -0.0290℃/yr to 0.0084℃/yr, respectively, and seasonal precipitation variation magnitude were from -1.4940 mm/yr to 0.6217 mm/yr, -1.6833 mm/yr to 2.6182 mm/yr, and -0.8567 mm/yr to 1.4077 mm/yr, respectively. The climates variation trend among the three mountain ranges were different in magnitude and direction, showing a complicated change of the climates in mountain ranges and some inconsistency with general trends in global climate change. The climate variations were significantly different and positively correlated cross mountain ranges, revealing that aspects significantly affected on climate variations and these variations resulted from a larger air circulation system, which were sensitive to global climate change. We conclude that location and terrain of aspect are the main factors affecting differences in climate variation among the mountain ranges with contrasting aspects.
The aim of this study was to better understand the mechanisms of regional climate variation in mountain ranges with contrasting aspects as mediated by changes in global climate. It may help predict trends of vegetation variations in native ecosystems in natural reserves. As measures of climate response, temperature and precipitation data from the north, east, and south-facing mountain ranges of Shennongjia Massif in the coldest and hottest months (January and July), different seasons (spring, summer, autumn, and winter) and each year were analyzed from a long-term dataset (1960 to 2003) to tested variations characteristics, temporal and spatial quantitative relationships of climates. The results showed that the average seasonal temperatures and precipitation in the north, east, and south aspects of the mountain ranges changed at different rates. The average seasonal temperatures change rate ranges in the north, east, and south-facing mountain ranges were from -0.0210℃/yr to 0.0143℃/yr, -0.0166℃/yr to 0.0311℃/yr, and -0.0290℃/yr to 0.0084℃/yr, respectively, and seasonal precipitation variation magnitude were from -1.4940 mm/yr to 0.6217 mm/yr, -1.6833 mm/yr to 2.6182 mm/yr, and -0.8567 mm/yr to 1.4077 mm/yr, respectively. The climates variation trend among the three mountain ranges were different in magnitude and direction, showing a complicated change of the climates in mountain ranges and some inconsistency with general trends in global climate change. The climate variations were significantly different and positively correlated cross mountain ranges, revealing that aspects significantly affected on climate variations and these variations resulted from a larger air circulation system, which were sensitive to global climate change. We conclude that location and terrain of aspect are the main factors affecting differences in climate variation among the mountain ranges with contrasting aspects.
2018, 28(1): 86-99.
doi: 10.1007/s11769-018-0933-y
Abstract:
Understanding the spatial interactions among multiple ecosystem services is crucial for ecosystem services management. Ecosystem services, including crop production, freshwater supply, aquatic production, net primary production, soil conservation, water conservation, flood regulation, forest recreation, were measured at 1-km grid scale covering the Taihu Lake Basin (TLB) of China. Our objective is to get a comprehensive understanding of the spatial distributions, trade-offs, synergies of multiple ecosystem services across the TLB. Our results found that:1) majority of ecosystem services were clustered in space and had a similar spatial distribution pattern with the geographical resource endowment. Most of the landscape contributed a high supply of no services, one or two, and a low supply of three to seven services. 2) There were high correlation between forest recreation and freshwater supply and regulating services. Aquatic production had low correlation with other services. 3) The changes of provisioning services led to trade-offs between regulating services and cultural services in the TLB, while synergies mainly occurred among the provisioning service. 4) The spatial relationships of multiple services are consistent at 1-km spatial scale, counties and provinces. This research could help integrate multiple ecosystem services across scales and serve as a reference for decision making.
Understanding the spatial interactions among multiple ecosystem services is crucial for ecosystem services management. Ecosystem services, including crop production, freshwater supply, aquatic production, net primary production, soil conservation, water conservation, flood regulation, forest recreation, were measured at 1-km grid scale covering the Taihu Lake Basin (TLB) of China. Our objective is to get a comprehensive understanding of the spatial distributions, trade-offs, synergies of multiple ecosystem services across the TLB. Our results found that:1) majority of ecosystem services were clustered in space and had a similar spatial distribution pattern with the geographical resource endowment. Most of the landscape contributed a high supply of no services, one or two, and a low supply of three to seven services. 2) There were high correlation between forest recreation and freshwater supply and regulating services. Aquatic production had low correlation with other services. 3) The changes of provisioning services led to trade-offs between regulating services and cultural services in the TLB, while synergies mainly occurred among the provisioning service. 4) The spatial relationships of multiple services are consistent at 1-km spatial scale, counties and provinces. This research could help integrate multiple ecosystem services across scales and serve as a reference for decision making.
2018, 28(1): 100-110.
doi: 10.1007/s11769-018-0934-x
Abstract:
Urban plants provide various ecosystem services and biodiversity for human well-being. It is necessary to examine the plant species and functional traits composition and the influencing factors. In this study, a field survey was conducted using the tessellation-randomized plot method to assess the plant species and functional traits variability in greenspaces across eight land use types (LUTs) in the built-up areas of Beijing, China. Results showed that the woody plants in the built-up areas of Beijing comprised 85 non-native species (57%), 21 pollen-allergenic species (14%), and 99 resistant species (67%). Residential areas, community parks and institutional areas had higher woody plant species richness than other LUTs. Native and extralimital native species were more widespread than exotic species. Proportions of species with resistances were low except for cold-and drought-resistance; consequently, a high intensity of management and maintenance is essential for survival of plants in this urban area. Caution should be exerted in selecting plant species with resistance to harsh conditions in different LUTs. Housing prices, distances from the urban center, years since the establishment of LUTs and greening rate were strongly correlated with the plant functional traits and species diversity. Urban forest managers should consider plant functional traits and LUT-specific strategies to maximize both forest and human health.
Urban plants provide various ecosystem services and biodiversity for human well-being. It is necessary to examine the plant species and functional traits composition and the influencing factors. In this study, a field survey was conducted using the tessellation-randomized plot method to assess the plant species and functional traits variability in greenspaces across eight land use types (LUTs) in the built-up areas of Beijing, China. Results showed that the woody plants in the built-up areas of Beijing comprised 85 non-native species (57%), 21 pollen-allergenic species (14%), and 99 resistant species (67%). Residential areas, community parks and institutional areas had higher woody plant species richness than other LUTs. Native and extralimital native species were more widespread than exotic species. Proportions of species with resistances were low except for cold-and drought-resistance; consequently, a high intensity of management and maintenance is essential for survival of plants in this urban area. Caution should be exerted in selecting plant species with resistance to harsh conditions in different LUTs. Housing prices, distances from the urban center, years since the establishment of LUTs and greening rate were strongly correlated with the plant functional traits and species diversity. Urban forest managers should consider plant functional traits and LUT-specific strategies to maximize both forest and human health.
2018, 28(1): 111-126.
doi: 10.1007/s11769-017-0912-8
Abstract:
This study focuses on China's coastal area and its marine economic development. Applying the information diffusion method, the study establishes a kernel density function and its decomposition using a marine economic per capita as the index of the model to depict the dynamic evolution law and the internal influential factors of the Chinese marine economy during 1996-2013. The relative development rate was introduced to analyze the spatial differences in the marine economy's development. In this way, space and time dimensions fully characterized the evolution of the Chinese marine economy. Additionally, the influence of growth and inequality in the process of its development can be analyzed. The study shows that the Chinese marine economy as a whole has been growing, and regional marine economic development is relatively coordinated. In addition, the marine economy began to develop even more rapidly after 2004. There are three factors affecting the dynamic evolution of China's marine economy:first, the most influential mean effect, followed by, second, the variance effect, and third, the least influential residual effect. The biggest influence on the dynamic evolution of the marine economy is the improvement of the development level of the marine economy in the coastal area. Meanwhile, due to the existence of inequality, provinces at higher development levels are more dispersed. Furthermore, the existence of the residual effect weakens the influence of the mean effect, and the influence on the dynamic evolution of the marine economy continuously increases. In the analysis of the influencing factors of the evolution and spatial difference of marine economic development, the level of opening to the outside world, the level of investment in fixed assets and the industrial structure have a positive role in promoting economic development. However, capital investment in scientific human research has a negative correlation with economic development, and does not pass the significant test. The difference in regional development levels and development speed is also very apparent; namely, the provinces with higher development levels generally displayed faster development speeds while those with lower development levels showed slower development speeds across the four periods analyzed.
This study focuses on China's coastal area and its marine economic development. Applying the information diffusion method, the study establishes a kernel density function and its decomposition using a marine economic per capita as the index of the model to depict the dynamic evolution law and the internal influential factors of the Chinese marine economy during 1996-2013. The relative development rate was introduced to analyze the spatial differences in the marine economy's development. In this way, space and time dimensions fully characterized the evolution of the Chinese marine economy. Additionally, the influence of growth and inequality in the process of its development can be analyzed. The study shows that the Chinese marine economy as a whole has been growing, and regional marine economic development is relatively coordinated. In addition, the marine economy began to develop even more rapidly after 2004. There are three factors affecting the dynamic evolution of China's marine economy:first, the most influential mean effect, followed by, second, the variance effect, and third, the least influential residual effect. The biggest influence on the dynamic evolution of the marine economy is the improvement of the development level of the marine economy in the coastal area. Meanwhile, due to the existence of inequality, provinces at higher development levels are more dispersed. Furthermore, the existence of the residual effect weakens the influence of the mean effect, and the influence on the dynamic evolution of the marine economy continuously increases. In the analysis of the influencing factors of the evolution and spatial difference of marine economic development, the level of opening to the outside world, the level of investment in fixed assets and the industrial structure have a positive role in promoting economic development. However, capital investment in scientific human research has a negative correlation with economic development, and does not pass the significant test. The difference in regional development levels and development speed is also very apparent; namely, the provinces with higher development levels generally displayed faster development speeds while those with lower development levels showed slower development speeds across the four periods analyzed.
2018, 28(1): 127-136.
doi: 10.1007/s11769-018-0935-9
Abstract:
To meet the challenge of sustainable development, sustainability must be made. Ecological network analysis (ENA) was introduced in this paper as an approach to quantitatively measure the growth, development, and sustainability of an economic system. The Guangdong economic networks from 1987 to 2010 were analyzed by applying the ENA approach. Firstly, a currency flow network among economic sectors was constructed to represent the Guangdong economic system by adapting the input-output (I-O) table data. Then, the network indicators from the ENA framework involving the total system throughput (TST), average mutual information (AMI), ascendency (A), redundancy (R) and development capacity (C) were calculated. Lastly, the network indicators were analyzed to acquire the overall features of Guangdong's economic operations during 1987-2010. The results are as follows:the trends of the network indicators show that the size of the Guangdong economic network grows exponentially at a high rate during 1987-2010, whereas its efficiency does not present a clear trend over its whole period. The growth is the main characteristic of the Guangdong economy during 1987-2010, with no clear evidence regarding its development. The quantitative results of the network also confirmed that the growth contributed to a great majority of the Guangdong economy during1987-2010, whereas the development's contribution was tiny during the same period. The average value of the sustainability indicator (α) of the Guangdong economic network was 0.222 during 1987-2010, which is less than the theoretically optimal value of 0.37 for a sustainable human-influenced system. The results suggest that the Guangdong economic system needs a further autocatalysis to improve its efficiency to support the system maintaining a sustainable evolvement.
To meet the challenge of sustainable development, sustainability must be made. Ecological network analysis (ENA) was introduced in this paper as an approach to quantitatively measure the growth, development, and sustainability of an economic system. The Guangdong economic networks from 1987 to 2010 were analyzed by applying the ENA approach. Firstly, a currency flow network among economic sectors was constructed to represent the Guangdong economic system by adapting the input-output (I-O) table data. Then, the network indicators from the ENA framework involving the total system throughput (TST), average mutual information (AMI), ascendency (A), redundancy (R) and development capacity (C) were calculated. Lastly, the network indicators were analyzed to acquire the overall features of Guangdong's economic operations during 1987-2010. The results are as follows:the trends of the network indicators show that the size of the Guangdong economic network grows exponentially at a high rate during 1987-2010, whereas its efficiency does not present a clear trend over its whole period. The growth is the main characteristic of the Guangdong economy during 1987-2010, with no clear evidence regarding its development. The quantitative results of the network also confirmed that the growth contributed to a great majority of the Guangdong economy during1987-2010, whereas the development's contribution was tiny during the same period. The average value of the sustainability indicator (α) of the Guangdong economic network was 0.222 during 1987-2010, which is less than the theoretically optimal value of 0.37 for a sustainable human-influenced system. The results suggest that the Guangdong economic system needs a further autocatalysis to improve its efficiency to support the system maintaining a sustainable evolvement.
2018, 28(1): 137-152.
doi: 10.1007/s11769-017-0924-4
Abstract:
Under the special background of China, the cooperative innovation between different government-industry-university-research institutes plays an increasingly important role in the agricultural field. However, the existing literature has paid little attention to it. Considering the cooperation patents, published in the agriculture field stemming from the Full-text Database of China Patents as the study object, the spatial and institutional attribute of the authors as the data source, and by combining the social network and spatial econometrics analysis, this paper analyzes the structure evolution characteristics of cooperative innovation networks of agricultural government-industry-university-research institute in the city level of China in 1985-2014, based on the triple helix theory, with the in-fluence factors discussed. This shows that, 1) since 1985, China's agricultural innovation level has been substantially increased, but the development degree of the cooperative innovation network is low, and the patent cooperation mainly relies on authors in the same unit; 2) enterprises play a leading role in the agricultural cooperative innovation. The effect of the government and hybrid organizations driven by the government is not obvious; 3) the cooperative innovation in the province and city dominates, and a multi-pole pattern has been formed. The cooperative innovation network structure evolves from a single helix empty core and double helix multi core to a double helix hierarchical network; 4) the city's science, education funding and personnel investment are key factors determining the agricultural cooperative innovation, while the agricultural development of the city presents slight negative impacts on it. The spatial mismatch of supply and demand is present in the technical cooperative innovation of China's agriculture. Therefore, the science enhancement and education investment to big agricultural provinces should be promptly implemented.
Under the special background of China, the cooperative innovation between different government-industry-university-research institutes plays an increasingly important role in the agricultural field. However, the existing literature has paid little attention to it. Considering the cooperation patents, published in the agriculture field stemming from the Full-text Database of China Patents as the study object, the spatial and institutional attribute of the authors as the data source, and by combining the social network and spatial econometrics analysis, this paper analyzes the structure evolution characteristics of cooperative innovation networks of agricultural government-industry-university-research institute in the city level of China in 1985-2014, based on the triple helix theory, with the in-fluence factors discussed. This shows that, 1) since 1985, China's agricultural innovation level has been substantially increased, but the development degree of the cooperative innovation network is low, and the patent cooperation mainly relies on authors in the same unit; 2) enterprises play a leading role in the agricultural cooperative innovation. The effect of the government and hybrid organizations driven by the government is not obvious; 3) the cooperative innovation in the province and city dominates, and a multi-pole pattern has been formed. The cooperative innovation network structure evolves from a single helix empty core and double helix multi core to a double helix hierarchical network; 4) the city's science, education funding and personnel investment are key factors determining the agricultural cooperative innovation, while the agricultural development of the city presents slight negative impacts on it. The spatial mismatch of supply and demand is present in the technical cooperative innovation of China's agriculture. Therefore, the science enhancement and education investment to big agricultural provinces should be promptly implemented.
2018, 28(1): 153-166.
doi: 10.1007/s11769-018-0936-8
Abstract:
Labor migration to urban centers is a common phenomenon in the Panxi region of the southwestern mountainous region of China, mainly owing to inadequate livelihood capital in rural areas. Numerous studies have been conducted to explore the relationship between labor migration and its causes, such as individual and family characteristics, but few studies have focused on livelihood capital. This paper examines the impact factors on labor migration employment location selection and duration from a household livelihood capital perspective. A case study of 279 households from 10 villages in the area was carried out in February 2016. We used both qualitative and quantitative methods to analyze the data. On the basis of the 279 questionnaires, the proportion of households with non-labor migration is 48.4%, whereas households with labor migration within a local city and migration across regions account for 28.7% and 22.9%, respectively. Social, financial, and human capitals are the primary factors that influence migrants' employment location choice positively. Among them, social capital has a significant impact on both migration within a local city and across regions; each of the regression coefficients is 1.111 and 1.183. Social, human, and financial capitals also have a positive impact on the duration of labor migration, and similarly, social capital is the highest coefficient with 2.489. However, physical capital only partly impacts labor migration across regions, whereas the impact of labor migration within a local city, and the duration, are not significant. Furthermore, the impact of household natural capital on migration space and time are all negative relationships, especially for labor migration across the regions and duration, with coefficient scores of 4.836 and 3.450, respectively. That is to say, a laborer is inclined to migrate within a local city for a short term, or not migrate at all, if natural cap-ital is abundant. Our analysis results show that household livelihood capital has a strong spatio-temporal impact on labor migration.
Labor migration to urban centers is a common phenomenon in the Panxi region of the southwestern mountainous region of China, mainly owing to inadequate livelihood capital in rural areas. Numerous studies have been conducted to explore the relationship between labor migration and its causes, such as individual and family characteristics, but few studies have focused on livelihood capital. This paper examines the impact factors on labor migration employment location selection and duration from a household livelihood capital perspective. A case study of 279 households from 10 villages in the area was carried out in February 2016. We used both qualitative and quantitative methods to analyze the data. On the basis of the 279 questionnaires, the proportion of households with non-labor migration is 48.4%, whereas households with labor migration within a local city and migration across regions account for 28.7% and 22.9%, respectively. Social, financial, and human capitals are the primary factors that influence migrants' employment location choice positively. Among them, social capital has a significant impact on both migration within a local city and across regions; each of the regression coefficients is 1.111 and 1.183. Social, human, and financial capitals also have a positive impact on the duration of labor migration, and similarly, social capital is the highest coefficient with 2.489. However, physical capital only partly impacts labor migration across regions, whereas the impact of labor migration within a local city, and the duration, are not significant. Furthermore, the impact of household natural capital on migration space and time are all negative relationships, especially for labor migration across the regions and duration, with coefficient scores of 4.836 and 3.450, respectively. That is to say, a laborer is inclined to migrate within a local city for a short term, or not migrate at all, if natural cap-ital is abundant. Our analysis results show that household livelihood capital has a strong spatio-temporal impact on labor migration.
2018, 28(1): 167-182.
doi: 10.1007/s11769-017-0915-5
Abstract:
The protection of historic sites, especially their relationship with urban development, has become a worldwide issue, both in developed and developing countries. In the context of rapid urbanization in China, the realistic compatibility between urban construction and the protection of historic sites is always a key research topic. In this study, first, to comprehend their spatial distribution patterns, 828 historic sites throughout the country are selected based on certain criteria. Then, we conduct quantitative research using GIS software, adopting indicators that include Nearest Neighbor Index, Gini Coefficient, and Geographic Concentration Index to analyze the spatial characteristics of historic sites on the three levels of city, province and nation. The results indicate that the spatial distribution of the different types of historic sites is an agglomeration on the nationwide scale, most of which is located in the regions of the Pearl (Zhujiang) River Delta, Yangtze (Changjiang) River Delta and Beijing-Tianjin Region. Because the majority of historic sites are located within approximately 10 km of the downtown area, a certain pattern has emerged, showing that the larger cities own more historic areas, which are in a more incomplete state of preservation, indicating the fragmentation of heritage spaces. The formation mechanism of the historic sites' distribution pattern is based on the conditions of the cities/towns as well as the bid-rent theory.
The protection of historic sites, especially their relationship with urban development, has become a worldwide issue, both in developed and developing countries. In the context of rapid urbanization in China, the realistic compatibility between urban construction and the protection of historic sites is always a key research topic. In this study, first, to comprehend their spatial distribution patterns, 828 historic sites throughout the country are selected based on certain criteria. Then, we conduct quantitative research using GIS software, adopting indicators that include Nearest Neighbor Index, Gini Coefficient, and Geographic Concentration Index to analyze the spatial characteristics of historic sites on the three levels of city, province and nation. The results indicate that the spatial distribution of the different types of historic sites is an agglomeration on the nationwide scale, most of which is located in the regions of the Pearl (Zhujiang) River Delta, Yangtze (Changjiang) River Delta and Beijing-Tianjin Region. Because the majority of historic sites are located within approximately 10 km of the downtown area, a certain pattern has emerged, showing that the larger cities own more historic areas, which are in a more incomplete state of preservation, indicating the fragmentation of heritage spaces. The formation mechanism of the historic sites' distribution pattern is based on the conditions of the cities/towns as well as the bid-rent theory.
Email
RSS