2017 Vol. 27, No. 5
Display Method:
2017, 27(5): 673-683.
doi: 10.1007/s11769-017-0901-y
Abstract:
Based on the interprovincial panel data of 2000-2014, this paper carries out an empirical analysis on the relationship between energy abundance and economic growth to test the theoretical hypothesis of ‘resource curse’ and explore its transmission mechanism for China and its three regions. The results show that, at the national level, positive correlation is present between energy abundance and economic growth, proving that the ‘resource curse’ phenomenon does not exist in China as a whole. Moreover, material capital input, human capital input and the level of opening to the outside world could promote economic growth, while technology innovation input may hinder economic growth. As seen by region, a positive correlation also exists between the energy abundance and economic growth in the eastern and western regions, and there is no ‘resource curse’ phenomenon either. In all three regions, the human capital input could promote economic growth. Material capital input could promote economic growth in the eastern but hinder economic growth in the western region; the level of opening to the outside world could promote economic growth in the eastern region. It is known through further survey and analysis on the transmission mechanism of resource curse that, at the national level, material capital input, human capital input, and the level of opening to the outside world present positive correlation with energy abundance, indicating that energy development becomes an important transmission factor by strengthening material capital input and human capital input and raising the level of opening to the outside world. However, technology innovation input presents negative correlation with energy development. As seen by region, both the material capital input and human capital input present positive correlation with energy development strength in the three regions. Similar as the eastern region, the level of opening to the outside world presents positive correlation with energy industry development in the middle and western regions; however, the energy development presents negative correlation with technology input level in the western region.
Based on the interprovincial panel data of 2000-2014, this paper carries out an empirical analysis on the relationship between energy abundance and economic growth to test the theoretical hypothesis of ‘resource curse’ and explore its transmission mechanism for China and its three regions. The results show that, at the national level, positive correlation is present between energy abundance and economic growth, proving that the ‘resource curse’ phenomenon does not exist in China as a whole. Moreover, material capital input, human capital input and the level of opening to the outside world could promote economic growth, while technology innovation input may hinder economic growth. As seen by region, a positive correlation also exists between the energy abundance and economic growth in the eastern and western regions, and there is no ‘resource curse’ phenomenon either. In all three regions, the human capital input could promote economic growth. Material capital input could promote economic growth in the eastern but hinder economic growth in the western region; the level of opening to the outside world could promote economic growth in the eastern region. It is known through further survey and analysis on the transmission mechanism of resource curse that, at the national level, material capital input, human capital input, and the level of opening to the outside world present positive correlation with energy abundance, indicating that energy development becomes an important transmission factor by strengthening material capital input and human capital input and raising the level of opening to the outside world. However, technology innovation input presents negative correlation with energy development. As seen by region, both the material capital input and human capital input present positive correlation with energy development strength in the three regions. Similar as the eastern region, the level of opening to the outside world presents positive correlation with energy industry development in the middle and western regions; however, the energy development presents negative correlation with technology input level in the western region.
2017, 27(5): 684-696.
doi: 10.1007/s11769-017-0902-x
Abstract:
Using data from the Economic Advisory Center of the State Information Center (SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers (foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.
Using data from the Economic Advisory Center of the State Information Center (SIC), we examined the spatial patterns of car sales in China at the prefectural level in 2012. We first analyzed the spatial distributions of car sales of different kinds of automakers (foreign automakers, Sino-foreign joint automakers, and Chinese automakers), and then identified spatial clusters using the local Moran's indexes. Location quotient analysis was applied to examine the relative advantage of each type of automaker in the local markets. To explain the variations of car sales across cities, we collected several socioeconomic variables and conducted regression analyses. Further, factor analysis was used to extract independent variables to avoid the problem of multicollinearity. By incorporating a spatial lag or spatial error in the models, we calibrated our spatial regression models to address the spatial dependence problem. The analytical results show that car sales varied significantly across cities in China, and most of the cities with higher car sales were the developed cities. Different automakers exhibit diverse spatial patterns in terms of car sales volume, spatial clusters, and location quotients. The scale and incomes factor were extracted and verified as the two most significant and positive factors that shape the spatial distributions of car sales, and together with the spatial effect, explained most of the variations of car sales across cities.
2017, 27(5): 697-708.
doi: 10.1007/s11769-017-0903-9
Abstract:
The economies of China-Japan-Korea (CJK) are complementary, with their proximity resulting in the three countries having a high degree of interdependence with respect to trade. Currently, trade among these countries relies mainly on port-centered shipping. The development of the shipping network is integral for in-depth integration of CJK trade. This paper analyzes the overall characteristics, centrality, spatial structure, and vulnerability of the CJK shipping network using the methods of complex network analysis, blocking flow theory, and interruption and deletion of hub ports. The main findings are as follows:1) The CJK shipping network has a small average path length and clustering coefficient, and its degree distribution follows a power-law distribution, which make the network present obvious characteristics of a Barabási-Albert scale-free. 2) The characteristics of the multi-center point of the CJK shipping network can alleviate traffic pressure. At the same time, the network shows a clear hierarchy in the port transportation system, with cargo transport relying mainly on the ‘hub port-hub port’ connection. 3) The CJK shipping network is relatively stable. Compared with ports in Japan and Korea, the main hub ports in China have a greater impact on the stability of the shipping network, in particular those ports of the central coastal region, including Shanghai, Ningbo, and Lianyungang.
The economies of China-Japan-Korea (CJK) are complementary, with their proximity resulting in the three countries having a high degree of interdependence with respect to trade. Currently, trade among these countries relies mainly on port-centered shipping. The development of the shipping network is integral for in-depth integration of CJK trade. This paper analyzes the overall characteristics, centrality, spatial structure, and vulnerability of the CJK shipping network using the methods of complex network analysis, blocking flow theory, and interruption and deletion of hub ports. The main findings are as follows:1) The CJK shipping network has a small average path length and clustering coefficient, and its degree distribution follows a power-law distribution, which make the network present obvious characteristics of a Barabási-Albert scale-free. 2) The characteristics of the multi-center point of the CJK shipping network can alleviate traffic pressure. At the same time, the network shows a clear hierarchy in the port transportation system, with cargo transport relying mainly on the ‘hub port-hub port’ connection. 3) The CJK shipping network is relatively stable. Compared with ports in Japan and Korea, the main hub ports in China have a greater impact on the stability of the shipping network, in particular those ports of the central coastal region, including Shanghai, Ningbo, and Lianyungang.
2017, 27(5): 709-721.
doi: 10.1007/s11769-017-0881-y
Abstract:
The coordinated development of Beijing, Tianjin and Hebei has been elevated as China's important strategy. And, the priority in considering how to bring the maximum effect of their coordinated development into play is to delineate the spheres of urban influence with regard to the cities in the Beijing-Tianjin-Hebei Metropolitan Region. By building an evaluation index system of urban comprehensive strength, this paper applies the principal component analysis method to determine centrality strength of the cities, and the breakpoint theory and weighted Voronoi diagram to identify the spheres of urban influence in all central cities of the region. Results show that 13 central cities within the region greatly differ in strength, which can be classified into four tiers and that the spheres of urban influence do not have a high goodness of fit with administrative jurisdiction scope. Cities like Beijing, Tianjin, Shijiazhuang and Handan have larger spheres of urban, spheres of urban influence in Tangshan and Qinhuangdao are basically consistent with their administrative jurisdiction scopes, and seven cities including Langfang and Baoding have smaller spheres of urban influence. So according to these cities' comprehensive strength and spheres of influence, the region can be divided into five plates:Beijing, Tianjin, Shijiazhuang, Tangshan and Handan. The major influence factors for inconsistency between spheres of urban influence and spheres of jurisdiction include difference in urban administrative ranking, small number of central cities with weak strength, discrepancy in the number of counties under jurisdiction, unreasonable spheres of jurisdiction and diversity in topographical conditions. In order to solve the imbalance in the spheres of urban influence and those of jurisdiction and better facilitate the coordinated development of the region, it is advised to adjust administrative areas so as to obtain more optimized urban spatial layout and more reasonable urban scale hierarchy system.
The coordinated development of Beijing, Tianjin and Hebei has been elevated as China's important strategy. And, the priority in considering how to bring the maximum effect of their coordinated development into play is to delineate the spheres of urban influence with regard to the cities in the Beijing-Tianjin-Hebei Metropolitan Region. By building an evaluation index system of urban comprehensive strength, this paper applies the principal component analysis method to determine centrality strength of the cities, and the breakpoint theory and weighted Voronoi diagram to identify the spheres of urban influence in all central cities of the region. Results show that 13 central cities within the region greatly differ in strength, which can be classified into four tiers and that the spheres of urban influence do not have a high goodness of fit with administrative jurisdiction scope. Cities like Beijing, Tianjin, Shijiazhuang and Handan have larger spheres of urban, spheres of urban influence in Tangshan and Qinhuangdao are basically consistent with their administrative jurisdiction scopes, and seven cities including Langfang and Baoding have smaller spheres of urban influence. So according to these cities' comprehensive strength and spheres of influence, the region can be divided into five plates:Beijing, Tianjin, Shijiazhuang, Tangshan and Handan. The major influence factors for inconsistency between spheres of urban influence and spheres of jurisdiction include difference in urban administrative ranking, small number of central cities with weak strength, discrepancy in the number of counties under jurisdiction, unreasonable spheres of jurisdiction and diversity in topographical conditions. In order to solve the imbalance in the spheres of urban influence and those of jurisdiction and better facilitate the coordinated development of the region, it is advised to adjust administrative areas so as to obtain more optimized urban spatial layout and more reasonable urban scale hierarchy system.
2017, 27(5): 722-734.
doi: 10.1007/s11769-017-0904-8
Abstract:
Promoting active travel behavior and decreasing transport-related carbon dioxide (CO2) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO2 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.
Promoting active travel behavior and decreasing transport-related carbon dioxide (CO2) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO2 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO2 emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.
2017, 27(5): 735-746.
doi: 10.1007/s11769-017-0905-7
Abstract:
In this paper, the artificial neural network (ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.
In this paper, the artificial neural network (ANN) model was used to evaluate the degree of intensive urban land use in Nanjing City, China. The construction and application of the ANN model took into account the comprehensive, spatial and complex nature of urban land use. Through a preliminary calculation of the degree of intensive land use of the sample area, representative sample area selection and using the back propagation neural network model to train, the intensive land use level of each evaluation unit is finally determined in the study area. Results show that the method can effectively correct the errors caused by the limitations of the model itself and the determination of the ideal value and weights when the multifactor comprehensive evaluation is used alone. The ANN model can make the evaluation results more objective and practical. The evaluation results show a tendency of decreasing land use intensity from the core urban area to the periphery and the industrial functional area has relatively low land use intensity compared with other functional areas. Based on the evaluation results, some suggestions are put forward, such as transforming the mode of urban spatial expansion, strengthening the integration and potential exploitation of the land in the urban built-up area, and strengthening the control of the construction intensity of protected areas.
2017, 27(5): 747-759.
doi: 10.1007/s11769-017-0906-6
Abstract:
Soil macronutrients (i.e. nitrogen (N), phosphorus (P), and potassium (K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks (ANN) and two geostatistical methods (geographically weighted regression (GWR) and cokriging (CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil (0-30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration (n=84) and validation (n=22). Chemical and physical variables including clay, pH and organic carbon (OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model (with coefficient of determination R2=0.922 and root mean square error RMSE=0.0079%) was more accurate compared to the CK model (with R2=0.612 and RMSE=0.0094%), and the GWR model (with R2=0.872 and RMSE=0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients (N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.
Soil macronutrients (i.e. nitrogen (N), phosphorus (P), and potassium (K)) are important soils components and knowing the spatial distribution of these parameters are necessary at precision agriculture. The purpose of this study was to evaluate the feasibility of different methods such as artificial neural networks (ANN) and two geostatistical methods (geographically weighted regression (GWR) and cokriging (CK)) to estimate N, P and K contents. For this purpose, soil samples were taken from topsoil (0-30 cm) at 106 points and analyzed for their chemical and physical parameters. These data were divided into calibration (n=84) and validation (n=22). Chemical and physical variables including clay, pH and organic carbon (OC) were used as auxiliary soil variables to estimate the N, P and K contents. Results showed that the ANN model (with coefficient of determination R2=0.922 and root mean square error RMSE=0.0079%) was more accurate compared to the CK model (with R2=0.612 and RMSE=0.0094%), and the GWR model (with R2=0.872 and RMSE=0.0089%) to estimate the N variable. The ANN model estimated the P with the RMSE of 3.630 ppm, which was respectively 28.93% and 20.00% less than the RMSE of 4.680 ppm and 4.357 ppm from the CK and GWR models. The estimated K by CK, GWR and ANN models have the RMSE of 76.794 ppm, 75.790 ppm and 52.484 ppm. Results indicated that the performance of the CK model for estimation of macro nutrients (N, P and K) was slightly lower than the GWR model. Also, the accuracy of the ANN model was higher than CK and GWR models, which proved to be more effective and reliable methods for estimating macro nutrients.
2017, 27(5): 760-771.
doi: 10.1007/s11769-017-0898-2
Abstract:
The decomposition of plant litter is a key process in the flows of energy and nutrients in ecosystems. However, the response of litter decomposition to global climate warming in plateau wetlands remains largely unknown. In this study, we conducted a one-year litter decomposition experiment along an elevation gradient from 1891 m to 3260 m on the Yunnan Plateau of Southwest China, using different litter types to determine the influences of climate change, litter quality and microenvironment on the decomposition rate. The results showed that the average decomposition rate (K) increased from 0.608 to 1.152, and the temperature sensitivity of litter mass losses was approximately 4.98%/℃ along the declining elevation gradient. Based on a correlation analysis, N concentrations and C︰N ratios in the litter were the best predictors of the decomposition rate, with significantly positive and negative correlations, respectively. Additionally, the cumulative effects of decomposition were clearly observed in the mixtures of Scirpus tabernaemontani and Zizania caduciflora. Moreover, the litter decomposition rate in the water was higher than that in the sediment, especially in high-elevation areas where the microenvironment was significantly affected by temperature. These results suggest that future climate warming will have significant impacts on plateau wetlands, which have important functions in biogeochemical cycling in cold highland ecosystems.
The decomposition of plant litter is a key process in the flows of energy and nutrients in ecosystems. However, the response of litter decomposition to global climate warming in plateau wetlands remains largely unknown. In this study, we conducted a one-year litter decomposition experiment along an elevation gradient from 1891 m to 3260 m on the Yunnan Plateau of Southwest China, using different litter types to determine the influences of climate change, litter quality and microenvironment on the decomposition rate. The results showed that the average decomposition rate (K) increased from 0.608 to 1.152, and the temperature sensitivity of litter mass losses was approximately 4.98%/℃ along the declining elevation gradient. Based on a correlation analysis, N concentrations and C︰N ratios in the litter were the best predictors of the decomposition rate, with significantly positive and negative correlations, respectively. Additionally, the cumulative effects of decomposition were clearly observed in the mixtures of Scirpus tabernaemontani and Zizania caduciflora. Moreover, the litter decomposition rate in the water was higher than that in the sediment, especially in high-elevation areas where the microenvironment was significantly affected by temperature. These results suggest that future climate warming will have significant impacts on plateau wetlands, which have important functions in biogeochemical cycling in cold highland ecosystems.
2017, 27(5): 772-783.
doi: 10.1007/s11769-017-0907-5
Abstract:
The agro-pastoral ecotone has been recognized as the main distribution area of reserved land resource for cultivation. Accordingly, clarifying this assumption, as well as concerting land reclamation and ecological restoration, is important to ensure food security and environmental improvement in the agro-pastoral ecotone. We selected Horqin Left Back Banner (HLBB) as the subject of our case study. The landscape ecological security pattern of this area was determined using the minimum cumulative resistance model. Over-cultivation, quantity of reserved land resource for cultivation, and changes in landscape indexes before and after land use adjustment were then analyzed. Over-cultivation is a serious problem in the agro-pastoral ecotone. Reserved land resource for cultivation is less than that considered previously, and the area of reserved land resource for cultivation in HLBB only accounts for 11.50% of total uncultivated land. With regard to changes in landscape indexes, the adjusted land use pattern is effective for anti-desertification. The compensation standard for abandoned cultivated land should be improved and the comprehensive results of ‘Grain for Green’ should be evaluated to further implement ecological restoration in the agro-pastoral ecotone.
The agro-pastoral ecotone has been recognized as the main distribution area of reserved land resource for cultivation. Accordingly, clarifying this assumption, as well as concerting land reclamation and ecological restoration, is important to ensure food security and environmental improvement in the agro-pastoral ecotone. We selected Horqin Left Back Banner (HLBB) as the subject of our case study. The landscape ecological security pattern of this area was determined using the minimum cumulative resistance model. Over-cultivation, quantity of reserved land resource for cultivation, and changes in landscape indexes before and after land use adjustment were then analyzed. Over-cultivation is a serious problem in the agro-pastoral ecotone. Reserved land resource for cultivation is less than that considered previously, and the area of reserved land resource for cultivation in HLBB only accounts for 11.50% of total uncultivated land. With regard to changes in landscape indexes, the adjusted land use pattern is effective for anti-desertification. The compensation standard for abandoned cultivated land should be improved and the comprehensive results of ‘Grain for Green’ should be evaluated to further implement ecological restoration in the agro-pastoral ecotone.
2017, 27(5): 784-799.
doi: 10.1007/s11769-017-0897-3
Abstract:
Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoigê Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoigê Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index (NDVI) in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index (NDWI) of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 km2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.
Accurate wetland delineation is the basis of wetland definition and mapping, and is of great importance for wetland management and research. The Zoigê Plateau on the Qinghai-Tibet Plateau was used as a research site for research on alpine wetland delineation. Several studies have analyzed the spatiotemporal pattern and dynamics of these alpine wetlands, but none have addressed the issues of wetland boundaries. The objective of this work was to discriminate the upper boundaries of alpine wetlands by coupling ecological methods and satellite observations. The combination of Landsat 8 images and supervised classification was an effective method for rapid identification of alpine wetlands in the Zoigê Plateau. Wet meadow was relatively stable compared with hydric soils and wetland hydrology and could be used as a primary indicator for discriminating the upper boundaries of alpine wetlands. A slope of less than 4.5° could be used as the threshold value for wetland delineation. The normalized difference vegetation index (NDVI) in 434 field sites showed that a threshold value of 0.3 could distinguish grasslands from emergent marsh and wet meadow in September. The median normalized difference water index (NDWI) of emergent marsh remained more stable than that of wet meadow and grasslands during the period from September until July of the following year. The index of mean density in wet meadow zones was higher than the emergent and upland zones. Over twice the number of species occurred in the wet meadow zone compared with the emergent zone, and close to the value of upland zone. Alpine wetlands in the three reserves in 2014 covered 1175.19 km2 with a classification accuracy of 75.6%. The combination of ecological methods and remote sensing technology will play an important role in wetland delineation at medium and small scales. The correct differentiation between wet meadow and grasslands is the key to improving the accuracy of future wetland delineation.
2017, 27(5): 800-809.
doi: 10.1007/s11769-017-0908-4
Abstract:
Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product (collection 6_L2) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra (MOD06) and Aqua (MYD06) satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 (12 years) with a spatial resolution of 5 km×5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.
Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product (collection 6_L2) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on board the Terra (MOD06) and Aqua (MYD06) satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 (12 years) with a spatial resolution of 5 km×5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.
2017, 27(5): 810-817.
doi: 10.1007/s11769-017-0909-3
Abstract:
Rising sea levels threaten the sustainability of coastal wetlands around the globe. The ability of coastal marshes to maintain their position in the intertidal zone depends on the accumulation of both organic and inorganic materials, and vegetation is important in these processes. To study the effects of vegetation type on surface elevation change, we measured surface accretion and elevation change from 2011 to 2016 using rod surface elevation table and feldspar marker horizon method (RSET-MH) in two Phragmites and two Suaeda marshes in the Liaohe River Delta. The Phragmites marshes exhibited higher rates of surface accretion and elevation change than the Suaeda marshes. The two Phragmites marsh sites had average surface elevation change rates at 8.78 mm/yr and 9.26 mm/yr and surface accretion rates at 17.56 mm/yr and 17.88 mm/yr, respectively. At the same time, the two Suaeda marsh sites had average surface elevation change rates at 5.77 mm/yr and 5.91 mm/yr and surface accretion rates at 13.42 mm/yr and 14.38 mm/yr, respectively. The elevation change rates in both the Phragmites marshes and the Suaeda marshes in the Liaohe River Delta could keep pace and even continue to gain elevation relative to averaged sea level rise in the Bohai Sea reported by the 2016 State Oceanic Administration, People's Republic of China projection (2.4-5.5 mm/yr) in current situations. Our data suggest that vegetation is important in the accretionary processes and vegetation type could regulate the wetland surface elevation. However, the vulnerability of coastal wetlands in the Liaohe River Delta need further assessment considering the accelerated sea level rise, the high rate of subsidence, and the declining sediment delivery, especially for the Suaeda marshes.
Rising sea levels threaten the sustainability of coastal wetlands around the globe. The ability of coastal marshes to maintain their position in the intertidal zone depends on the accumulation of both organic and inorganic materials, and vegetation is important in these processes. To study the effects of vegetation type on surface elevation change, we measured surface accretion and elevation change from 2011 to 2016 using rod surface elevation table and feldspar marker horizon method (RSET-MH) in two Phragmites and two Suaeda marshes in the Liaohe River Delta. The Phragmites marshes exhibited higher rates of surface accretion and elevation change than the Suaeda marshes. The two Phragmites marsh sites had average surface elevation change rates at 8.78 mm/yr and 9.26 mm/yr and surface accretion rates at 17.56 mm/yr and 17.88 mm/yr, respectively. At the same time, the two Suaeda marsh sites had average surface elevation change rates at 5.77 mm/yr and 5.91 mm/yr and surface accretion rates at 13.42 mm/yr and 14.38 mm/yr, respectively. The elevation change rates in both the Phragmites marshes and the Suaeda marshes in the Liaohe River Delta could keep pace and even continue to gain elevation relative to averaged sea level rise in the Bohai Sea reported by the 2016 State Oceanic Administration, People's Republic of China projection (2.4-5.5 mm/yr) in current situations. Our data suggest that vegetation is important in the accretionary processes and vegetation type could regulate the wetland surface elevation. However, the vulnerability of coastal wetlands in the Liaohe River Delta need further assessment considering the accelerated sea level rise, the high rate of subsidence, and the declining sediment delivery, especially for the Suaeda marshes.
2017, 27(5): 818-826.
doi: 10.1007/s11769-017-0910-x
Abstract:
Urban parks composed mostly of vegetation and water bodies can effectively mitigate the urban heat island effect. Many studies have investigated the cooling effects of urban parks; however, little attention has been given to park landscape structure. Based on landscape metrics, this study has explored the influences of the park landscape structure on its inner thermal environment, taking heavily urbanized Beijing Municipality in China as the study area. Three indices, including the percentage of landscape (PLAND), landscape shape index (LSI) and aggregation index (AI), were used to measure the composition and configuration characteristics of the landscape components inside the parks. The indices were calculated for five landscape types being interpreted from Quickbird images. Urban thermal conditions were measured using the land surface temperature (LST) derived from Landsat TM images. The results showed that the park LST had a negative relationship with the park size, but no significant relationship was found with park shape. For the park's interior landscape, however, the configuration and composition characteristics of the landscape components inside the park explained 70% of the park LST variance. The area percentage of water bodies and the aggregation index of woodland were identified as the key influencing characteristics. In addition, when the composition and configuration characteristics of the park landscape components were separately considered, the configuration characteristics (LSI and AI) explained approximately 54% of the variance in park LST, which was comparable with that explained by the composition characteristics (PLAND). Thus, this study suggested that an effective and practical way for urban cooling park design is the optimization of spatial configuration of landscape components inside the park.
Urban parks composed mostly of vegetation and water bodies can effectively mitigate the urban heat island effect. Many studies have investigated the cooling effects of urban parks; however, little attention has been given to park landscape structure. Based on landscape metrics, this study has explored the influences of the park landscape structure on its inner thermal environment, taking heavily urbanized Beijing Municipality in China as the study area. Three indices, including the percentage of landscape (PLAND), landscape shape index (LSI) and aggregation index (AI), were used to measure the composition and configuration characteristics of the landscape components inside the parks. The indices were calculated for five landscape types being interpreted from Quickbird images. Urban thermal conditions were measured using the land surface temperature (LST) derived from Landsat TM images. The results showed that the park LST had a negative relationship with the park size, but no significant relationship was found with park shape. For the park's interior landscape, however, the configuration and composition characteristics of the landscape components inside the park explained 70% of the park LST variance. The area percentage of water bodies and the aggregation index of woodland were identified as the key influencing characteristics. In addition, when the composition and configuration characteristics of the park landscape components were separately considered, the configuration characteristics (LSI and AI) explained approximately 54% of the variance in park LST, which was comparable with that explained by the composition characteristics (PLAND). Thus, this study suggested that an effective and practical way for urban cooling park design is the optimization of spatial configuration of landscape components inside the park.
2017, 27(5): 827-835.
doi: 10.1007/s11769-017-0894-6
Abstract:
Land cover classification (LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidEye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidEye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows:1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement (3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.
Land cover classification (LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidEye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidEye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows:1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement (3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.
2017, 27(5): 836-846.
doi: 10.1007/s11769-017-0900-z
Abstract:
The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.
The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.
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