期刊：
Environmental Science and Pollution Research,2023年30(42):96329-96349 ISSN：0944-1344
通讯作者：
Yu, J
作者机构：
[Li, Yimin; Nie, Yan; Yin, Chen; Zhou, Yong; Yu, Lei] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Li, Yimin; Qin, Hong; Nie, Yan; Yin, Chen; Zhou, Yong; Yu, Lei] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Yu, J; Yu, Jing] Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan 430062, Peoples R China.
通讯机构：
[Yu, J ] H;Hubei Univ, Hubei Key Lab Reg Dev & Environm Response, Wuhan 430062, Peoples R China.
关键词：
Arable land multifunction;Functional trade-offs;Root mean square deviation method;Ecological compensation;The West Mountain Regions of Hubei Province
摘要：
Tropical deforestation frontiers continue to expand at alarming rates, but their fine-scale temporal patterns (e.g., start timing, patch forming speed, temporal clustering within a year) remain unresolved. Previous deforestation monitoring focus on the annual dynamics or the timely identification of deforestation activities; however, improved methods are needed for accurate mapping of deforestation patches at higher temporal resolution (i.e., sub-monthly) to better reveal their fine-scale temporal dynamics. We propose an optimization method inte-grating the spatial and temporal context information to improve the sub-monthly deforestation mapping from Sentinel-1 (S1) SAR data: (1) a deep learning-based spatial optimization to suppress speckle noises; (2) a Bayesian-based temporal optimization to meaningfully combine deforestations detected in the S1 data streams. The proposed method was comprehensively assessed in three deforestation hotspots in Brazil -Acre, Rondo<SIC>nia and Par ' a, for the whole year of 2019. Results showed: (1) the spatial optimization alone can improve the ac-curacies of deforestation mapping from single-date S1 images for up to 7.3%; (2) the Bayesian-based temporal optimization improved the deforestation mapping accuracies for about 5.9% after three post-deforestation S1 observations (about 18 +/- 3 days after deforestation); (3) combining the spatial and temporal optimizations achieved the highest classification accuracies (overall accuracy of 91.0%, IoU of 89.1%), surpassing the baseline monthly composite method (overall accuracy of 89.3%, IoU of 87.3%) within fewer observation days. Further frontier analysis based on these sub-monthly results showed varying distributions of patch size and forming speed in these three study sites during the wet and dry seasons. The temporal clustering of deforestation also differed among sites during 2019: deforestations in Rondo<SIC>nia were most concentrated during the dry season (CV = 1.1), followed by Par ' a (CV = 0.75), while Acre showed more even temporal distribution in deforestation year-round (CV = 0.57). The proposed method thus can be used for revealing unprecedented temporal details regarding tropical deforestation frontiers, which is critical for evaluating the ecological consequences and formulating scientific conservation strategies.
期刊：
Computers, Environment and Urban Systems,2023年100:101921 ISSN：0198-9715
通讯作者：
Lin, Anqi(linanqi@mails.ccnu.edu.cn)
作者机构：
[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, 152 Luoyu Rd, Wuhan, Peoples R China.;[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan, Peoples R China.;[Olteanu-Raimond, Ana-Maria] Univ Gustave Eiffel, LASTIG, ENSG, IGN, St Mande, France.;[Lin, Anqi] Cent China Normal Univ, Room 318,10 Bldg,152 Luoyu Rd, Wuhan, Peoples R China.
通讯机构：
[Anqi Lin] H;Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China
关键词：
Ensemble learning;SALT features;Urban functional zone mapping;Volunteered geographic information
摘要：
The global shipping industry faces increasingly complex safety challenges due to the rapid growth of international maritime trade. This study develops a novel framework that combines spatial density analysis and machine learning (i.e., extreme gradient boosting model) to investigate the evolutionary patterns of global maritime accidents during 2001-2020 from both spatial and temporal dimensions, and then identifies key environmental risk factors affecting maritime safety. The results show that the number of global maritime accidents exhibits fluctuations between 2001 and 2019, with a significant decrease observed in 2020. Furthermore, the distribution of global maritime accidents shows significant spatial variation over different time periods. Denmark's sea areas have high accident rates between 2001 and 2005, while concentrated accidents are observed in the seas around the United Kingdom, Denmark, and China between 2006 and 2010. From 2011 to 2015, Europe's accident-prone areas increase, but fewer accidents are reported along China's east coast. The Strait of Malacca is also an accident-prone area from 2016 to 2020. In addition, wave height, sea surface temperature, wind speed, water depth, and precipitation are identified as key environmental risk factors affecting maritime safety. These findings can inform strategies and mitigation plans to improve navigational safety in the global shipping industry.
期刊：
International Journal of Environmental Research and Public Health,2023年20(1):363- ISSN：1661-7827
通讯作者：
Zhenwei Wang
作者机构：
[Li, Weisong] Collaborat Innovat Ctr Emiss Trading Syst Coconstr, Wuhan 430205, Peoples R China.;[Li, Weisong] Hubei Univ Econ, Wuhan 430205, Peoples R China.;[Wang, Zhenwei] Hubei Univ, Coll Publ Adm, Wuhan 430062, Peoples R China.;[Mao, Zhibin] Hubei Univ Econ, Expt Teaching Ctr, Wuhan 430205, Peoples R China.;[Cui, Jiaxing] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Zhenwei Wang] C;College of Public Administration, Hubei University, Wuhan 430062, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Within the context of the "30 center dot 60 dual carbon" goal, China's low-carbon sustainable development is affected by a series of environmental problems caused by rapid urbanization. Revealing the impacts of urbanization on carbon emissions (CEs) is conducive to low-carbon city construction and green transformation, attracting the attention of scholars worldwide. The research is rich concerning the impacts of urbanization on CEs but lacking in studies on their spatial dependence and heterogeneity at multiple different scales, especially in areas with important ecological statuses, such as the Han River Ecological Economic Belt (HREEB) in China. To address these gaps, this study first constructed an urbanization level (UL) measurement method. Then, using a bivariate spatial autocorrelation analysis and geographically weighted regression model, the spatial relationships between UL and CEs from 2000 to 2020 were investigated from a multiscale perspective. The results were shown as follows. The total CEs in the HREEB witnessed an upsurge in the past two decades, which was mainly dispersed in the central urban areas of the HREEB. The ULs in different regions of the HREEB varied evidently, with high levels in the east and low levels in the central and western regions, while the overall UL in 2020 was higher than that in 2000, regardless of the research scale. During the study period, there was a significant, positive spatial autocorrelation between UL and CEs, and similar spatial distribution characteristics of the bivariate spatial autocorrelation between CEs and UL at different times, and different scales were observed. UL impacted CEs positively, but the impacts varied at different grid scales during the study period. The regression coefficients in 2020 were higher than those in 2000, but the spatial distribution was more scattered, and more detailed information was provided at the 5 km grid scale than at the 10 km grid scale. The findings of this research can advance policy enlightenment for low-carbon city construction and green transformation in HREEB and provide a reference for CE reduction in other similar regions of the world.
期刊：
Journal of Soils and Sediments,2023年23(2):880-890 ISSN：1439-0108
通讯作者：
Xiufu Shuai
作者机构：
[Shuai, Xiufu] Cent China Normal Univ, Sch Urban & Environm Sci, Hubei Prov Key Lab Geog Proc Anal & Simulat, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xiufu Shuai] H;Hubei Province Key Lab. for Geographical Process Analysis and Simulation, School of Urban and Environmental Science, Central China Normal Univ, Wuhan, China
摘要：
Purpose Triple-layer model (TLM) is distinct from other surface complexation models (SCMs) with the charged beta-layer between solid surface and diffuse layer. However, its structure of electrical double layer, i.e., three capacitors connected in series, produced an uncharged beta-layer according to the rule of capacitors in the electrical circuit theory. The objective of this study was to modify TLM with the development of a new structure of electrical double layer and mathematical models for the charge-potential relationships. Methods The rule of capacitors in the electrical circuit theory was used to modify the electrical double layer in TLM. Published acid-based titration experiments on goethite in KNO3 solution by Yates and Healy (J Colloid Interface Sci 52:222-228, 1975) was used to demonstrate the modified TLM. Simulation study of the modified TLM for goethite was carried out by changing pH from 4.0 to 10.0 and ionic strength of KNO3 solution from 0.001 to 0.100 mol.l(-1). Results The finite size of ions in aqueous solution determined the parallel connection of the two capacitors, which were described by the constant capacitance model (CCM) and the diffuse layer model (DLM). A new concept termed as ion size factor delta, which was governed by the radius r of hydrated ion, was proposed to quantify the percentages of surface area occupied by the CCM and DLM capacitors. A new characteristic relationship of the modified TLM was derived to be a linear relationship between net surface charge and square root of ionic strength when the surface potential was small. The experimental results verified the characteristic relationship, and the ion size factor was validated by the success in estimating the dielectric constant of the CCM capacitor and the radii of hydrated ions (K+ and NO3-). The CCM capacitor occupied 33.8% of the area of goethite surface. Simulation results showed that substantial amount of charge was at the compact layer, and it contributed 14.6% to 74.4% of the net surface charge. Conclusion New electrical double layer with structure of connection of the two capacitors in parallel eliminated the internal flaw of the classical TLM, modified the classical TLM into a general model which unified CCM and DLM, and supported the core of the classical TLM (i.e., the charged compact layer and the diffuse layer).
摘要：
With ongoing climate change, aridity is increasing worldwide, affecting biodiversity and ecosystem function in drylands. However, how the depth-profile microbial community structure and metabolic limitations change along aridity gradients are still poorly explored. Here, 16S rRNA and ITS amplicon sequencing and ecoenzymatic stoichiometry analysis were used to investigate both bacterial and fungal diversities and resource limitations in 1 m depth profiles across a wide aridity gradient (0.51-0.78) in a semiarid region. Results showed a sharp decrease in microbial diversity with soil depth, accompanied by an increase in microbial phosphorus (P) vs. N (nitrogen) limitation and a decrease in microbial carbon (C) vs. nutrient limitation. Aridity led to a strong shift in microbial community composition, but aridity has a threshold effect on microbial resource limitation through impacts on soil pH and C/P or N/P. When the aridity threshold (1-precipitation/evapotranspiration) exceeds 0.65, relationship between aridity and microbial resource demand was decoupled; but at aridity threshold = 0.65, microbial relative C limitation and C-acquiring enzyme activity dropped. These results suggest that aridity might have a stronger influence on microbial community composition, than on diversity, shaped by inherent soil biotic factors (i.e., MBC:MBP or MBN:MBP). These findings suggest that soil microbial diversity or enzymatic stoichiometry may be not necessary to mirror changes in water availability in the drylands, while aridity would be well explained by microbial community composition.
作者机构：
[Zhu, Yuanyuan; Ao, Rongjun; Shen, Xue; Zhou, Xiaoqi; Chen, Jing; Aihemaitijiang, Yierfanjiang] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Hubei, Peoples R China.;[Zhu, Yuanyuan; Ao, Rongjun; Shen, Xue; Zhou, Xiaoqi; Chen, Jing; Aihemaitijiang, Yierfanjiang] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Hubei, Peoples R China.
通讯机构：
[Ao, RJ ] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Hubei, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Hubei, Peoples R China.
摘要：
This study introduces the principle of resilience into the study of human settlements. In this study, a comprehensive evaluation model of urban human settlements' resilience based on the provincial region of China was constructed using the Driver-Pressure-State-Impact-Response framework. The spatio-temporal evolution characteristics of urban human settlements' resilience was explored. The influencing factors were analysed by geographical detectors, and the driving mechanism was constructed. Results show that the following. (1) The resilience level of human settlements in China continued to increase, and the resilience level of each province and city changed significantly. The overall clustering effect showed a tendency to fluctuate and weaken. The distribution of cold spot areas became less and less, and the hot spots were moving from northeast China to southeast China. (2) Significant differences existed in the intensity of the impact of different indicators on the resilience system. The value of the impact factor showed an overall upward trend, and the number of key impact factors increased. (3) Improving the ability of scientific and technological innovation, accelerating the transformation and upgrading of the regional economy, increasing the training of talents and making financial inclination in scientific and technological development and industrial pollution control were all important ways for developing and maintaining the resilience of urban human settlements. This study not only introduces a new evaluation of urban human settlements from the perspective of resilience but also explores key impact indices and driving mechanisms, which provides new ideas for studying urban human settlements.
摘要：
Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern recognition still faces challenges due to the complexity and diversity of river networks. To address this issue, this study proposes a river network pattern recognition method based on graph convolutional networks (GCNs), aiming to achieve accurate classification of different river network patterns. We utilize binary trees to construct a hierarchical tree structure based on river reaches and progressively determine the tree hierarchy by identifying the upstream and downstream relationships among river reaches. Based on this representation, input features for the graph convolutional model are extracted from both spatial and geometric perspectives. The effectiveness of the proposed method is validated through classification experiments on four types of vector river network data (dendritic, fan-shaped, trellis, and fan-shaped). The experimental results demonstrate that the proposed method can effectively classify vector river networks, providing strong support for research and applications in related fields.
作者机构：
[Li, Zhuofan] Xinyang Normal Univ, Coll Tourism, Xinyang 464000, Peoples R China.;[Li, Zhuofan] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
关键词：
rural regional system;rural restructuring;rural transformation;Jianghan Plain in China
摘要：
The rural decline accompanying industrialization and urbanization is a lingering puzzle in human society, while promoting rural restructuring and transformation is considered the primary task of contemporary rural development. It is the historical mission of rural geography research in the new era to scientifically understand the characteristics of contemporary rural development and accurately explain the patterns of rural reconstructing and transformation. In this paper, the Jianghan Plain in China is selected for the case study. Characteristic indexes are selected based on the "structure-function" correlation to interpret rural restructuring. Measurement benchmarks are unified through functional value marketization to interpret rural transformation. Multiple statistical analysis is adopted to identify the action paths and decipher the correlation mechanisms. The case study yields the following findings. (1) The rural restructuring on Jianghan Plain has spatial and temporal differences. Rural restructuring has roughly gone through the social restructuring-led, economic restructuring-led, and spatial restructuring-led evolution stages, showing spatially divergent patterns with high rural comprehensive restructuring index (RRC) areas concentrated around the main traffic arteries and linear low RRC areas along the Yangtze River banks. (2) Rural restructuring and transformation on Jianghan Plain show significant correlation effects. During the study period, the rural transformation magnitude (RTM) continues to increase and shows a spatial map similar to that of rural restructuring, with economic-spatial restructuring-led and economic-social restructuring-led as the main modes of rural transformation. (3) The correlation mechanism of rural restructuring and transformation on Jianghan Plain has characteristics typical of less-developed agricultural areas. The economic restructuring led by agricultural land changes and the social restructuring led by rural population outward migration remain the main paths of rural transformation, and the agricultural function still plays an important role in some rural areas. The quantitative measurement of rural region functions in this study need further optimization, and the refinement and accuracy of regional function accounting needs further exploration. The research results are expected to provide a scientific basis for stimulating rural development and promoting sustainable rural development in contemporary developing countries.
