As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ...As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.展开更多
Land consolidation(LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation(CLC) to support the rural revitalization initiative. However, th...Land consolidation(LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation(CLC) to support the rural revitalization initiative. However, there are ongoing challenges in understanding CLC's specific pathway and mechanism, particularly its role in stimulating rural endogenous development. This study aims to investigate the localization process of international experiences, examine the pathway of CLC, and scrutinize its mechanism in rural development from a novel perspective of neo-endogenous development. Field research and semi-structured interviews were conducted in Nanzhanglou village, renowned for its early adoption of CLC practices inspired by German experiences since 1988. Overall, key findings underscore the advantages of CLC in spatial restructuring, industrial development, and human capital enhancement in rural areas. Additionally, international experiences emerge as crucial exogenous forces, primarily by knowledge embedding, which catalyzes rural neo-endogenous development via the “resource-engagement-identity-endogenous” mechanism. Collectively, by introducing a neo-endogenous theoretical framework, this study offers valuable insights into the CLC implementation in China and beyond, and emphasizes the positive impact of knowledge embedding as an exogenous force in promoting rural neo-endogenous development to address existing research gaps. Recommendations for sustainable rural development involve enhancing rural planning practicality, governance capacity, and local leadership, while prioritizing agricultural modernization and increasing investments in education and vocational training to ensure that villagers benefit from industrial development.展开更多
Spatially explicit modeling plays a vital role in land use/cover change and urbanization research as well as resources management;however,current models lack proper validation and fail to incorporate uncertainty into ...Spatially explicit modeling plays a vital role in land use/cover change and urbanization research as well as resources management;however,current models lack proper validation and fail to incorporate uncertainty into the formulation of model predictions.Consequently,policy makers and the general public may develop opinions based on potentially misleading research,which fails to allow for truly informed decisions.Here we use an uncertainty strategy of spatially explicit modeling combined with the series statistic of Kappa index for location and quantity to estimate the uncertainty of future predications and to determine model accuracy.We take the Beijing metropolitan area as an example to demonstrate the uncertainty in extrapolations of predictive land use change and urban sprawl with spatially explicit modeling at multiple resolutions.The sensitivity of scale effects is also discussed.The results show that an improvement in specification of location is more helpful in increasing accuracy as compared to an improvement in the specification of quantity at fine spatial resolutions.However,the spatial scale has great effects on modeling accuracy and correct due to chance tends to increase as resolution becomes coarser.The results allow us to understand the uncertainty when using spatially explicit models for land-use change or urbanization estimates.展开更多
This research reconstructs China＇s provincial farmland dataset in the last 300 years （1661-1985） by applying factor correction, citing replacement, linear interpolation, cohesion and contrast, man-land relationship...This research reconstructs China＇s provincial farmland dataset in the last 300 years （1661-1985） by applying factor correction, citing replacement, linear interpolation, cohesion and contrast, man-land relationship test, farming trend test, provincial administrative area adjustment, etc. on available farmland data based on China＇s current provincial administrative boundary. Based on this dataset, a quantitative analysis has been applied to study the farm- land amount and its change Characteristics at both national and provincial level. Three con- clusions are derived： （1） Along with the rapid population growth, national farmland amount has increased by about 320% in the last 300 years from 424,480 km2 in the early Qing Dy- nasty to 1,368,600 km2 in 1985. Comparing with global and national farmland datasets, in terms of the overall trend of national farmland growth, very low deviation exists but significant variances do appear for some provinces. （2） At the beginning of the Qing Dynasty, China＇s farming activities mainly existed in the Yangtze River Plain, the North China Plain, the Guanzhong Basin and the Yinchuan Plain. Thereafter, reclamation activities expanded to outer agricultural areas. Regarding of the growth rate, national farmland increase can be divided into five phases. National policy, disasters, wars, and economic development, are the main factors affecting farmland changes. （3） Significant regional variances exist in farmland changes. In the space shaped by the average farmland amount and the average annual change rate of farmland, the nation can be divided into six areas.展开更多
基金Under the auspices of National Basic Research Program of China(No.2011CB952001)National Natural Science Foundation of China(No.41340016,412013860)
文摘As an important part of land use/cover change(LUCC), historical LUCC in long time series attracts much more attention from scholars. Currently, based on the view of combining the overall control of cropland area and ′top-down′ decision-making behaviors, here are two global historical land-use datasets, generally referred as the Sustainability and the Global Environment datasets(SAGE datasets) and History Database of the Global Environment datasets(HYDE datasets). However, at the regional level, these global datasets have coarse resolutions and inevitable errors. Considering various factors that influenced cropland distribution, including cropland connectivity and the limitation of natural and human factors, this study developed a reconstruction model of historical cropland based on constrained Cellular Automaton(CA) of ′bottom-up′. Then, an available labor force index is used as a proxy for the amount of cropland to inspect and calibrate these spatial patterns. Applied the reconstruction model to Shandong Province, we reconstructed its spatial distribution of cropland during 8 periods. The reconstructed results show that: 1) it is properly suitable for constrained CA to simulate and reconstruct the spatial distribution of cropland in traditional cultivated region of China; 2) compared with ′SAGE datasets′ and ′HYDE datasets′, this study have formed higher-resolution Boolean spatial distribution datasets of historical cropland with a more definitive concept of spatial pattern in terms of fractional format.
基金National Natural Science Foundation of China,No.42271259The Open Fund of Key Laboratory of Coastal Zone Exploitation and Protection,Ministry of Natural Resources,China,No.2021CZEPK07。
文摘Land consolidation(LC) stands as a globally recognized strategy for rural development. In China, it has evolved towards comprehensive land consolidation(CLC) to support the rural revitalization initiative. However, there are ongoing challenges in understanding CLC's specific pathway and mechanism, particularly its role in stimulating rural endogenous development. This study aims to investigate the localization process of international experiences, examine the pathway of CLC, and scrutinize its mechanism in rural development from a novel perspective of neo-endogenous development. Field research and semi-structured interviews were conducted in Nanzhanglou village, renowned for its early adoption of CLC practices inspired by German experiences since 1988. Overall, key findings underscore the advantages of CLC in spatial restructuring, industrial development, and human capital enhancement in rural areas. Additionally, international experiences emerge as crucial exogenous forces, primarily by knowledge embedding, which catalyzes rural neo-endogenous development via the “resource-engagement-identity-endogenous” mechanism. Collectively, by introducing a neo-endogenous theoretical framework, this study offers valuable insights into the CLC implementation in China and beyond, and emphasizes the positive impact of knowledge embedding as an exogenous force in promoting rural neo-endogenous development to address existing research gaps. Recommendations for sustainable rural development involve enhancing rural planning practicality, governance capacity, and local leadership, while prioritizing agricultural modernization and increasing investments in education and vocational training to ensure that villagers benefit from industrial development.
基金supported by China Postdoctoral Science Foundation (Grant No.20070420630)National Basic Research Program of China (Grant Nos.2002CB412507,G19990435)
文摘Spatially explicit modeling plays a vital role in land use/cover change and urbanization research as well as resources management;however,current models lack proper validation and fail to incorporate uncertainty into the formulation of model predictions.Consequently,policy makers and the general public may develop opinions based on potentially misleading research,which fails to allow for truly informed decisions.Here we use an uncertainty strategy of spatially explicit modeling combined with the series statistic of Kappa index for location and quantity to estimate the uncertainty of future predications and to determine model accuracy.We take the Beijing metropolitan area as an example to demonstrate the uncertainty in extrapolations of predictive land use change and urban sprawl with spatially explicit modeling at multiple resolutions.The sensitivity of scale effects is also discussed.The results show that an improvement in specification of location is more helpful in increasing accuracy as compared to an improvement in the specification of quantity at fine spatial resolutions.However,the spatial scale has great effects on modeling accuracy and correct due to chance tends to increase as resolution becomes coarser.The results allow us to understand the uncertainty when using spatially explicit models for land-use change or urbanization estimates.
基金National Basic Research Program of China, No.2011CB952001 National Natural Science Foundation of China, No .41340016
文摘This research reconstructs China＇s provincial farmland dataset in the last 300 years （1661-1985） by applying factor correction, citing replacement, linear interpolation, cohesion and contrast, man-land relationship test, farming trend test, provincial administrative area adjustment, etc. on available farmland data based on China＇s current provincial administrative boundary. Based on this dataset, a quantitative analysis has been applied to study the farm- land amount and its change Characteristics at both national and provincial level. Three con- clusions are derived： （1） Along with the rapid population growth, national farmland amount has increased by about 320% in the last 300 years from 424,480 km2 in the early Qing Dy- nasty to 1,368,600 km2 in 1985. Comparing with global and national farmland datasets, in terms of the overall trend of national farmland growth, very low deviation exists but significant variances do appear for some provinces. （2） At the beginning of the Qing Dynasty, China＇s farming activities mainly existed in the Yangtze River Plain, the North China Plain, the Guanzhong Basin and the Yinchuan Plain. Thereafter, reclamation activities expanded to outer agricultural areas. Regarding of the growth rate, national farmland increase can be divided into five phases. National policy, disasters, wars, and economic development, are the main factors affecting farmland changes. （3） Significant regional variances exist in farmland changes. In the space shaped by the average farmland amount and the average annual change rate of farmland, the nation can be divided into six areas.
