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 compr...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.展开更多
Artificial surfaces, characterized with intensive land-use changes and complex landscape structures, are important indicators of human impacts on terrestrial ecosystems. Without high-resolution land-cover data at cont...Artificial surfaces, characterized with intensive land-use changes and complex landscape structures, are important indicators of human impacts on terrestrial ecosystems. Without high-resolution land-cover data at continental scale, it is hard to evaluate the impacts of urbanization on regional climate, ecosystem processes and global environment. This study constructed a hierarchical classification system for artificial surfaces, promoted a remote sensing method to retrieve subpixel components of artificial surfaces from 30-m resolution satellite imageries(Globe Land30) and developed a series of data products of high-precision urban built-up areas including impervious surface and vegetation cover in Asia in 2010. Our assessment, based on multisource data and expert knowledge, showed that the overall accuracy of classification was 90.79%. The mean relative error for the impervious surface components of cities was 0.87. The local error of the extracted information was closely related to the heterogeneity of urban buildings and vegetation in different climate zones. According to our results, the urban built-up area was 18.18×104 km2, accounting for 0.59% of the total land surface areas in Asia; urban impervious surfaces were 11.65×104 km2, accounting for 64.09% of the total urban built-up area in Asia. Vegetation and bare soils accounted for 34.56% of the urban built-up areas. There were three gradients: a concentrated distribution, a scattered distribution and an indeterminate distribution from east to west in terms of spatial pattern of urban impervious surfaces. China, India and Japan ranked as the top three countries with the largest impervious surface areas, which respectively accounted for 32.77%, 16.10% and 11.93% of the urban impervious surface area of Asia. We found the proportions of impervious surface and vegetation cover within urban built-up areas were closely related to the economic development degree of the country and regional climate environment. Built-up areas in developed countries had relatively low impervious surface and high public green vegetation cover, with 50–60% urban impervious surfaces in Japan, South Korea and Singapore. In comparison, the proportion of urban impervious surfaces in developing countries is approaching or exceeding 80% in Asia. In general, the composition and spatial patterns of built-up areas reflected population aggregation and economic development level as well as their impacts on the health of the environment in the sub-watershed.展开更多
基金Under the auspices of Special Financial Grant and General Financial Grant from the China Postdoctoral Science Foundation(No.2015T80127,2014M561040)National Natural Science Foundation of China(No.41371172,41401171,41471143)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(No.164320H101)
文摘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.
基金financially supported by the National Natural Science Foundation of China (Grant Nos. 41371408 & 41371409)the National High Technology Research and Development Program of China (Grant No. 2013AA122802)the State Key Development Program for Basic Research of China (Grant No. 413714082014CB954302)
文摘Artificial surfaces, characterized with intensive land-use changes and complex landscape structures, are important indicators of human impacts on terrestrial ecosystems. Without high-resolution land-cover data at continental scale, it is hard to evaluate the impacts of urbanization on regional climate, ecosystem processes and global environment. This study constructed a hierarchical classification system for artificial surfaces, promoted a remote sensing method to retrieve subpixel components of artificial surfaces from 30-m resolution satellite imageries(Globe Land30) and developed a series of data products of high-precision urban built-up areas including impervious surface and vegetation cover in Asia in 2010. Our assessment, based on multisource data and expert knowledge, showed that the overall accuracy of classification was 90.79%. The mean relative error for the impervious surface components of cities was 0.87. The local error of the extracted information was closely related to the heterogeneity of urban buildings and vegetation in different climate zones. According to our results, the urban built-up area was 18.18×104 km2, accounting for 0.59% of the total land surface areas in Asia; urban impervious surfaces were 11.65×104 km2, accounting for 64.09% of the total urban built-up area in Asia. Vegetation and bare soils accounted for 34.56% of the urban built-up areas. There were three gradients: a concentrated distribution, a scattered distribution and an indeterminate distribution from east to west in terms of spatial pattern of urban impervious surfaces. China, India and Japan ranked as the top three countries with the largest impervious surface areas, which respectively accounted for 32.77%, 16.10% and 11.93% of the urban impervious surface area of Asia. We found the proportions of impervious surface and vegetation cover within urban built-up areas were closely related to the economic development degree of the country and regional climate environment. Built-up areas in developed countries had relatively low impervious surface and high public green vegetation cover, with 50–60% urban impervious surfaces in Japan, South Korea and Singapore. In comparison, the proportion of urban impervious surfaces in developing countries is approaching or exceeding 80% in Asia. In general, the composition and spatial patterns of built-up areas reflected population aggregation and economic development level as well as their impacts on the health of the environment in the sub-watershed.
