作者机构：
[Zhu, Wenchao; Wu, Hao; Jiang, Zhimeng; Cen, Luyu] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zhu, Wenchao; Wu, Hao; Jiang, Zhimeng; Cen, Luyu] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.
通讯机构：
[Zhimeng Jiang] C;College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China<&wdkj&>Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, China
关键词：
land use spatial pattern;resource environment carrying capacity;land use change;spatial optimization;high-quality land development;Zhengzhou city
摘要：
High-intensity land use and resource overloaded-induced regional land use spatial pattern (LUSP) are essential and challenging for high-quality development. The empirical studies have shown that a scientific land uses spatial layout, and the supporting system should be based on a historical perspective and require better considering the double influence between the current characteristics and future dynamics. This study proposes a comprehensive framework that integrates the resource environment carrying capacity (RECC) and land use change (LUC) to investigate strategies for optimizing the spatial pattern of land use for high-quality development. China's Zhengzhou city was the subject of a case study whose datasets include remote sensing, spatial monitoring, statistics, and open sources. Three significant results emerged from the analysis: (1) The RECC has significant spatial differentiation but does not follow a specific spatial law, and regions with relatively perfect ecosystems may not necessarily have better RECC. (2) From 2020 to 2030, the construction land and farmland will fluctuate wildly, with the former increasing by 346.21 km(2) and the latter decreasing by 295.98 km(2). (3) The study area is divided into five zones, including resource conservation, ecological carrying, living core, suitable construction, and grain supply zones, and each one has its LUSP optimization orientation. This uneven distribution of RECC reflects functional defects in the development and utilization of LUSP. In addition, the increase in construction land and the sharp decline of farmland pose potential threats to the sustainable development of the study area. Hence, these two elements cannot be ignored in the future high-quality development process. The findings indicate that the LUSP optimization based on dual dimensions of RECC and LUC is more realistic than a single-dimension solution, exhibiting the LUSP optimization's effectiveness and applicability.
作者机构：
[Jie Yan; Chaohui Zheng] School of Architecture and Urban Planning, Hunan City University, Yiyang, People’s Republic of China;College of Urban and Environmental Science, Central China Normal University, Wuhan, People’s Republic of China;[Hui Tang] School of Architecture and Urban Planning, Hunan City University, Yiyang, People’s Republic of China<&wdkj&>College of Urban and Environmental Science, Central China Normal University, Wuhan, People’s Republic of China
通讯机构：
[Jie Yan] S;School of Architecture and Urban Planning, Hunan City University, Yiyang, People’s Republic of China
关键词：
coupling coordination degree;Healthy;supply and demand matching;the central urban area of Yiyang;urban park green space
作者机构：
[Fan, Junfu; Zuo, Jiwei; Shi, Zongwen; Chen, Jiahao; Zhang, Mengzhen] Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255000, Peoples R China.;[Fan, Junfu; Zhang, Mengzhen] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China.;[Chen, Jiahao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Ji, Min] Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266510, Peoples R China.
通讯机构：
[Chen, JH ] S;Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255000, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
building change detection;deep learning;high-resolution;multispectral;multisource spectral data
摘要：
Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images.
通讯机构：
[Yonglong Lu] S;State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian 361102, China<&wdkj&>State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
关键词：
Energy Modeling;Energy management;Energy policy;Energy resources
摘要：
The flourishing logistics in both developed and emerging economies leads to huge greenhouse gas (GHG) emissions; however, the emission fluxes are poorly constrained. Here, we constructed a spatial network of logistic GHG emissions based on multisource big data at continental scale. GHG emissions related to logistics transportation reached 112.14Mt CO(2)-equivalents (CO(2)e), with seven major urban agglomerations contributing 63% of the total emissions. Regions with short transport distances and well-developed road infrastructure had relatively high emission efficiency. Underlying value flow of the commodities is accompanied by logistics carbon flow along the supply chain. The main driving factors affecting GHG emissions are driving speed and gross domestic product. It may mitigate GHG emissions by 27.50-1162.75 Mt CO(2)e in 15 years if a variety of energy combinations or the appropriate driving speed (65-70km/h) is adopted. The estimations are of great significance to make integrated management policies for the global logistics sector.
摘要：
This study utilized Trichoderma and activated sludge to construct combined activated sludge (TAS). The metagenomic approach was employed to examine the shifts in microbial community structure and function of TAS under amoxicillin stress and investigate the mechanism underlying the reduction of β-lactam antibiotic resistance genes (β-ARGs). The findings demonstrated that the elevated aundance of glpa, glpd, ugpq, glpq, and glpb were primarily responsible for the reduction in total phosphorus (TP) removal by TAS. The increased abundance of Proteobacteria and Verrucomicrobia led to enhanced expression of ugpb, phnd, and phne, thereby improving the TP removal of TAS. Furthermore, antibiotic inactivation has gradually become the primary antibiotic resistance mechanism in TAS. Specifically, an increase in the abundance of OXA-309 in TAS will decrease the probability of amoxicillin accumulation in TAS. A decrease in β-ARGs diversity confirmed this. This study presents a novel approach to reducing antibiotic and ARG accumulation in sludge.
摘要：
The tiers of prefectures and counties are important indicators that reflect their political status,population and economic importance.By using historical quantitative analysis and GIS analysis methods,this paper unpacks the spatiotemporal changes of 339 prefectures and ...MORE The tiers of prefectures and counties are important indicators that reflect their political status,population and economic importance.By using historical quantitative analysis and GIS analysis methods,this paper unpacks the spatiotemporal changes of 339 prefectures and 1607 counties in Tang Dynasty.The results show that:(1)The number of prefecture tier Fu(府),Fu(辅),Xiong(雄)and county tier Chi(赤),Ji(畿),Ci-Chi(次赤)and Ci-Ji(次畿)was relatively stable in Tang Dynasty,while the number of Shang(上),Zhong(中),Xia(下)prefectures and counties changed drastically.In the late Tang Dynasty,the number of upgraded prefectures and counties was more than that of degraded prefectures and counties,with the most significant hierarchical change took place from Kaiyuan(713-741)to Yuanhe(806-820).(2)The spatio-temporal changes of prefectures and counties in Tang Dynasty was“high in the north and low in the south”.Guanzhong Plain was the highest area in the prefecture and county level.The temporal change was“falling in the north and rising in the south”.The Plain of Hubei and Hunan,Poyang Lake Plain and Taihu Plain in the middle and lower reaches of the Yangtze River rose most significantly.(3)The tiers of prefectures and counties in the vicinity of the capital of the Tang Dynasty were most affected by political factors,while the tiers of the frontier fortresses and traffic throats were most affected by military factors.Other prefectures and counties tiers were mainly affected by economic factors,especially population size.(4)The spatio-temporal changes of the tiers of prefectures and counties in Tang Dynasty reflected the eastward and southward movement of the national political,demographic,urban and economic centers after the An-Shi Rebellion in the middle of the Tang Dynasty.FEWER
期刊：
Frontiers in Environmental Science,2023年11:1202661 ISSN：2296-665X
通讯作者：
Cui, J.
作者机构：
[Jing, Ying] School of Business, NingboTech University, Ningbo, China;[Cui, Jiaxing] College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China;[Ma, Ding] School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China;[Chen, Yiyun] School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
通讯机构：
[Cui, J.] C;College of Urban and Environmental Sciences, China
关键词：
geographic big data;geographical information science (GIScience);spatial analysis;spatial planning and design;sustainable development
摘要：
A high-quality remote sensing interpretation dataset has become crucial for driving an intelligent model, i.e., deep learning (DL), to produce land-use/land-cover (LULC) products. The existing remote sensing datasets face the following issues: the current studies (1) lack object-oriented fine-grained information; (2) they cannot meet national standards; (3) they lack field surveys for labeling samples; and (4) they cannot serve for geographic engineering application directly. To address these gaps, the national-standards- and DL-oriented raster and vector benchmark dataset (RVBD) is the first to be established to map LULC for conducting soil water erosion assessment (SWEA). RVBD has the following significant innovation and contributions: (1) it is the first second-level object- and DL-oriented dataset with raster and vector data for LULC mapping; (2) its classification system conforms to the national industry standards of the Ministry of Water Resources of the People's Republic of China; (3) it has high-quality LULC interpretation accuracy assisted by field surveys rather than indoor visual interpretation; and (4) it could be applied to serve for SWEA. Our dataset is constructed as follows: (1) spatio-temporal-spectrum information is utilized to perform automatic vectorization and label LULC attributes conforming to the national standards; and (2) several remarkable DL networks (DenseNet161, HorNet, EfficientNetB7, Vision Transformer, and Swin Transformer) are chosen as the baselines to train our dataset, and five evaluation metrics are chosen to perform quantitative evaluation. Experimental results verify the reliability and effectiveness of RVBD. Each chosen network achieves a minimum overall accuracy of 0.81 and a minimum Kappa of 0.80, and Vision Transformer achieves the best classification performance with overall accuracy of 0.87 and Kappa of 0.86. It indicates that RVBD is a significant benchmark, which could lay a foundation for intelligent interpretation of relevant geographic research about SWEA in the Yangtze River Basin and promote artificial intelligence technology to enrich geographical theories and methods.
摘要：
Cadmium (Cd) contamination poses a considerable threat to human health through grain enrichment and limits biological nitrogen fixation (BNF) in paddy fields. Biochar has shown great potential for agricultural soil remediation because it inactivates Cd, but uncertainties remain as to how biochar amendments affect BNF and grain N use efficiency in paddies. To elucidate these issues, we investigated the effects of biochar amendment on the structure and function of diazotrophic bacterial communities in different rice growth stages in Cd-contaminated paddy fields, and evaluated the contribution of BNF to grain N use efficiency under biochar amendment. The results showed that biochar amendment significantly increased the abundance of diazotrophic bacteria in the tillering and jointing stages. Furthermore, the community structure of soil diazotrophic bacteria markedly changed with biochar amendment, with a significant reduction in the abundances of Euryarchaeota, Desulfobacterales (Proteobacteria), and Sphingomonadales (Bacteroidetes) in the tillering stage. Changes in the soil carbon/nitrogen (C/N) ratio was the main factor driving diazotrophic microbial community characteristics caused by the release of available C from biochar at the tillering stage, rather than the Cd. Moreover, biochar amendment increased the efficiency of BNF (especially for autotrophic N2 fixation) in the vegetative phase of rice growth. Notably, biochar amendment significantly decreased BNF efficiency during the filling stage and reduced grain N use efficiency. The limited available nutrients in biochar and the toxicity of polycyclic aromatics and phenols in biochar-derived dissolved organic matter were responsible for the varied impacts of biochar on BNF in different rice growth stages. For the first time, we report that biochar amendment in paddy soils reduces Cd toxicity but also inhibits BNF and thereby decreases N use efficiency. Therefore, before applying biochar to inactivate Cd in paddy fields, there should be a trade-off between agricultural production and ecological safety to achieve sustainable agriculture.
作者机构：
[Xu, Baodong; Wei, Haodong; Xu, Zilu; Yang, Jingya; Cai, Zhiwen] Huazhong Agr Univ, Macro Agr Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Hu, Qiong; He, Zhen] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[You, Liangzhi] Huazhong Agr Univ, Coll Econ & Management, Wuhan 430070, Peoples R China.;[You, Liangzhi] Int Food Policy Res Inst, 1201 1 St NW, Washington, DC 20005 USA.;[Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China.
通讯机构：
[Baodong Xu] M;Macro Agriculture Research Institute, College of Resources and Environment, Huazhong Agricultural University, Wuhan, 430070, China<&wdkj&>State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China
关键词：
Ratoon rice;Potential northern limits;Potential planting areas;Climate conditions;MaxEnt model
摘要：
Ratoon rice has emerged as a promising rice cropping system to improve grain production and reduce labor costs compared with traditional single/double rice in China. However, the potential planting areas of ratoon rice in China remain unclear. This research investigated the potential northern limits and promotion extent of ratoon rice in China by considering its climatic suitability based on the optimized maximum entropy (MaxEnt) model as well as terrain and land use conditions. The MaxEnt model derived by all environmental variables yielded a good performance, with average AUC (area under the curve) and TSS (true skill statistic) over the validation dataset of 0.940 and 0.825, respectively. The comparison with field samples and previous studies revealed the reliability of the derived potential promotion areas. Potential northern limits contained a closed curve surrounding the Sichuan Basin, and the other curve ran from Yunnan Province to Jiangsu Province. Safe promotion areas of ratoon rice in China were 472,003 km2, mainly located in Sichuan, Hubei, Guangxi and Hunan. Risky promotion areas were 74,150 km2, which were dominant in Henan, Anhui and Yunnan. Our study provides crucial infor-mation for rice planting pattern adjustment to alleviate national food insecurity caused by the loss of double rice.
期刊：
Global Change Biology,2023年29(8):2203-2226 ISSN：1354-1013
通讯作者：
Linchuan Fang
作者机构：
[Liu, Ji; Fang, Linchuan; Liu, Lanfa; Zhou, Baitao] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan, Peoples R China.;[Liu, Ji] Leibniz Inst Freshwater Ecol & Inland Fisheries, Dept Ecohydrol, Berlin, Germany.;[Fang, Linchuan; Qiu, Tianyi; He, Haoran; Cui, Qingliang] Northwest A&F Univ, Chinese Acad Sci, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling, Shaanxi, Peoples R China.;[Sardans, Jordi; Penuelas, Josep] UAB, CSIC, Global Ecol Unit CREAF, Bellaterra, Catalonia, Spain.;[Sardans, Jordi; Penuelas, Josep] CREAF, Cerdanyola Del Valles, Catalonia, Spain.
通讯机构：
[Linchuan Fang] H;Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan, China<&wdkj&>State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Chinese Academy of Sciences, Northwest A&F University, Yangling, Shaanxi, China<&wdkj&>School of Resource and Environmental Engineering, Wuhan University of Technology, Wuhan, China
摘要：
Crop residues coupled with inorganic fertilizers balance soil ecological stoichiometry and thus improve soil carbon, nitrogen, and phosphorus sequestration. Subsequently, environmental effects are mitigated and grain yields are increased. Abstract Although soil ecological stoichiometry is constrained in natural ecosystems, its responses to anthropogenic perturbations are largely unknown. Inputs of inorganic fertilizer and crop residue are key cropland anthropogenic managements, with potential to alter their soil ecological stoichiometry. We conducted a global synthesis of 682 data pairs to quantify the responses of soil carbon (C), nitrogen (N), and phosphorus (P) and grain yields to combined inputs of crop residue plus inorganic fertilizer compared with only inorganic fertilizer application. Crop residue inputs enhance soil C (10.5%–12%), N (7.63%–9.2%), and P (2.62%–5.13%) contents, with an increase in C:N (2.51%–3.42%) and C:P (7.27%–8.00%) ratios, and grain yields (6.12%–8.64%), indicating that crop residue alleviated soil C limitation caused by inorganic fertilizer inputs alone and was able to sustain balanced stoichiometry. Moreover, the increase in soil C and C:N(P) ratio reached saturation in ~13–16 years after crop residue return, while grain yield increase trend discontinued. Furthermore, we identified that the increased C, N, and P contents and C:N(P) ratios were regulated by the initial pH and C content, and the increase in grain yield was not only related to soil properties, but also negatively related to the amount of inorganic N fertilizer input to a greater extent. Given that crop residual improvement varies with soil properties and N input levels, we propose a predictive model to preliminary evaluate the potential for crop residual improvement. Particularly, we suggest that part of the global budget should be used to subsidize crop residue input management strategies, achieving to a win‐win situation for agricultural production, ecological protection, and climate change mitigation.
期刊：
ISPRS Journal of Photogrammetry and Remote Sensing,2023年204:397-420 ISSN：0924-2716
通讯作者：
Meng, R;Zhao, F
作者机构：
[Meng, Ran; Lv, Zhengang; Zhou, Longfei; Meng, R; Huang, Zehua; Xu, Binyuan; Sun, Rui] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Zhong, Liheng] Ant Grp, Hangzhou 311121, Peoples R China.;[Zhao, Feng] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Wu, Jin] Univ Hong Kong, Sch Biol Sci, Hong Kong, Peoples R China.;[Wu, Jin] Chinese Univ Hong Kong, State Key Lab Agrobiotechnol, Shatin, Hong Kong, Peoples R China.
通讯机构：
[Zhao, F ] C;[Meng, R ] H;Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;HIT Artificial Intelligence Res Inst Co Ltd, Harbin, Heilongjiang, Peoples R China.
关键词：
Tree species mapping;Key phenological stage;Transformer;Attention mechanism;Deep learning;Plantation forests
摘要：
Plantation forests provide critical ecosystem services and have experienced worldwide expansion during the past few decades. Accurate mapping of tree species through remote sensing is critical for managing plantation forests. The typical temporal behaviors and traits of tree species in satellite image time series (SITS) generate temporal and spectral features in multiple phenological stages that are critical to improve tree species mapping. However, the diverse input features, sequential relations and complex structures in SITS drastically increase the dimension and difficulty of spectral-temporal feature extraction, which challenges the capacity of many general classifiers not explicitly adapted for spectral-temporal learning. As a result, there is still a lack of a method that could automatically extract spectral-temporal features with high separability and regional adaptability from highdimensional SITS for tree species mapping of plantation forests. Moreover, the effects of varying temporal resolution and feature combination on the plantation tree species mapping are under-explored. Here, we developed a multi-head attention-based method for automatically extracting spectral-temporal features with high separability based on a modified Transformer network (Transformer4SITS) for improved plantation tree species mapping. The end-to-end network model consists of a feature extraction module to learn deep spectral-temporal features from SITS and a fusion module to combine multiple features for improving mapping accuracy. We applied this method to two representative plantation forests in southern and northern China for tree species mapping. The results show that: (1) Transformer4SITS method could self-adaptively extract typical spectraltemporal features of key phenological stages (e.g., greenness rising and falling) from SITS, and achieved significantly improved accuracies by at most 15% in comparison with all four baseline methods (i.e., long shortterm memory, harmonic analysis, time-weighted dynamic time warping, linear discriminant analysis); (2) time series with higher temporal resolution tended to produce more accurate species maps consistently across two sites, with their overall accuracies (OA) respectively increasing from 91.05% and 84.33% (60-day) to 94.88% and 88.72% (5-day), but the effect of high temporal resolution respectively leveled off around 90-day and 50-day resolution across two sites; (3) the mapping results using all available bands and two-band spectral indices outperformed the results using a subset of them, but with only modest increase in the accuracy (i.e., OA increased from 93.63% and 86.01% to 94.88% and 88.72%. This study thus provides a state-of-the-art deep learning-based method for improved tree species mapping, which is critical for sustainable management and biodiversity monitoring of plantation forests across large scales.
