摘要：
Exploring the spatial patterns and driving factors of cropland ecosystem services and the production-living-ecology are essential to implementing spatial zoning management and optimization, especially in major grain-producing regions. In this study, we first developed an evaluation system for evaluating the production-living-ecology of cropland ecosystem services in the Jianghan Plain (JHP). We then revealed the spatial patterns of various cropland ecosystem services and integrated production-living-ecology index based on multi-source data. Additionally, we employed the regionalization with dynamically constrained agglomerative clustering and partitioning algorithm (REDCAP) to delineate agricultural function zones. Finally, we used redundancy analysis to reveal the driving factors of each functional zone. Results indicated that (1) high habitat quality, soil retention, and carbon storage services exhibited spatial similarity in 2020, with the spatial pattern of high in the west and low in the east, water conservation services showed an opposite distribution pattern, while culture and recreation services of high value were concentrated in the northern part, and high grain production was observed in the southern region. Overall, the production-living-ecology index displayed a north–south spatial distribution, with higher values in the north and lower values in the south. (2) based on the spatial zoning results of the production-living-ecology index, the six functional zones were identified, including zones of the production cropland, ecology cropland, ecology and living cropland, production and ecology cropland, production and living cropland and production-living-ecology cropland. (3) the production-living-ecology index and various ecosystem services were mainly influenced by population density, night light and evaporation in the JHP, and trade-offs were observed between the production function, and other functions as well as the production-living-ecology index in each functional zone of cropland ecosystems. The findings hold significant implications for the sustainable development of major grain-producing regions.
作者机构：
[Lang, Yang; Lang, Y; Yi, Qi; Zhang, Shixiao; Yang, Furong; Li, Xiuni; Qiao, Xinran] Yunnan Univ, Sch Earth Sci, Kunming 650050, Peoples R China.;[Li, Xiuni] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Gu, Yuefei] Yunnan Univ, Inst Int Rivers & Ecosecur, Kunming 650050, Peoples R China.;[Luo, Lifeng] Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA.;[Luo, Lifeng] Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA.
通讯机构：
[Lang, Y ] Y;Yunnan Univ, Sch Earth Sci, Kunming 650050, Peoples R China.
关键词：
CFSR;CMADS;meteorological variables;hydrological simulations;SWAT plus;upper Lancang-Mekong River Basin (LMRB)
摘要：
Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson's correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling.
关键词：
multi-view stereo images, building elements, multi-task learning, similar semantic patches, segmentation, clustering
摘要：
The reconstruction and analysis of building models are crucial for the construction of smart cities. A refined building model can provide a reliable data support for data analysis and intelligent management of smart cities. The colors, textures, and geometric forms of building elements, such as building outlines, doors, windows, roof skylights, roof ridges, and advertisements, are diverse; therefore, it is challenging to accurately identify the various details of buildings. This article proposes the Multi-Task Learning AINet method that considers features such as color, texture, direction, and roll angle for building element recognition. The AINet is used as the basis function; the semantic projection map of color and texture, and direction and roll angle is used for multi-task learning, and the complex building facade is divided into similar semantic patches. Thereafter, the multi-semantic features are combined using hierarchical clustering with a region adjacency graph and the nearest neighbor graph to achieve an accurate recognition of building elements. The experimental results show that the proposed method has a higher accuracy for building detailed edges and can accurately extract detailed elements.
期刊：
Journal of Urban Planning and Development,2023年149(3):05023025 ISSN：0733-9488
通讯作者：
Zheng, WS
作者机构：
[Zheng, Wensheng; Zhou, Ying] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;[Li, Chenggu] Northeast Normal Univ, Sch Geog Sci, Changchun 130024, Peoples R China.;[Zheng, Wensheng] China Tourism Acad, Wuhan Branch, Wuhan 430079, Peoples R China.;[Zheng, Wensheng] Cent China Normal Univ, Hubei High Qual Dev Inst, Wuhan 430079, Peoples R China.;[Ma, Zuopeng] Chinese Acad Sci, Northeast Inst Geog & Agroecol, Changchun 130102, Peoples R China.
通讯机构：
[Zheng, WS ] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Hubei, Peoples R China.;China Tourism Acad, Wuhan Branch, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Hubei High Qual Dev Inst, Wuhan 430079, Peoples R China.
摘要：
The governance of urban shrinkage has recently become an important topic for geographers and urban planners. However, a comprehensive exploration of the various spatial problems and governance paths in shrinking cities remains a huge gap in urban shrinkage governance literature. Building on the review and induction of extensive literature, this paper constructs a theoretical framework for the spatial governance of urban shrinkage from four perspectives: conceptual connotations, influencing factors, paths, and effects. By field investigation and policy interpretation, it takes Jixi City as a case, a coal-based shrinking city in China, to explore the policy response of the government and their effects. We find that the spatial governance of Jixi is a process of multisubject participation and multielement integration focusing on various spatial issues, which confirms the rationality of the analytical framework. Also, there are both similarities and differences in the spatial governance of urban shrinkage between Western countries and Chinese. This study enriches the theory of urban shrinkage governance, shows Chinese characteristics in urban governance, and provides governance enlightenment for other shrinking cities.
作者机构：
[Gong, Shengsheng; Gong, SS; Yang, Mengmeng; Wang, Wuwei] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.;[Huang, Shuqiong; Huo, Xixiang] Hubei Prov Ctr Dis Control & Prevent, Wuhan, Peoples R China.
通讯机构：
[Gong, SS ] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
摘要：
Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.
作者机构：
[Barnieh, Beatrice Asenso; Jia, Li; Jia, L] Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China.;[Menenti, Massimo; Zeng, Yelong; Lv, Yunzhe; Barnieh, Beatrice Asenso; Jia, Li; Jiang, Min; Bennour, Ali; Jia, L] Chinese Acad Sci, Beijing 100101, Peoples R China.;[Zeng, Yelong; Lv, Yunzhe; Barnieh, Beatrice Asenso; Bennour, Ali] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 19 Yuquan Rd, Beijing 100040, Peoples R China.;[Kabo-Bah, Amos Tiereyangn; Barnieh, Beatrice Asenso; Nyantakyi, Emmanuel Kwesi] Univ Energy & Nat Resources, Earth Observat Res & Innovat Ctr, POB 214, Sunyani, Ghana.;[Menenti, Massimo] Delft Univ Technol, Fac Civil Engn & Earth Sci, Stevinweg 1, NL-2628 CD Delft, Netherlands.
通讯机构：
[Jia, L ] I;Int Res Ctr Big Data Sustainable Dev Goals, Beijing 100094, Peoples R China.;Chinese Acad Sci, Beijing 100101, Peoples R China.
关键词：
natural vegetation;intensity analysis;spatial patterns;systematic transitions;random transitions;West Africa
摘要：
Land Use/Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key trend among the changes of the past few years is the loss of natural vegetation related to changes in different LULC categories, e.g., water bodies, wetland, and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in Africa. Here, we applied an open and synergistic framework to update and improve the existing LULC maps for West Africa at five-year intervals from 1990 to 2020-updating them to a finer spatial resolution of 30 m. Next, we detected spatial-temporal patterns in past and present LULC changes with the intensity analysis framework, focusing on the following periods: 1990-2000, 2000-2010, and 2010-2020. A faster annual rate of overall transition was detected in 1990-2000 and 2010-2020 than in 2000-2010. We observed consistent increases in shrubland and grassland in all of the periods, which confirms the observed re-greening of rangeland in West Africa. By contrast, forestland areas experienced consistent decreases over the entire period, indicating deforestation and degradation. We observed a net loss for cropland in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all of the periods. We observed net gains in water bodies in the 1990-2000 period and net losses in the 2010-2020 period. We highlighted the active forestland losses as systematic and issued a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concerns and should act as an early warning sign to policy makers that the food security of marginal geographic locations is under threat, despite the massive expansion of cropland observed in this study area. Instead of focusing on the dynamics of all the LULC categories that may be irrelevant, the intensity analysis framework was vital in identifying the settlement category relevant for LULC change management intervention in West Africa, as well as a cost-effective LULC change management approach.
摘要：
Delineating urban and peri-urban areas has often used information from multiple sources including remote sensing images, nighttime light images, and points-of-interest (POIs). Human mobility from big geo-spatial data could also be relevant for delineating peri-urban areas but its use is not fully explored. Moreover, it is necessary to assess how individual data sources are associated with identification results. Aiming at these gaps, we apply a neural network model to integrate indicators from multi-sources including land cover maps, nighttime light imagery as well as incorporating information about human movement from taxi trips to identify peri-urban areas. SHapley Additive exPlanations (SHAP) values are used as an explanation tool to assess how different data sources and indicators may be associated with delineation results. Wuhan, China is selected as a case study. Our findings highlight that socio-economic indicators, such as nighttime light intensity, have significant impacts on the identification of peri-urban areas. Spatial/physical attributes derived from land cover images and road density have relative low associations. Moreover, taxi intensity as a typical human movement dataset may complement nighttime light and POIs datasets, especially in refining boundaries between peri-urban and urban areas. Our study could inform the selection of data sources for identifying peri-urban areas, especially when facing data availability issues.
