摘要：
A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People's Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods.
期刊：
International Journal of Environmental Research and Public Health,2023年20(3):2147- ISSN：1661-7827
通讯作者：
Chang Li
作者机构：
[Dehua Li; Jing Wu; Yan Jiang; Yijin Wu] Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China;[Chang Li] Author to whom correspondence should be addressed.
通讯机构：
[Chang Li] A;Author to whom correspondence should be addressed.
关键词：
belt and road;life expectancy;ecological environment;spatiotemporal lag spatial cross-correlation analysis;comprehensive ecological index
摘要：
The impact of building the Belt and Road on the ecological environment and the health of the related cities along this belt deserves more attention. Currently, there are few relevant pieces of research in this area, and the problem of a time lag between the ecological environment and health (e.g., life expectancy, LE) has not been explored. This paper investigates the aforementioned problem based on five ecological indicators, i.e., normalized difference vegetation index, leaf area index, gross primary production (GPP), land surface temperature (LST), and wet, which were obtained from MODIS satellite remote-sensing products in 2010, 2015, and 2020. The research steps are as follows: firstly, a comprehensive ecological index (CEI) of the areas along the Belt and Road was calculated based on the principle of component analysis; secondly, the changes in the trends of the five ecological indicators and the CEI in the research area in the past 11 years were calculated by using the trend degree analysis method; then, the distributions of the cold and hot spots of each index in the research area were calculated via cold and hot spot analysis; finally, the time lag relationship between LE and the ecological environment was explored by using the proposed spatiotemporal lag spatial crosscorrelation analysis. The experimental results show that ① there is a positive correlation between LE and ecological environment quality in the study area; ② the ecological environment has a lagging impact on LE, and the impact of ecological indicators in 2010 on LE in 2020 is greater than that in 2015; ③ among the ecological indicators, GPP has the highest impact on LE, while LST and Wet have a negative correlation with LE.
作者机构：
[Wang, Cong; Hu, Qiong; Wu, Yijin] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Cong; Hu, Qiong; Wu, Yijin] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Hu, Jie; Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Macro Agr Res Inst, Wuhan 430070, Peoples R China.;[Lin, Shangrong] Sun Yat Sen Univ, Sch Atmospher Sci, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.;[Xie, Qiaoyun] Univ Technol Sydney, Fac Sci, Sch Life Sci, Sydney, NSW 2007, Australia.
通讯机构：
[Qiong Hu] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province & School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
vegetation phenology;climatic limitation;solar-induced chlorophyll fluorescence;enhanced vegetation index
摘要：
Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.
期刊：
International Journal of Remote Sensing,2022年 ISSN：0143-1161
通讯作者：
Chang Li
作者机构：
[Huo, Zehua; Wang, Xueyu; Wu, Yijin; Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat, Wuhan, Hubei, Peoples R China.;[Huo, Zehua] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China.
通讯机构：
[Chang Li] K;Key Laboratory for Geographical Process Analysis & Simulation, College of Urban and Environmental Science, Central China Normal University, Wuhan, Hubei, China
关键词：
Spatio-temporal modelling;geographically and temporally weighted regression model (GTWR);night-time light intensity (NTLI);gross domestic product (GDP);NPP-VIIRS imagery
摘要：
Currently, most of the studies establish the relationship between night-time light intensity (NTLI) and gross domestic product (GDP) only in the temporal dimension or spatial dimension, without combining both of them or considering real spatio-temporal modelling. Moreover, few studies verify the spatio-temporal heterogeneity of the model. To solve the aforementioned problems, this paper is the first to propose using the geographically and temporally weighted regression model (GTWR) for coupling NTLI and GDP. The NTLI derived from NPP-VIIRS satellites and GDP statistics for 14 urban agglomerations in China from 2013 to 2020 were systematically studied by comparing four methods, including OLS (ordinary least squares), GWR (geographically weighted regression model), TWR (time-weighted regression model), and GTWR. It is found that the GTWR model has the highest coefficient of determination. This finding proves that ‘the spatio-temporal nature of material movement is inseparable from each other’ and verifies the superiority, correctness, and scientific validity of the GTWR model. The implications of this paper include 1) demonstrating the superiority of the GTWR model (i.e. three-dimensional spatio-temporal model) in fitting the relationship between NTLI and GDP of urban agglomerations; 2) solving the problem of insufficient sample size of the single dimension of NPP-VIIRS through the spatio-temporal model; 3) quantitatively detecting the spatio-temporal heterogeneity of urban agglomerations and analysing the high non-stationarity urban agglomerations by GTWR and spatio-temporal cube with spatio-temporal hot spot analysis.
作者机构：
[Zuo, Zilin; Wu, Yijin] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zuo, Zilin] Changjiang River Water Resources Commiss, Network & Informat Ctr, Wuhan 430010, Peoples R China.;[Wang, Hui] Hubei Anyuan Safety & Environm Protect Technol Co, Wuhan 430000, Peoples R China.;[Ding, Shuwen] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.
通讯机构：
[Hui Wang] A;[Yijin Wu] S;Authors to whom correspondence should be addressed.<&wdkj&>Hubei Anyuan Safety and Environmental Protection Technology Co., Ltd., Wuhan 430000, China<&wdkj&>School of Urban and Environment Science, Central China Normal University, Wuhan 430079, China<&wdkj&>Authors to whom correspondence should be addressed.
摘要：
Rill erosion is an important kind of slope erosion and the main source of sediment. Through simulated rainfall tests, the morphological characteristics of rill were quantified by stereophotogrammetry technology, and the relationship between rill development and sediment yield was studied. The results show that there was a positive correlation between sediment yield and slope and rainfall intensities. With the increase in rainfall duration, sediment yield first increased sharply and then decreased gradually after reaching the peak value, until it reached dynamic stability. With the increase in rainfall intensity and slope, the length, width, and number of rills increased significantly, with a maximum length of 2.58 m and a maximum width and depth of 9.7 and 2.2 cm. The rill density (RD) increased from 16.67% to 62.65%; rill fragmentation degree (RFD) increased from 16.67% to 100.00%; rill complexity (RC) increased from 10.62% to 30.84%, and rill width-depth ratio (RWDR) decreased from 15.82% to 56.28% with the increase in slope from 6 degrees to 15 degrees and rainfall intensity from 2.0 to 3.0 mm/min. There was a good nonlinear relationship between sediment yield and RC and RWDR (R-2 = 0.89, NSE = 0.85, n = 10). This study could provide help for the quantification research of rill erosion mechanisms and provide reference for the measurement and scale transformations of soil erosion at different scales.
作者机构：
[Ren, Wei; Wu, Yijin] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430100, Hubei, Peoples R China.;[Wang, Hui] Hubei Anyuan Safety & Environm Protect Technol Co, Wuhan 430100, Hubei, Peoples R China.
通讯机构：
[Wu, Yijin] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430100, Hubei, Peoples R China.
关键词：
Ecological environment;tourism economy;impoverished mountainous area;urbanization development model;sustainable development
摘要：
The ecological environment of poor mountainous areas is relatively fragile, and they are generally located in the national ecological environment protection zone. Subject to the constraints of the special natural environment, it is an inevitable requirement for the development of impoverished mountainous areas to find a way to optimize the development of urbanization. Tourism is an environmentally friendly industry suitable for sustainable economic development in poor mountainous areas. In this paper, taking the impoverished mountainous area in western Sichuan Province, China as an example, we conducted a systematic study on the urbanization development model of the coupling of ecological environment and tourism economy in this region. The entropy method and the compound index method were used to construct the evaluation index of the urbanization development of the study area, and the weights of 13 indexes were determined. The weight values of these indicators are mainly distributed from 0.11 to 0.16, with little difference. There is a good positive correlation between per capita tourism GDP and urbanization rate. This shows that tourism occupies a very large proportion in the economy of poor mountainous areas. There are three indicators (regional environmental noise, inhalable particulate matter, and urban-rural income gap index) that have a negative relationship with urbanization development. The scores of tourism economic indicators of a single region in different years show an upward trend. The economic indicators of tourism between the core tourist business district and the other two types of areas are all positively correlated. This shows that the development of tourism economy in the core tourist business area will drive the economic development of other types of regions. The eco-environmental indicators between the ecological environment protection area and the other two types of areas are all negatively correlated. This shows that in the ecological environment protection zone, ecological protection measures have been strengthened year by year. The development of tourism economy will have certain adverse effects on the ecological environment. From the perspective of ecological civilization, the urbanization of the study area should be transformed into an ecological and healthy city.
期刊：
Canadian Journal of Remote Sensing,2021年47(3):396-412 ISSN：0703-8992
通讯作者：
Li, Chang
作者机构：
[Wu, Jing; Zhang, Pengfei; Wu, Yijin; Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat, Wuhan, Hubei, Peoples R China.
通讯机构：
[Chang Li] K;Key Laboratory for Geographical Process Analysis and Simulation, College of Urban and Environmental Science, Central China Normal University, Hubei Province, China
作者机构：
[Yu, Yang; Wang, Li; Chen, Siyun; Chen, Yun; Xu, Xin; Tian, Xiaobo; Wu, Yijin] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Wu, Yijin] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
community care facilities;spatial accessibility;rural areas;the nearest distance method;Hubei Province
摘要：
With the increasing aging of the world's population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.
作者机构：
[Zhu, Heli; Wang, Di; Dong, Jing; Wu, Yijin; Li, Chang; Jiang, Chang] Cent China Normal Univ, Key Lab Geog Proc Analysing & Modelling, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Zhu, Heli; Wang, Di; Dong, Jing; Wu, Yijin; Li, Chang; Jiang, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Ye, Xinyue] New Jersey Inst Technol, Dept Informat, Newark, NJ USA.
通讯机构：
[Li, Chang] C;Cent China Normal Univ, Key Lab Geog Proc Analysing & Modelling, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China.
摘要：
In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.
