2008 Vol. 18, No. 4
Display Method:
2008, 18(4): 291-299.
doi: 10.1007/s11769-008-0291-2
Abstract:
Urban agglomeration (UA) compactness means spatial concentration degree of physical entities, such as cities (towns), industries, resources, funds, traffic and technologies, whose concentration is formed according to specified economic and technologic association in the process of UA formation and development. The UA industrial compactness means the concentration degree of industry and industry clusters with reference to the industrial, technological and economic relations among the cities in the UA in the process of rational industrial division and with the exten- sion of industrial chain. After analyzing the researches on compactness, this paper finds that the relevant measurement coefficient and methods reflecting industrial geographical concentration fail to link industries spatial concentration with urban spatial concentration. Taking 23 UAs as samples and classifying them by development degree, this paper probes into UA compactness and spatial distribution characteristics from the perspective of industry by adopting UA index systems of industry and measurement models. The research finds out: 1) there is obvious positive correlation between UA industrial compactness and UA development degree; 2) the spatial distribution difference of UA industrial compactness is relatively great; and 3) UA industrial compactness shows a gradually decreasing tendency from the eastern part, the middle part to the western part of China. From the research thoughts and approaches, this article suggests that studies on the UA integrated compactness measurement should be enhanced from a multidimensional perspective involving space, traffic, population density and so on.
Urban agglomeration (UA) compactness means spatial concentration degree of physical entities, such as cities (towns), industries, resources, funds, traffic and technologies, whose concentration is formed according to specified economic and technologic association in the process of UA formation and development. The UA industrial compactness means the concentration degree of industry and industry clusters with reference to the industrial, technological and economic relations among the cities in the UA in the process of rational industrial division and with the exten- sion of industrial chain. After analyzing the researches on compactness, this paper finds that the relevant measurement coefficient and methods reflecting industrial geographical concentration fail to link industries spatial concentration with urban spatial concentration. Taking 23 UAs as samples and classifying them by development degree, this paper probes into UA compactness and spatial distribution characteristics from the perspective of industry by adopting UA index systems of industry and measurement models. The research finds out: 1) there is obvious positive correlation between UA industrial compactness and UA development degree; 2) the spatial distribution difference of UA industrial compactness is relatively great; and 3) UA industrial compactness shows a gradually decreasing tendency from the eastern part, the middle part to the western part of China. From the research thoughts and approaches, this article suggests that studies on the UA integrated compactness measurement should be enhanced from a multidimensional perspective involving space, traffic, population density and so on.
2008, 18(4): 300-307.
doi: 10.1007/s11769-008-0300-5
Abstract:
Since land and labor force are primary resources to be used and controlled by rural households, the allocation of labor forces will influence land uses, and further lead to land use conversion. The present study used the Binary Logit model to investigate the influence of labor force transfer, characteristics of rural households, location, and land market on agricultural land use conversion at rural household level. This study was conducted based on 329 valid questionnaires, which were obtained in Changshu, Rudong, and Tongshan counties, respectively representing the southern, middle and northern areas of Jiangsu Province. The results showed that land market participation, location, zonal difference and labor transfer had strong influences on agricultural land use conversion. The participation of land market had a strong positive effect on land use conversion, especially for the farmland converted to the fishpond. The nearer to the county seat, the more conversion of land use occurred. Particularly, the labor force transfer caused by wage employment decreased this conversion probability, while the labor transfer caused by self-employment led to more conversion; and the increasing of income from labor transfer increased the conversion. Moreover, land use con- versions demonstrated zonal difference, which were more in Rudong and Changshu counties than in Tongshan County, and the factors influencing this conversion were different in the three regions.
Since land and labor force are primary resources to be used and controlled by rural households, the allocation of labor forces will influence land uses, and further lead to land use conversion. The present study used the Binary Logit model to investigate the influence of labor force transfer, characteristics of rural households, location, and land market on agricultural land use conversion at rural household level. This study was conducted based on 329 valid questionnaires, which were obtained in Changshu, Rudong, and Tongshan counties, respectively representing the southern, middle and northern areas of Jiangsu Province. The results showed that land market participation, location, zonal difference and labor transfer had strong influences on agricultural land use conversion. The participation of land market had a strong positive effect on land use conversion, especially for the farmland converted to the fishpond. The nearer to the county seat, the more conversion of land use occurred. Particularly, the labor force transfer caused by wage employment decreased this conversion probability, while the labor transfer caused by self-employment led to more conversion; and the increasing of income from labor transfer increased the conversion. Moreover, land use con- versions demonstrated zonal difference, which were more in Rudong and Changshu counties than in Tongshan County, and the factors influencing this conversion were different in the three regions.
2008, 18(4): 308-315.
doi: 10.1007/s11769-008-0308-x
Abstract:
Agricultural village is an important rural type in China. The evolution process and mechanism of its cultural landscape are different from those of the industrialized village. The paper focuses this theme by a case study of Qianzhai Village of Qufu City, Shangdong Province. In the case of Qianzhai Village, the evolution of its settlement has experienced three stages: mechanical expansion, sprawling expansion and hollowing, and recentralization. The land-scape evolution of residents' houses has undergone three phases: traditional quadrangle house, one-storied house and multistoried house. The evolution of its land use has experienced three stages: circled stratification, fragment and intensive use, and concentration and extensive use. We can see that the main driving factors of cultural landscape evolution of agricultural village are the changes of rural population, society, economy and culture, which are influenced by the change of urban-rural relation, the national modernization process and economic development, the reform of family planning and land system, and the changes in governmental policies. In the future, the modernization, ecological trend, and individualization for residents' houses of agricultural villages in China will develop step by step. The recentralization of settlement and the scaled, sustainable, intensive land use are likely to be the trends of agricultural villages in China.
Agricultural village is an important rural type in China. The evolution process and mechanism of its cultural landscape are different from those of the industrialized village. The paper focuses this theme by a case study of Qianzhai Village of Qufu City, Shangdong Province. In the case of Qianzhai Village, the evolution of its settlement has experienced three stages: mechanical expansion, sprawling expansion and hollowing, and recentralization. The land-scape evolution of residents' houses has undergone three phases: traditional quadrangle house, one-storied house and multistoried house. The evolution of its land use has experienced three stages: circled stratification, fragment and intensive use, and concentration and extensive use. We can see that the main driving factors of cultural landscape evolution of agricultural village are the changes of rural population, society, economy and culture, which are influenced by the change of urban-rural relation, the national modernization process and economic development, the reform of family planning and land system, and the changes in governmental policies. In the future, the modernization, ecological trend, and individualization for residents' houses of agricultural villages in China will develop step by step. The recentralization of settlement and the scaled, sustainable, intensive land use are likely to be the trends of agricultural villages in China.
2008, 18(4): 316-322.
doi: 10.1007/s11769-008-0316-x
Abstract:
Taking Nanjing as a case, the paper explains the spatial behavior differences existing in the information technology use among different groups of residents and households, by virtue of analyzing the survey data of urban households in the 11 districts of Nanjing, from the social, spatial, life and other non-technical angles. Also it makes various analyses and evaluation quantitatively and qualitatively on the social and spatial effect of information technology. The results show that the new technology is changing the social spatial behaviors of urban residents. New behavioral spaces of urban family such as telecommuting, email and QQ have begun to emerge. With the help of Internet, the communication scope of families has expanded greatly, and more new forms of publicizing community information have begun to emerge. Telecommunication contact forms have been developing swiftly, and their frequencies of contact have been increasing dramatically.
Taking Nanjing as a case, the paper explains the spatial behavior differences existing in the information technology use among different groups of residents and households, by virtue of analyzing the survey data of urban households in the 11 districts of Nanjing, from the social, spatial, life and other non-technical angles. Also it makes various analyses and evaluation quantitatively and qualitatively on the social and spatial effect of information technology. The results show that the new technology is changing the social spatial behaviors of urban residents. New behavioral spaces of urban family such as telecommuting, email and QQ have begun to emerge. With the help of Internet, the communication scope of families has expanded greatly, and more new forms of publicizing community information have begun to emerge. Telecommunication contact forms have been developing swiftly, and their frequencies of contact have been increasing dramatically.
2008, 18(4): 323-330.
doi: 10.1007/s11769-008-0323-y
Abstract:
The Liupan Mountains is located in the southern Ningxia Hui Autonomous Region of China, which forms an important dividing line between landforms and bio-geographic regions. The populated part of the Liupan Mountains region has suffered tremendous ecological damages over time due to population pressure, excessive demand and inappropriate use of agricultural land resources. In this paper, datasets of land use between 1990 and 2000 were obtained from Landsat TM imagery, and then spatial models were used to characterize landscape conditions. Also, the relationship between the population density and land use/cover change (LUCC) was analyzed. Results indicate that cropland, forestland, and urban areas have increased by 44,186ha, 9001ha and 1550ha, respectively while the grassland area has appreciably decreased by 54,025ha in the study period. The decrease in grassland was most notable. Of the grassland lost, 49.4% was converted into cropland. The largest annual land conversion rate in the study area was less than 2%. These changes are attributed to industrial and agricultural development and population growth. To improve the eco-economic conditions in the study region, population control, urbanization and development of an ecological friendly agriculture were suggested.
The Liupan Mountains is located in the southern Ningxia Hui Autonomous Region of China, which forms an important dividing line between landforms and bio-geographic regions. The populated part of the Liupan Mountains region has suffered tremendous ecological damages over time due to population pressure, excessive demand and inappropriate use of agricultural land resources. In this paper, datasets of land use between 1990 and 2000 were obtained from Landsat TM imagery, and then spatial models were used to characterize landscape conditions. Also, the relationship between the population density and land use/cover change (LUCC) was analyzed. Results indicate that cropland, forestland, and urban areas have increased by 44,186ha, 9001ha and 1550ha, respectively while the grassland area has appreciably decreased by 54,025ha in the study period. The decrease in grassland was most notable. Of the grassland lost, 49.4% was converted into cropland. The largest annual land conversion rate in the study area was less than 2%. These changes are attributed to industrial and agricultural development and population growth. To improve the eco-economic conditions in the study region, population control, urbanization and development of an ecological friendly agriculture were suggested.
2008, 18(4): 331-339.
doi: 10.1007/s11769-008-0331-y
Abstract:
This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipitation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipitation showed little increase over the period of 1957–2002. By changing the time scale from 32-year to 4-year, we observed nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.
This paper applied an integrated method combining grey relation analysis, wavelet analysis and statistical analysis to study climate change and its effects on runoff of the Kaidu River at multi-time scales. Major findings are as follows: 1) Climatic factors were ranked in the order of importance to annual runoff as average annual temperature, average temperature in autumn, average temperature in winter, annual precipitation, precipitation in flood season, average temperature in summer, and average temperature in spring. The average annual temperature and annual precipitation were selected as the two representative factors that impact the annual runoff. 2) From the 32-year time scale, the annual runoff and the average annual temperature presented a significantly rising trend, whereas the annual precipitation showed little increase over the period of 1957–2002. By changing the time scale from 32-year to 4-year, we observed nonlinear trends with increasingly obvious oscillations for annual runoff, average annual temperature, and annual precipitation. 3) The changes of the runoff and the regional climate are closely related, indicating that the runoff change is the result of the regional climate changes. With time scales ranging from 32-year, 16-year, 8-year and to 4-year, there are highly significant linear correlations between the annual runoff and the average annual temperature and the annual precipitation.
2008, 18(4): 340-347.
doi: 10.1007/s11769-008-0340-x
Abstract:
A non-dimensional relative sensitivity coefficient was employed to predict the responses of reference crop evapotranspiration (ET0) to perturbation of four climate variables in Tao'er River Basin of the northeastern China. Mean monthly ET0 and yearly ET0 from 1961 to 2005 were estimated with the FAO-56 Penman-Monteith Equation. A 45-year historical dataset of average monthly maximum/minimum air temperature, mean air temperature, wind speed, sunshine hours and relative humidity from 15 meteorological stations was used in the analysis. Results show that: 1) Sensitivity coefficients of wind speed, air temperature and sunshine hours were positive except for those of air temperature of Arxan Meteorological Station, while those of relative humidity were all negative. Relative humidity was the most sensitive variable in general for the Tao'er River Basin, followed by sunshine hours, wind speed and air temperature. 2) Similar to climate variable, monthly sensitivity coefficients exhibit large annual fluctuations. 3) Sensitivity coefficients for four climate variables all showed significant trends in seasonal/yearly series. Also, sensitivity coefficients of air temperature, sunshine hours and wind speed all showed significant trends in spring. 4) Among all sensitivity coefficients, the average yearly sensitivity coefficient of relative humidity was highest throughout the basin and showed largest spatial variability. Longitudinal distribution of sensitivity coefficients for air temperature, relative humidity and sunshine hours was also found, which was similar to the distribution of the three climate variables.
A non-dimensional relative sensitivity coefficient was employed to predict the responses of reference crop evapotranspiration (ET0) to perturbation of four climate variables in Tao'er River Basin of the northeastern China. Mean monthly ET0 and yearly ET0 from 1961 to 2005 were estimated with the FAO-56 Penman-Monteith Equation. A 45-year historical dataset of average monthly maximum/minimum air temperature, mean air temperature, wind speed, sunshine hours and relative humidity from 15 meteorological stations was used in the analysis. Results show that: 1) Sensitivity coefficients of wind speed, air temperature and sunshine hours were positive except for those of air temperature of Arxan Meteorological Station, while those of relative humidity were all negative. Relative humidity was the most sensitive variable in general for the Tao'er River Basin, followed by sunshine hours, wind speed and air temperature. 2) Similar to climate variable, monthly sensitivity coefficients exhibit large annual fluctuations. 3) Sensitivity coefficients for four climate variables all showed significant trends in seasonal/yearly series. Also, sensitivity coefficients of air temperature, sunshine hours and wind speed all showed significant trends in spring. 4) Among all sensitivity coefficients, the average yearly sensitivity coefficient of relative humidity was highest throughout the basin and showed largest spatial variability. Longitudinal distribution of sensitivity coefficients for air temperature, relative humidity and sunshine hours was also found, which was similar to the distribution of the three climate variables.
2008, 18(4): 348-355.
doi: 10.1007/s11769-008-0348-2
Abstract:
Many kinds of uncertainties are involved, such as random, fuzzy, grey, unascertained property and so on, in soil erosion process. To exactly predict the non-point source pollution loads, some uncertainties should be taken into consideration. Aiming at the deficiency of present blind number theory being helpless for fuzziness, a novel blind number, i.e. extended-blind number, was introduced by substituting a set of triangular fuzzy numbers (TFNs), expressed as α-cuts, for interval values in present blind number, and the expected value of extended-blind number was also brought forward by referring to the current blind number theory. On the basis of denoting the parameters of Universal Soil Loss Equation (USLE) as extended-blind parameters, a novel USLE model was established for quantitatively evaluating soil erosion loss and non-point source pollution loads. As a case, the uncertain USLE was employed for predicting the soil erosion loss and non-point source pollution loads of absorbed nitrogen and phosphorus in a district in the Hangbu-Fengle River basin, in the upstream of Chaohu Lake watershed. The results show that it is feasible in theory to extend blind number into fuzzy environment and reliable on conclusion to apply extended-blind number theory for predicting non-point source pollution loads.
Many kinds of uncertainties are involved, such as random, fuzzy, grey, unascertained property and so on, in soil erosion process. To exactly predict the non-point source pollution loads, some uncertainties should be taken into consideration. Aiming at the deficiency of present blind number theory being helpless for fuzziness, a novel blind number, i.e. extended-blind number, was introduced by substituting a set of triangular fuzzy numbers (TFNs), expressed as α-cuts, for interval values in present blind number, and the expected value of extended-blind number was also brought forward by referring to the current blind number theory. On the basis of denoting the parameters of Universal Soil Loss Equation (USLE) as extended-blind parameters, a novel USLE model was established for quantitatively evaluating soil erosion loss and non-point source pollution loads. As a case, the uncertain USLE was employed for predicting the soil erosion loss and non-point source pollution loads of absorbed nitrogen and phosphorus in a district in the Hangbu-Fengle River basin, in the upstream of Chaohu Lake watershed. The results show that it is feasible in theory to extend blind number into fuzzy environment and reliable on conclusion to apply extended-blind number theory for predicting non-point source pollution loads.
2008, 18(4): 356-360.
doi: 10.1007/s11769-008-356-2
Abstract:
On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
2008, 18(4): 361-368.
doi: 10.1007/s11769-008-0361-5
Abstract:
In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang Plain, Northeast China. The basic principle of the PCA is that the pairs of parameters which are highly correlated and linear with each other would contribute the same information to an assessment and one of them should be eliminated. The method of the PCA is that a correlation relationship among candidate parameters is examined before the FSE. If there is an apparent nonlinear or curvilinear relationship between two parameters, then both will be retained; if the correlation is significant (p<0.01), and the scatter plot suggests a linear relationship, then one of them will be deleted. However, which one will be deleted? For solving this problem, a sensitivity test was conducted and the higher sensitivity parameters remained. The results indicate that the original data should be preprocessed through the PCA for redundancy and variability. The study shows that introducing the PCA into the FSE can simplify the FSE calculation process greatly, while the results have not been changed much.
In order to improve the effectiveness of Fuzzy Synthetic Evaluation (FSE) models, a Parameter Correlation Analysis (PCA) was introduced into the FSE and a case study was carried out in the Naoli River in the Sanjiang Plain, Northeast China. The basic principle of the PCA is that the pairs of parameters which are highly correlated and linear with each other would contribute the same information to an assessment and one of them should be eliminated. The method of the PCA is that a correlation relationship among candidate parameters is examined before the FSE. If there is an apparent nonlinear or curvilinear relationship between two parameters, then both will be retained; if the correlation is significant (p<0.01), and the scatter plot suggests a linear relationship, then one of them will be deleted. However, which one will be deleted? For solving this problem, a sensitivity test was conducted and the higher sensitivity parameters remained. The results indicate that the original data should be preprocessed through the PCA for redundancy and variability. The study shows that introducing the PCA into the FSE can simplify the FSE calculation process greatly, while the results have not been changed much.
2008, 18(4): 369-373.
doi: 10.1007/s11769-008-0369-x
Abstract:
The influence derived from atmosphere transmitting of radar wave, in the application of high-resolution airborne Synthetic Aperture Radar (SAR) stereo positioning, may produce some phase errors, and eventually be introduced into positioning model. This paper described the principle of airborne SAR stereo positioning and the error sources of stereo positioning accuracy that arose from atmosphere transmitting, established a corresponding assessment model of atmosphere transmitting influence, and testified the model and the assessment principle taking the 1-m resolution airborne SAR images of Zigong City, Sichuan Province in China, as the test dataset. The test result has proved that the assessment model is reliable and reasonable. And, it has shown that the phase error arisen from time delay is the main error source during the atmosphere transmitting, which has much more influences on cross-track direction and introduces a stereo positioning error of about eight meters, but less on the along-track direction.
The influence derived from atmosphere transmitting of radar wave, in the application of high-resolution airborne Synthetic Aperture Radar (SAR) stereo positioning, may produce some phase errors, and eventually be introduced into positioning model. This paper described the principle of airborne SAR stereo positioning and the error sources of stereo positioning accuracy that arose from atmosphere transmitting, established a corresponding assessment model of atmosphere transmitting influence, and testified the model and the assessment principle taking the 1-m resolution airborne SAR images of Zigong City, Sichuan Province in China, as the test dataset. The test result has proved that the assessment model is reliable and reasonable. And, it has shown that the phase error arisen from time delay is the main error source during the atmosphere transmitting, which has much more influences on cross-track direction and introduces a stereo positioning error of about eight meters, but less on the along-track direction.
2008, 18(4): 374-381.
doi: 10.1007/s11769-008-0374-x
Abstract:
Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecology and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.
Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecology and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.
Email
RSS