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
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：
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.
期刊：
Critical Reviews in Environmental Science and Technology,2023年53(20):1795-1816 ISSN：1064-3389
通讯作者：
Linchuan Fang
作者机构：
[He, Haoran; Fang, Linchuan; Chao, Herong; Zeng, Yi; Chen, Li; Zhang, Zhiqin] Northwest A&F Univ, Coll Nat Resources & Environm, Yangling, Peoples R China.;[He, Haoran; Duan, Chengjiao; Fang, Linchuan; Chao, Herong; Zeng, Yi; Chen, Li; Zhang, Zhiqin] Inst Soil & Water Conservat CAS & MWR, State Key Lab soil Eros & Dryland Farming Loess Pl, Yangling, Peoples R China.;[Wang, Fayuan] Qingdao Univ Sci & Technol, Coll Environm & Safety Engn, Qingdao, Peoples R China.;[Hu, Weifang] Guangdong Acad Agr Sci, Inst Agr Resources & Environm, Guangzhou, Peoples R China.;[Liu, Ji] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan, Peoples R China.
通讯机构：
[Linchuan Fang] C;College of Natural Resources and Environment, Northwest A&F University, Yangling, 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, China
关键词：
Arbuscular mycorrhizal fungi;bioaccumulation;crop growth;Jörg Rinklebe and Lena Q. Ma;meta-analysis;physiological activities;potentially toxic elements
摘要：
Soil pollution from potentially toxic elements (PTEs) is a serious environmental issue worldwide that affects agricultural safety and human health. Arbuscular mycorrhizal fungi (AMF), as ecosystem engineers, can alleviate PTE toxicity in crop plants. However, the comprehensive effects of AMF on crop performance in PTE-contaminated soils have not yet been recognized globally. Here, a meta-analysis of 153 studies with 3213 individual observations was conducted to evaluate the effects of AMF on the growth and PTE accumulation of five staple crops (wheat, rice, maize, soybean, and sorghum) in contaminated soils. Our results demonstrated that AMF had strong positive effects on the shoot and root biomass. This is because AMF can effectively alleviate oxidative damage induced by PTEs by stimulating photosynthesis, promoting nutrition, and activating non-enzymatic and enzymatic defense systems in crop plants. AMF also decreased shoot PTE accumulation by 23.6% and increased root PTE accumulation by 0.8%, demonstrating that AMF effectively inhibited the PTE transfer and uptake by crop shoot. Meanwhile, AMF-mediated effects on shoot PTE accumulation were weaker in soils with pH > 7.5. Overall, this global survey has essential implications on the ability of AMF to enhance crop performance in PTE-contaminated soils and provides insights into the guidelines for safe agricultural production worldwide.
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.
期刊：
Science of The Total Environment,2023年892:164735 ISSN：0048-9697
通讯作者：
Li, J
作者机构：
[Liu, Qinhuo; Gu, Chenpeng; Dong, Yadong; Zhao, Jing; Li, Jing; Liu, Chang; Mumtaz, Faisal] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.;[Liu, Qinhuo; Gu, Chenpeng; Dong, Yadong; Zhao, Jing; Li, Jing; Liu, Chang; Mumtaz, Faisal] Univ Chinese Acad Sci, Beijing 100049, Peoples R China.;[Gao, Jixi] Minist Ecol & Environm Peoples Republ China, Satellite Applicat Ctr Ecol & Environm, Beijing 100094, Peoples R China.;[Wang, Cong] Cent China Normal Univ, Sch Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
通讯机构：
[Li, J ] C;Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
关键词：
Leaf area index;Eurasian Steppe (EAS);ENSO;Vegetation change
摘要：
As the most influential atmospheric oscillation on Earth, the El Niño/Southern Oscillation (ENSO) can significantly change the surface climate of the tropics and subtropics and affect the high latitudes of northern hemisphere areas through atmospheric teleconnection. The North Atlantic Oscillation (NAO) is the dominant pattern of low-frequency variability in the Northern Hemisphere. As the dominant oscillations in the Northern Hemisphere, the ENSO and NAO have been affecting the giant grassland belt in the world, the Eurasian Steppe (EAS), in recent decades. In this study, the spatio-temporal anomaly patterns of grassland growth in the EAS and their correlations with the ENSO and NAO were investigated using four long-term leaf area index (LAI) and one normalized difference vegetation index (NDVI) remote sensing products from 1982 to 2018. The driving forces of meteorological factors under the ENSO and NAO were analyzed. The results showed that grassland in the EAS has been turning green over the past 36years. Warm ENSO events or positive NAO events accompanied by increased temperature and slightly more precipitation promoted grassland growth, and cold ENSO events or negative NAO events with cooling effects over the whole EAS and uneven precipitation decreased deteriorated the EAS grassland. During the combination of warm ENSO and positive NAO events, a more severe warming effect caused more significant grassland greening. Moreover, the co-occurrence of positive NAO with cold ENSO or warm ENSO with negative NAO kept the characteristic of the decreased temperature and rainfall in cold ENSO or negative NAO events, and deteriorate the grassland more severely.
作者机构：
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;Author to whom correspondence should be addressed.;[Liu, Jingyi; He, Nan; Wang, Li; Zuo, Qian; 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;[Zhou, Yong] 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<&wdkj&>Author to whom correspondence should be addressed.
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<&wdkj&>Author to whom correspondence should be addressed.
摘要：
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.
摘要：
Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July-August) had the lowest PA while winter had the highest (January-February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20-and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice.
摘要：
As a driving force for regional development, innovation holds an increasing position in regional competitiveness, and a reasonable and coordinated innovation network structure can promote high-quality regional development. Utilizing the modified gravity model and social network analysis method, an innovation network composed of 27 cities in the Yangtze River Delta urban agglomeration from 2010 to 2021 was studied. The following conclusions were founded: (1) The innovation development level in the Yangtze River Delta urban agglomeration was constantly improving, and the innovation development level generally showed a spatial pattern of high in the southeast and low in the northwest. (2) The intensity and density of innovation network correlations in urban agglomerations were increasing, and the centrality of network nodes had an obvious hierarchical characteristic. The innovation network had a significant core-periphery spatial structure, with core cities that had higher centrality, such as Shanghai, Nanjing, and Hangzhou, playing the role of "intermediaries" and "bridges", while cities with lower centrality, such as Anhui and cities in northern Jiangsu, generally played the role of "periphery actors" in the network. (3) The spatial correlation network of innovation of the Yangtze River Delta urban agglomeration could be divided into four blocks, namely, main benefit, broker, two-way spillover, and net spillover, and the spillover effect among them had obvious gradient characteristics of hierarchy.
期刊：
European Journal of Agronomy,2023年151 ISSN：1161-0301
通讯作者：
Xu, BD;Hu, Q
作者机构：
[Xu, Baodong; Xu, BD; Zhou, Wei; Cai, Zhiwen] Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Hu, Jie; Zhang, Xinyu; Wei, Haodong; Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China.;[Yang, Jingya] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China.;[Hu, Q; Hu, Qiong] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Xiong, Hang] Huazhong Agr Univ, Coll Econ & Management, Wuhan 430070, Peoples R China.
通讯机构：
[Hu, Q ] C;[Xu, BD ] H;Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.
关键词：
Plastic -mulched citrus index;Spectral separability;Intra-annual and interannual analysis;Spatio-temporal variation;Multi -source remote sensing data
摘要：
The technology of canopy plastic mulching has been widely used in citrus orchards for protecting fruit trees from cold damage. Understanding the spatio-temporal dynamics of plastic-mulched citrus (PMC) is of great importance for precision management of citrus orchards. However, monitoring the long-term and large-area PMC dynamics is challenging because the PMC is typically distributed in cloudy and rainy areas with rapid spatial variability, leading to the limited availability of high-quality remotely sensed data. Moreover, it is difficult to collect sufficient field samples in rugged mountainous regions for extracting PMC. To address these limitations, we proposed a new plastic-mulched citrus index (PMCI) based on spectral separability analysis of PMC and other land cover types. The images from Landsat-5/7/8, Sentinel-2, and Gaofen-1 satellites were employed to extract the intra-annual PMC distribution from 2019 to 2020 and interannual PMC distribution from 2008 to 2020 in Yangshuo county, Guangxi Zhuang Autonomous Region, China. Results showed that the PMCI outperformed other widely used indices in PMC extraction with overall accuracy (OA) increased by 0.09-0.3. Besides, the PMCI exhibited good performances in extracting PMC over different observation dates with OA ranging 0.92-0.98 and 0.91-0.98 in the intra-annual and annual PMC maps, respectively. According to the derived PMC time series maps, PMC displayed significant intra-annual spatial variations in the start date and length of plastic mulching period, whereas the interannual variation of PMC revealed long-term technology adoption patterns in the study area. Furthermore, we found that temperature mainly affected intra-annual PMC variability, and that agricultural policy, market factor, neighborhood effect and variety replacement can explain the interannual PMC variability. These results indicate that the developed PMCI can effectively identify PMC over different agricultural regions and observation dates, and the resultant spatio-temporal dynamic PMC maps based on multi-source satellite data provide an important basis for the promotion of PMC at larger scales and the sustainable development of the citrus industry.
