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.
通讯机构:
[Yi, J.] H;Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, China
关键词:
nitrogen balance;nitrogen leaching;nitrogen uptake;nitrogen use efficiency;paddy yield
摘要:
Nitrogen loss from paddy fields contributes to most of the nitrogen pollution load in the Ningxia Yellow River irrigation area, threatening the water quality of the Yellow River. Consequently, optimizing the nitrogen management practices in this area is essential, which can maintain paddy grain productivity and reduce nitrogen loss simultaneously. Five treatments with different nitrogen application rates and nitrogen fertilizer types were set in this study, including conventional urea application with zero nitrogen application rate (CK, 0 kg hm(-2)), nitrogen expert-based fertilization application strategy (NE, 210 kg hm(-2)), optimized nitrogen fertilizer application strategy recommended by local government (OPT, 240 kg hm(-2)), and farmer's experience-based nitrogen fertilizer application strategy (FP, 300 kg hm(-2)), and controlled-release urea application (CRU, 180 kg hm(-2)). The data from one growth season field experiment in 2021 revealed the dynamics of nitrogen concentration, paddy yield and its nitrogen uptake characteristic, and nitrogen balance in the paddy field under different nitrogen application practices. Most nitrogen leaching was observed during the seedling and tillering stages in the form of nitrate nitrogen (NO3-N). Compared with the FP, the CRU and OPT significantly reduced the nitrogen concentrations of total nitrogen (TN), ammonium nitrogen (NH4+-N), and NO3-N in the surface and soil water and reduced the nitrogen leaching at 100 cm soil depth. Meanwhile, the paddy grain yield in CRU (7737 kg hm(-2)) and OPT (7379 kg hm(-2)) was not significantly decreased compared with FP (7918 kg hm(-2)), even though the nitrogen uptake by grain and straw was higher in FP (135 kg hm(-2)) than in other treatments (52.10 similar to 126.40 kg hm(-2)). However, the grain yield in NE (6972 kg hm(-2)) was decreased compared with the FP. The differences in grain yield among these treatments were mainly attributed to the ear number and grain number changes. Also, the highest nitrogen use efficiency (40.14%), apparent nitrogen efficiency (19.53 kg kg(-1)), and nitrogen partial productivity (43.98 kg kg(-1)) were identified in CRU than in other treatments. Considering increased grain yield and reducing nitrogen loss in the paddy field simultaneously, the treatments of CRU (i.e., 180 kg hm(-2) nitrogen application rate with controlled-release urea) and OPT (i.e., 240 kg hm(-2) nitrogen application rate with conventional urea) were recommended for nitrogen fertilizer application in the study area.
摘要:
Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD's performance. With this insight, a Temporal-Transform Module (TTM) is designed to capture change relationships across temporal dimensions. TTM selectively aggregates features across all temporal images, enhancing the unique features of each temporal image at distinct pixels. Moreover, we build a Temporal-Transform Network (TTNet) for SCD, comprising two semantic segmentation branches and a binary change detection branch. TTM is embedded into the decoder of each semantic segmentation branch, thus enabling TTNet to obtain better land cover classification results. Experimental results on the SECOND dataset show that TTNet achieves enhanced performance when compared to other benchmark methods in the SCD task. In particular, TTNet elevates mIoU accuracy by a minimum of 1.5% in the SCD task and 3.1% in the semantic segmentation task.
摘要:
Abstract: Within the context of the “30·60 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. Keywords: carbon emissions; urbanization level; geographically weighted regression model; multiscale analysis; China
摘要:
Abstract: 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. Keywords: drainage patterns; pattern recognition; river network; binary tree; graph convolution
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.
作者机构:
[Ma, Li; Ni, Yongxin; Wang, Jianwei; Lv, Xizhi; Zhang, Qiufen] Yellow River Inst Hydraul Res, Henan Key Lab Yellow Basin Ecol Protect & Restorat, Zhengzhou 450003, Peoples R China.;[Zhang, Xin; Qin, TL; Qin, Tianling] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.;[Ni, Yongxin] Hohai Univ, Coll Hydrol & Water Recourses, Nanjing 210098, Peoples R China.;[Nie, Hanjiang] Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Nie, Hanjiang] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Qin, TL ] C;China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China.
关键词:
distributed hydrological model;water and land resources;evapotranspiration;runoff coefficient;Sihe River Basin
摘要:
Abstract: Conflicts between humans and land use in the process of using water and conflicts between humans and water resources in the process of using land have led to an imbalance between natural ecosystems and socio-economic systems. It is difficult to understand the impact of the processes of water production and consumption on land patches and their ecological effects. A grid-type, basin-distributed hydrological model was established in this study, which was based on land-use units and coupled with groundwater modules to simulate the water production and consumption processes in different units. By combining land use and net primary productivity, the runoff coefficient and the water use efficiency (NPP/ET) of different land units were used as indicators to characterize the interaction between water and land resources. The results showed that the average runoff coefficients of cultivated land, forest land and grassland were 0.7, 0.5 and 0.9, respectively. Moreover, the average runoff coefficients of hills, plains and basins were 0.7, 0.7 and 0.8, respectively. The NPP produced by the average unit, evapotranspiration, in cultivated land, forest land and grassland was 7 (gC/(m2•a))/mm, 0.7 (gC/(m2•a))/mm and 0.2 (gC/(m2•a))/mm, respectively. These results provide quantitative scientific and technological support in favor of the comprehensive ecological management of river basins. Keywords: distributed hydrological model; water and land resources; evapotranspiration; runoff coefficient; Sihe River Basin
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:
Urban functional zone mapping;SALT features;Ensemble learning;Volunteered geographic information
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:
[Liu, Jingyi] Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China;College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China;[He, Nan; Wang, Li; Zuo, Qian; Zhou, Yong; Li, Qing] Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, 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&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
摘要:
Abstract: Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. The impact of LUCC on ESs in the Qingjiang Watershed (QJW), an ecologically sensitive area, and LUCC’s role in future ESs under different land use scenarios are crucial to promoting ecological conservation and land use management. This paper assessed water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) using the InVEST model, and their responses to LUCC in the QJW from 1990 to 2018 using the geodetector and multiscale geographically weighted regression. We predicted land use patterns using the Logistic–CA–Markov model and their effects on ESs in 2034 under business as usual (BAU), ecological land protection (ELP), arable land protection (ALP) and ecological economic construction (EEC) scenarios. From 1990 to 2018, the area of cropland and woodland decreased by 28.3 and 138.17 km2, respectively, while the built-up land increased by 96.65 km2. The WY increased by 18.92%, while the SC, CS and HQ decreased by 26.94%, 1.05% and 0.4%, respectively. The increase in the arable land area led to a increase in WY, and the decrease in forest land and the increase in construction land led to a decrease in SC, CS and HQ. In addition to being influenced by land use patterns, WY and SC were influenced mainly by meteorological and topographical factors, respectively. In 2034, there was an obvious spatial growth conflict between cropland and construction land, especially in the area centered on Lichuan, Enshi and Yidu counties. Under four scenarios, WY and SC were ranked ALP > BAU > EEC > ELP, while CS and HQ were ranked ELP > EEC > BAU > ALP. Considering the sustainable eco-socio-economic development of the QJW, the EEC scenario can be chosen as a future development plan. These results can indicate how to rationally improve the supply of watershed ESs through land resource allocation, promoting sustainable regional development in mountainous watershed areas. Keywords: ecosystem services; LUCC; multi-scenario prediction; InVEST model; Logistic–CA–Markov model; the Qingjiang Watershed
摘要:
Fractional vegetation cover (FVC) has a significant role in indicating changes in ecosystems and is useful for simulating growth processes and modeling land surfaces. The fine-resolution FVC products represent detailed vegetation cover information within fine grids. However, the long revisit cycle of satellites with fine-resolution sensors and cloud contamination has resulted in poor spatial and temporal continuity. In this study, we propose to derive a spatially and temporally continuous FVC dataset by comparing multiple methods, including the data-fusion method (STARFM), curve-fitting reconstruction (S-G filtering), and deep learning prediction (Bi-LSTM). By combining Landsat and Sentinel-2 data, the integrated FVC was used to construct the initial input of fine-resolution FVC with gaps. The results showed that the FVC of gaps were estimated and time-series FVC was reconstructed. The Bi-LSTM method was the most effective and achieved the highest accuracy (R-2 = 0.857), followed by the data-fusion method (R-2 = 0.709) and curve-fitting method (R-2 = 0.705), and the optimal time step was 3. The inclusion of relevant variables in the Bi-LSTM model, including LAI, albedo, and FAPAR derived from coarse-resolution products, further reduced the RMSE from 5.022 to 2.797. By applying the optimized Bi-LSTM model to Hubei Province, a time series 30 m FVC dataset was generated, characterized by a spatial and temporal continuity. In terms of the major vegetation types in Hubei (e.g., evergreen and deciduous forests, grass, and cropland), the seasonal trends as well as the spatial details were captured by the reconstructed 30 m FVC. It was concluded that the proposed method was applicable to reconstruct the time-series FVC over a large spatial scale, and the produced fine-resolution dataset can support the data needed by many Earth system science studies.