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.
期刊：
Frontiers in Environmental Science,2023年11:1260949 ISSN：2296-665X
通讯作者：
Li, XM
作者机构：
[Shi, Pengfei] Southwest Univ, Sch Econ & Management, Chongqing, Peoples R China.;[Long, Huibing] Hunan Univ, Sch Econ & Trade, Changsha, Peoples R China.;[Yao, Yikun] Sun Yat Sen Univ, Coll Tourism Management, Guangzhou, Peoples R China.;[Li, Xingming; Li, XM] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.;[Wang, Xinrui] Shandong Vocat & Tech Univ Int Studies, Sch Foreign Languages, Rizhao, Peoples R China.
通讯机构：
[Li, XM ] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
关键词：
tourism green production efficiency(TGPE);Space-time characteristics;Spatial spillover effect;Yangtze;Influence factors
摘要：
Tourism green production efficiency serves as the foundation for assessing the mutual coupling performance of the tourism economy and the ecological environment. In this paper, the tourism carbon sink is included in the measurement framework, and the TGPE of 41 cities in the Yangtze River Delta region from 2011 to 2019 is estimated by the Super-SBM model. Furthermore, kernel density estimate, spatial autocorrelation, Markov chain and spatial Durbin model are further integrated to explore its spatio-temporal evolution process, spatial effects and influencing factors. The results show that 1) TGPE in the Yangtze River Delta has been increasing during the study period. The high-efficiency and low-efficiency areas of the TGPE have a bipolar pattern characterized by "low-low convergence" and "high-high convergence." 2) There is considerable spatial variation in TGPE from north to south. The number of hot spots and sub-hot spots increases in volatility, whereas the number of sub-cold spots and cold spots decreases. 3) Although cities with low levels of TGPE have a higher probability of moving to the next level, grade transformation across hierarchies is difficult to attain. When considering the factor of adjacent types and the influence of spatial lag on the transfer probability. 4) The positive spatial spillover effects of TGPE is significant. At the same time, economic development level, transport accessibility and tourism industry agglomeration have positive spillover effects on neighboring cities. Conversely, urbanization level and openness level have negative spillover effects.
作者机构：
[Zhang, Chunxiao; Li, Heng; Niu, Yunyun] China Univ Geosci Beijing, Sch Informat Engn, 29, Xueyuan Rd, Beijing 100083, Peoples R China.;[Zhang, Chunxiao] Minist Nat Resources, Observat & Res Stn Beijing Fangshan Comprehens Exp, Beijing 100083, Peoples R China.;[Chen, Min] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Chunxiao Zhang] S;School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing, 100083, China<&wdkj&>Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources, Beijing, 100083, China
摘要：
Leaf senescence plays a pivotal role in the regulation of carbon and nutrient cycles within terrestrial ecosystems. However, our understanding of leaf senescence velocity (LSV) remains comparatively limited when compared to the end of the growing season (EOS). In this study, we extracted the LSV in Tibet Plateau (TP) over the period 2001-2018 based on the satellite-derived normalized difference greenness index (NDGI), then we evaluated the influences of climate drivers on the spatio-temporal variations of LSV. Lastly, we explored the implications of LSV on vegetation growth by analyzing the correlation between LSV and the start of growing season (SOS) and the annual net primary production (NPP). Our findings revealed that the multi-year averaged LSV ranged from 30 to 70% mon-1 and displayed a discernible spatial gradient, declining from west to east, and the spatial pattern of LSV was mainly controlled by radiation. Trend tests and partial correlation analyses unveiled a temporal decrease in LSV within the central TP region, attributed to rising temperatures, while an increase was observed in the southwestern TP due to water deficits. We also found that LSV had a strong impact on current-year net NPP and following-year SOS. This suggests that LSV may play a significant role in regulating carbon exchange during the current year and influencing the onset of spring green-up in the subsequent year. By emphasizing the continuous nature of leaf senescence, our study provides fresh insights into the intricate interactions between vegetation growth and climate change, contributing to the existing body of knowledge in this field.
作者机构：
[Zhou, Jie; Liu, Xuan; Zhao, Feng; Cui, Yilin; Xiong, Xuqian] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Luoyu Rd 152, Wuhan 430079, Peoples R China.;[Menenti, Massimo; Jia, Li] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;[Zhou, Jie; Menenti, Massimo] Delft Univ Technol, Fac Civil Engn & Geosci, Stevinweg 1, NL-2825 CN Delft, Netherlands.;[Gao, Bo] Capital Normal Univ, Coll Resources Environm & Tourism, Beijing, Peoples R China.;[Li, Dengchao] First Geol brigade Hubei Geol Bur, Huangshi, Peoples R China.
通讯机构：
[Jie Zhou] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, College of Urban and Environmental Sciences, Central China Normal University, Wuhan, People’s Republic of China<&wdkj&>Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands
关键词：
Time series reconstruction;remote sensing;Google Earth Engine;HANTS;gap-filling
摘要：
Spatiotemporal residual noise in terrestrial earth observation products, often caused by unfavorable atmospheric conditions, impedes their broad applications. Most users prefer to use gap-filled remote sensing products with time series reconstruction (TSR) algorithms. Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources. This study introduces a new open-source software package entitled 'HANTS-GEE' that implements a well-known and robust TSR algorithm, i.e. Harmonic ANalysis of Time Series (HANTS), on the Google Earth Engine (GEE) platform for scalable reconstruction of terrestrial earth observation data. Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE. According to site-based and regional-based case evaluation, the new tool can effectively eliminate cloud contamination in the time series of earth observation data. Compared with traditional PC-based HANTS implementation, the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites. The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume, promoting algorithm usage by much broader user communities. To our knowledge, the software package is the first tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.
摘要：
Read the free Plain Language Summary for this article on the Journal blog. Abstract Nitrogen (N) and phosphorus (P) are essential elements limiting plant–microbial growth in forest ecosystems. However, whether the pattern of plant–microbe nutrient limitation is consistent across forest biomes and the associated potential mechanisms remain largely unclear, limiting us to better understand the biogeochemical processes under future climate change. Here, we investigated patterns of plant–microbial N/P limitation in forests across a wide environmental gradient and biomes in China to explore the divergence of plant–microbial N/P limitation and the driving mechanisms. We revealed that 42.6% of the N/P limitation between plant–microbial communities was disconnected. Patterns in plant–microbial N/P limitations were consistent only for 17.7% of N and 39.7% of P. Geospatially, the inconsistency was more evident at mid‐latitudes, where plants were mainly N limited and microbes were mainly P limited. Furthermore, our findings were consistent with the ecological stoichiometry of plants and microbes themselves and their requirements. Whereas plant N and P limitation was more strongly responsive to meteorological conditions and atmospheric deposition, that of microbes was more strongly responsive to soil chemistry, which exacerbated the plant–microbe N and P limitation divergence. Our work identified an important disconnection between plant and microbial N/P limitation, which should be incorporated into future Earth System Model to better predict forest biomes–climate change feedback. Read the free Plain Language Summary for this article on the Journal blog.
摘要：
Grass recovery is often implemented in the loess area of China to control erosion. However, the effect mechanisms of grass cover on runoff erosion dynamics on steep loess hillslopes is still not clear. Taking the typical forage species (Coreopsis) in semiarid areas as subject, this study quantified the effects of canopies and roots on controlling slope runoff and erosion. A series of field experiments were conducted in a loess hilly region of China. Field plots (5 m length, 2 m width, 25 degrees slope gradient) constructed with three ground covers (bare soil; Coreopsis with intact grass; only roots of Coreopsis), were applied with simultaneous simulated rainfall (60 mm h(-)(1)) and upslope inflow (10, 30, 50, 70, 90 L min(-)(1)). The results showed that compared with bare soil, intact grass significantly reduced runoff and soil loss rates by 16.6% and 62.4% on average, and decreased soil erodibility parameter by 66.3%. As inflow rate increased, the reductions in runoff and soil loss rates increased from 2.93 to 14.00 L min(-)(1) and 35.11 to 121.96 g m(-)(2) min(-)(1), respectively. Canopies relatively contributed 66.7% to lowering flow velocity, turbulence, weakening erosive force and increasing hydraulic resistance. Roots played a predominant role in reducing soil loss and enhancing soil anti-erodibility, with relative contributions of 78.8% and 73.8%. Furthermore, the maximum erosion depth reduced by Coreopsis was at the upper slope section which was previously eroded the most. These results demonstrated the efficiency of Coreopsis cover in controlling runoff and erosion on steep loess slopes, especially under large inflow rates and at upper slope sections. We suggest protecting Coreopsis with intact grass at upper slope sections, while the aboveground grass biomass can be used for grazing or harvesting at middle and lower slope sections, with roots reserved.
期刊：
FRONTIERS IN PLANT SCIENCE,2023年14:1266801 ISSN：1664-462X
通讯作者：
Li, XR
作者机构：
[Mo, Yunhua; Fu, Yongshuo] Beijing Normal Univ, Coll Water Sci, Beijing, Peoples R China.;[Guo, Yahui; Li, Xiran] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
通讯机构：
[Li, XR ] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
关键词：
PEP725;SSP scenarios;climate change;future prediction;spring phenological model
摘要：
Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology.
通讯机构：
[Xu, LL ] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
关键词：
global dryland;land cover change;CCILC;remote sensing;multi-indices classifiers;sustainable global ecosystems
摘要：
The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.
期刊：
ISPRS International Journal of Geo-Information,2023年12(1):3- ISSN：2220-9964
通讯作者：
Hui Tang
作者机构：
[Tang, Hui; Xu, Tao; Xiong, Yajun] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Tang, Hui] Hunan City Univ, Sch Architecture & Urban Planning, Yiyang 413000, Peoples R China.
通讯机构：
[Hui Tang] S;School of Architecture and Urban Planning, Hunan City University, Yiyang 413000, China<&wdkj&>College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
Yellow River Basin urban agglomeration;high-speed railway access pattern;overlapping community model;spatial overlap delineation;overlapping space identification
摘要：
With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR access time in the Yellow River Basin to comparatively analyze the differences in HSR access in the urban agglomeration in the Yellow River Basin, and using the 3 h HSR access to central cities as the background to conduct regional division and overlapping space identification through cross-regional economic links, before finally selecting the overlapping city of Changzhi for long-term space development strategic planning. The main conclusions were as follows: First, the low-value area of HSR travel time in the Yellow River Basin urban agglomerations was biased toward the center of the urban agglomerations, while the peripheral areas were relatively high-value travel traffic circles, and the HSR travel time showed a circular spatial pattern characteristic of continuous expansion from the center to the peripheral areas. Four urban agglomerations in the upper reaches of the city achieved a 2 h access pattern within the urban agglomeration, whereas three urban agglomerations in the middle and lower reaches of the city only reached the 2 h access level in the center. Second, the Yellow River Basin was divided into six community spaces using the SLPA model based on the economic linkage between the central city and other cities, which were filtered by the 3 h access time from the central city to each city for HSR travel. Three of the six communities produced overlapping spaces, i.e., Community 3 and Community 4 produced overlapping spaces containing Linfen, Community 3 and Community 5 produced overlapping spaces containing Changzhi, Handan, and Xingtai, and Community 4 and Community 5 produced overlapping spaces containing Yuncheng and Sanmenxia. Third, the overlapping space of Changzhi City was selected as a case study for a visionary strategic planning outlook. Combining the geographic location characteristics and future development opportunities of Changzhi, we can try to transform a pass-through node like Changzhi into a hub node in the future, strengthening the gateway status and expanding the hinterland. According to the results of the research and analysis, policymakers can try to implement the expansion and renovation of HSR trunk lines, break the transportation bottlenecks in less developed areas, improve the coverage of the HSR network, and establish a "cross-urban agglomeration" cooperation and coordination mechanism.
作者机构：
[Fang, Linchuan; Qiu, Tianyi] Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Shaanxi, Peoples R China.;[Yu, Jialuo] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modelling, Beijing 100101, Peoples R China.;[He, Liyuan] San Diego State Univ, Biol Dept, San Diego, CA USA.;[Liu, Ji] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Zhao, Shuling; Duan, Chengjiao; Fang, Linchuan; Cui, Qingliang] Inst Soil & Water Conservat CAS & MWR, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Peoples R China.
通讯机构：
[Linchuan Fang] C;College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China<&wdkj&>State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation CAS and MWR, Yangling 712100, China<&wdkj&>CAS Center for Excellence in Quaternary Science and Global Change, Chinese Academy of Sciences, Xi’an 710061, China
关键词：
Ecological stoichiometry;Phosphorus utilization;Plant–microbe interaction;Resource imbalance;Rhizosphere effect;Slope position
摘要：
Spring phenology is a critical indicator to characterize vegetation dynamics and their responses to climate change. Spring phenology on the Tibetan Plateau (TP) has received extensive attentions as it has experienced one of the most rapid warmings. Warming-induced advancement of spring phenology has been revealed by many studies, however, the underlying mechanisms remain obscure. In this article, we derived the start of growing season (SOS) from the satellite solar-induced chlorophyll fluorescence (SIF) and investigated the spatial and temporal variations of SOS over grasslands on the TP during 2001-2020. The temperature sensitivity (St) of SOS was then analyzed, i.e., the slope of a linear regression between the advanced SOS and preseason air temperature. Results showed an average advanced trend of 0.29 days per decade of SOS, although not statistically significant. Spatially, grasslands in eastern TP showed an earlier trend of SOS whilst those in western TP showed a later trend of SOS. The spatial distribution of St was much more affected by precipitation and air temperature, i.e., a 1 mm decrease of precipitation and 1 degrees C warming incur a decrease in St of 0.02 and 0.54 day/degrees C, respectively. Temporally, St showed a significant decrease with an average speed of 0.14 day/degrees C per year during 2001-2020, and the climate controllers show a high spatial heterogeneity. These findings improved our understanding of grasslands spring phenology responses to warming and help us clarify future global water and energy cycles.
作者机构：
[Zhang, Jing; Liu, Zunchi; Zhang, Xuan; Mo, Yunhua; Fu, Yongshuo] Beijing Normal Univ, Coll Water Sci, Beijing 100875, Peoples R China.;[Hao, Fanghua] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Fu, Yongshuo] Univ Antwerp, Dept Biol, Plants & Ecosyst, B-2610 Antwerp, Belgium.
通讯机构：
[Yongshuo Fu] C;College of Water Sciences, Beijing Normal University, Beijing 100875, China<&wdkj&>Plants and Ecosystems, Department of Biology, University of Antwerp, 2610 Wilrijk, Belgium<&wdkj&>Author to whom correspondence should be addressed.
关键词：
spring phenology;extreme climate events;remote sensing;partial correlation analysis;all-subsets regression
摘要：
The response of vegetation spring phenology to climate warming has received extensive attention. However, there are few studies on the response of vegetation spring phenology to extreme climate events. In this study, we determined the start of the growing season (SOS) for three vegetation types in temperate China from 1982 to 2015 using the Global Inventory Modeling and Mapping Study's third-generation normalized difference vegetation index and estimated 25 extreme climate events. We analyzed the temporal trends of the SOS and extreme climate events and quantified the relationships between the SOS and extreme climate events using all-subsets regression methods. We found that the SOS was significantly advanced, with an average rate of 0.97 days per decade in China over the study period. Interestingly, we found that the SOS was mainly associated with temperature extremes rather than extreme precipitation events. The SOS was mainly influenced by the frost days (FD, r = 0.83) and mean daily minimum temperature (TMINMEAN, r = 0.34) for all three vegetation types. However, the dominant influencing factors were vegetation-type-specific. For mixed forests, the SOS was most influenced by TMINMEAN (r = 0.32), while for grasslands and barren or sparsely vegetated land, the SOS was most influenced by FD (r > 0.8). Our results show that spring phenology was substantially affected by extreme climate events but mainly by extreme temperature events rather than precipitation events, and that low temperature extremes likely drive spring phenology.
作者机构：
[Liu, Yi-li; Luo, Li; Luo, Jia-Wei; Guo, Wen-Zhao; Wang, Shao-Kun] Northwest Agr & Forestry Univ, Coll Water Resources & Architectural Engn, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Peoples R China.;[Luo, Li; Guo, Wen-Zhao] Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Prot, Chengdu 610059, Peoples R China.;[Tian, Pei] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
通讯机构：
[Pei Tian] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, 430079, China
摘要：
Currently, the vegetation has recovered well in most areas of the Loess Plateau in China, and soil erosion has significantly decreased. However, the heavy rainfall event in July 2018 triggered many instances of a unique type of loess landslides (i.e., slide-flows) on the gully-slopes with vegetation recovery in the Nanxiaohegou Basin on the Loess Plateau. This rainfall event was unusual and was a persistent heavy rainfall. The accumulated rainfall from 24 June to 10 July was 232.2 mm, which comprised 42% of the mean annual rainfall. A loess slide-flow is characterized by combining two movement types of the slide and flow. The loess slide-flows first slide on the gully-slopes and then turn into long run-out earthflows moving downstream, delivering vast amounts of sediment to the river. The average landslide erosion rates were 110.8-134.9 kg/m2. These loess slide-flows generally did not occur individually but in groups, which were characterized by large numbers, high density, small scale, and shallow depth. The changes of vegetation characteristics and soil characteristics both had a significant impact on the scale of the landslides. Grass with high coverage had an adverse effect on the occurrence of shallow landslides. The adverse hydrological effects of the plant may offset the weak root reinforcement. Loess slide-flows have become a new geological hazard and erosion process on the Loess Plateau. Loess slide-flows are a prominent ecological and environmental problem after vegetation restoration, and more attention should be paid to loess slide-flows in the future. (c) 2022 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
摘要：
The role of national dietary guidelines in realizing sustainable diet is noteworthy. Exploring the evolution, characteristics and adjustment of Chinese dietary guidelines can provide important inspiration for global sustainable diet adjustment. Here, we explored the differences between the dietary structure and dietary guidelines, and quantified the environmental impact of Chinese dietary guidelines using the food carbon footprint, water footprint and land demand. The results showed that Chinese dietary guidelines changed from survival to nutrition balance. In contrast, Chinese dietary guidelines run counter to the increasing consumption of animal foods in the current dietary structure, which can improve the health of the nation to some extent. In terms of environmental impact, if adjusted to the dietary guidelines, food carbon emissions will increase by 9%, and water-land resources consumption increased by 56% and 87%, respectively. In addition, the environmental impact of Chinese dietary guidelines is also higher compared to the three global dietary patterns, mainly due to the increase in animal foods consumption, especially meat. Therefore, we proposed that the new Chinese dietary guidelines should optimize the dietary structure and appropriately reduce meat consumption to achieve a win-win situation of health and environmental sustainability. However, we didn't take into account the uncertainty of data sources, nevertheless, this study analyzed the rationality of Chinese dietary guidelines from a systematic perspective, which can contribute to the achievement of global sustainable diets. In addition, China's experience and lessons can also provide inspiration and reference for developing countries.
期刊：
Computers & Chemical Engineering,2023年176:108313 ISSN：0098-1354
通讯作者：
Shen, DT
作者机构：
[Hu, Zukang] Hohai Univ, Coll Comp & Informat, Nanjing, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.;[Chen, Wenlong] Jiangsu Prov Planning & Design Grp, Nanjing, Peoples R China.
通讯机构：
[Shen, DT ] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.
关键词：
Deep learning;Domain adaptation;Multi-scale feature extraction;Pipe burst location
摘要：
This study proposes a domain adaption method for pipe burst location based on deep learning. Multi-scale feature extractors are designed to extract pipe burst features, then three classifiers are trained by pipe burst features with different scales, and adversarial training is introduced during the edge domain fusion. Finally, the probability ranking of each pipeline is obtained according to the classification results of the three classifiers. In this study, a Net3 pipe network hydraulic model was used as an example to carry out related research. The pressure monitoring data of three sensors were used to train and test the model, and different scenarios of one, two and three sensors were considered at the same time. The results showed that the overall prediction accuracy of the three scenarios was over 90% when considering the five pipelines with the highest pipe burst probability.
通讯机构：
[Xu, BD ] H;Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.
关键词：
Vegetation mapping;Reflectivity;Estimation;Indexes;Analytical models;Mathematical models;Satellites;Chlorophyll-insensitive vegetation index (CIVI);leaf area index (LAI);PROSAIL;red-edge;sensitivity analysis;Sentinel-2
摘要：
Leaf area index (LAI) is an important indicator for monitoring vegetation growth and estimating crop yields. The empirical-based model using vegetation indices (VIs) is an effective method for LAI estimation at the regional scale. However, due to the complexity of canopy radiation interaction processes, the leaf chlorophyll content ( $C_{ab}$ ) and saturation effects on canopy reflectance restrict the accuracy of VI-based LAI retrieval. To address these limitations, we propose a novel chlorophyll-insensitive VI (CIVI) using red, red-edge, and near-infrared (NIR) bands to improve regional LAI mapping. The CIVI was developed based on the sensitivity analysis of red-edge band reflectance to LAI and $C_{ab}$ using the simulation dataset from the PROSAIL model. Then, the performance of CIVI was carefully evaluated from two aspects: the sensitivity of VI to LAI and other parameters and the accuracy of LAI estimates using different VIs over homogeneous (cropland and grassland) and nonhomogeneous (forest) biome canopies. The results suggested that CIVI can capture LAI variations well while remaining insensitive to $C_{ab}$ variations. Additionally, the sensitivity of CIVI to other vegetation biochemical and biophysical parameters did not increase significantly compared to that of other VIs. Furthermore, CIVI exhibited the best performance of LAI retrievals over both homogeneous ( $R^{2}=0.938$ , RMSE = 0.447, and rRMSE = 21.3%) and nonhomogenous ( $R^{2}=0.635$ , RMSE = 0.693, and rRMSE = 14.0%) canopies among all selected VIs, especially for the high LAI. Our results indicated that the developed CIVI incorporating red-edge bands with a suitable formula can effectively reduce the $C_{ab}$ and saturation effects, which is promising for improving VI-based LAI estimation.
摘要：
Continuous population growth, global warming, extreme weather and local wars pose considerable challenges to agricultural production. Excessive agricultural intensification may lead to serious environmental problems such as water resources depletion and non-point source pollution. Sustainable agricultural intensification is a potential solution, and cropping intensity and its spatio-temporal patterns are of great significance to the planet hearth, human well-being and sustainable development of agriculture. However, traditional methods for cropping in-tensity mapping lack the deep analysis of crop phenology and failure to consider the spatial detail, spatial coverage and time continuity simultaneously. Existing cropping intensity indicators, such as cropping frequency or multiple crop index, have their limitations in China because of its diverse topographies and fragmented landscapes. In actual fact, few indicators for fine cropping intensity have been developed that address this problem. Time series vegetation index data contain meaningful vegetation phenology information. Information mining on these data can provide new insights for cropping intensity mapping. In this study, the concept of systematic mapping was put forward to measure cropping intensity with a fresh perspective. A new phenology-based cropping intensity index (PCII) was proposed to reach the goal of "from frequency to intensity" in cropping intensity mapping. Through thoroughly exploring the temporal spectral characteristics of croplands and considering the crop pattern as a whole, PCII set up the mapping between cropping intensity and land-cover types. The method can accurately map cropping intensity even though the study spanned a vast area, and can handle the effects of geographical differentiation caused by vegetation phenology differences. It can also pre-cisely measure cropping intensity at the abundance level and reflect the heterogeneity within a pixel. It's a novel method for annual fine cropping intensity mapping at large-scale and long-term.
期刊：
Journal of Cleaner Production,2022年367:132922 ISSN：0959-6526
通讯作者：
Yong Zhou
作者机构：
[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi; Liu, Yujie] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi; Liu, Yujie] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Yong Zhou] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, 430079, China<&wdkj&>The College of Urban & Environmental Sciences, Central China Normal University, Wuhan, 430079, China
关键词：
Soil heavy metal;Sentinel-2A images;Estimation mechanism;Dimidiate pixel model;Random forest
摘要：
Rapid and accurate predictions of heavy metal contents in farmland are of great significance to ensure the safety of agricultural products and maintain ecosystem balance. Combining multispectral images and chemometric modeling provides a feasible means of estimating cadmium (Cd) and lead (Pb) contents in farmland. We collected 640 samples from the surface soils of farmland in Xiangzhou District, Hubei Province, China. The Cd, Pb, soil organic matter (SOM), pH, and Fe of the soil samples were measured in a laboratory. In this study, the dimidiate pixel model was used to process remote sensing images. In particular, we used random forest (RF) to screen the best spectral indices for use as input variables. Partial least squares regression (PLSR), backward propagation neural network (BPNN), and RF were used to calibrate the spectral data with Cd and Pb contents, and the optimal model was used for the regional mapping of soil Cd and Pb contents. Additionally, we explored the potential of using spectral estimation mechanisms to estimate Cd and Pb contents. The mechanism for estimating Cd and Pb contents with multispectral images depended mainly on the covariance of Cd and Pb contents with that of SOM. For both Cd and Pb estimations, the double-date image estimation model performed better than the single-date image estimation model, and the unmixed image estimation model was more accurate than the original image estimation model. Overall, the estimation model using the best spectral indices as input variables performed better than the model using full-band data as input variables. The RF model outperformed the PLSR and BPNN models in all cases. Relatively satisfactory estimates of Cd and Pb contents in farmland of the study area (maximum R-val(2) (determination coefficient of the validation set) = 0.60 for Cd and R-val(2) = 0.63 for Pb) were obtained. Our results show that areas of farmland contaminated by Cd and Pb throughout the study area have increased and that contamination levels have worsened since 1990. In addition, the spatial patterns of Cd and Pb contents in farmland throughout the study area were analyzed and validated using field survey results. The results of the study provide a theoretical basis and methodological reference for the rapid prediction of Cd and Pb contents in regional farmland.
