作者机构：
[He, Tao; Wang, Cai-Qun; Lu, Jun] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430072, Peoples R China.;[Song, Dan-Xia; Song, DX] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Song, Dan-Xia; Song, DX] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Song, DX ] C;Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
BRDF;spectral and angular harmonization;data fusion;land surface phenology
摘要：
Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio-temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion.
期刊：
Journal of Cleaner Production,2022年373:133977 ISSN：0959-6526
通讯作者：
Dingtao Shen
作者机构：
[Hu, Zukang] Hohai Univ, Coll Comp & Informat, Nanjing, Peoples R China.;[Tian, Pei; Shen, Dingtao] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Tian, Pei; Shen, Dingtao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.;[Wang, Helong] Zhejiang Inst Marine Planning & Design, Zhejiang Inst Hydraul & Estuary, Hangzhou, Peoples R China.;[Wang, Helong] Zhejiang Univ, Coll Civil Engn & Architecture, Hangzhou, Peoples R China.
通讯机构：
[Dingtao Shen] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
关键词：
Water distribution systems;Anomaly detection;Pipe burst;Sensor failure
摘要：
Data-driven anomaly detection and early warning have been extensively used in water distribution systems (WDS). Events such as pipe bursts and sensor failure cause abnormal monitoring data. Anomaly detection during real-time data monitoring and identification of various events are crucial in WDS. This study proposes a framework for anomaly detection and early warning in WDS. This framework comprises four anomaly detection modules—single-point anomaly identification, sensor sequence, inter-sensor sequence, qualitative module. A case study is conducted using the Net3 pipe network model. The results indicate that the proposed method can accurately identify pipe bursts and detect situations causing abnormal sensor data.
作者机构：
[Tang, Hui; Ao, Rongjun; Shen, Xue; Shi, Guoning] 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.;[Chen, Yun] Chengdu Univ, Sch Tourism & Culture Ind, Chengdu 610106, Peoples R China.
通讯机构：
[Yun Chen; Rongjun Ao] A;Authors to whom correspondence should be addressed.<&wdkj&>School of Tourism and Culture Industry, Chengdu University, Chengdu 610106, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
关键词：
population health;economic development;coupling coordination;driving factors;China
摘要：
Promoting the coordinated development of population health and the economy is an important part of building a “Healthy China” and promoting high-quality economic development. Based on the systematic construction of the population health and economic development evaluation index system, this paper uses the coupled coordination model, geodetector, and geographically weighted regression (GWR) to comprehensively measure the population health level and economic development level at the provincial scale in China in 2000 and 2015, and reveals the spatial and temporal evolution characteristics of the coupled coordination relationship between the population health level and economic development level at the provincial scale in China from 2000 to 2015 and its driving factors. The results show the following: (1) China’s population health and economic development are in a high-level coupling stage, and the coupling level increases slightly with time; spatially, two types of running-in coupling and high-level coupling coexist; the coupling degree in the eastern and central regions tends to increase, while the coupling degree in the western region tends to weaken. (2) China’s population health and economic development are in a good coupling coordination stage as a whole, and the coupling coordination degree has an increasing trend; spatially, the coupling coordination degree shows high spatial differentiation characteristics in the east and low in the west; the good and high-quality coupling coordination type area tends to expand to the west, while the moderate coupling coordination type area tends to shrink to the west; there is also positive spatial agglomeration of coupling coordination degree, and the spatial agglomeration is gradually enhanced. (3) The coupling coordination of China’s population health and economic development is driven by multiple factors such as natural conditions, health resources, culture quality, and urbanization level; the interaction between factors is stronger than that of a single factor, and the driving effect of each factor also shows significant spatial heterogeneity. This study is intended to provide a scientific basis for promoting harmonious population health and economic development.
期刊：
International Journal of Environmental Research and Public Health,2022年19(19):11979- ISSN：1661-7827
通讯作者：
Bin Yu
作者机构：
[Yan, Meiyan; Guo, Hui; Zhang, Hanxia; Zhang, Zonggang; Guo, Xinwei; Yu, Bin; Ren, Junhu] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Yan, Meiyan; Guo, Hui; Zhang, Hanxia; Zhang, Zonggang; Guo, Xinwei; Yu, Bin; Ren, Junhu] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Yan, Meiyan] Dali Univ, Coll Teacher Educ, Dali 671003, Peoples R China.
通讯机构：
[Bin Yu] 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.
关键词：
rural revitalization;ecological economy;ecological products;ecological industry;“two mountains” theory
摘要：
This article aims to discuss how to give full play to the comparative advantages of the rural ecological environment and realize the endogenous development of rural society and economy in China. First, based on the ecological economy theory of “lucid waters and lush mountains are golden and silver mountains” (the “two mountains” theory), we integrated the theories and methods of ecology, economics, and geography disciplines to examine the transformation of “ecological advantages” into “economic development” from a comprehensive perspective. Second, based on the matching relationship between the division of major function zones and the classification of ecological services, we creatively constructed a theoretical framework for the endogenous development of rural areas. Third, accounting indicators and methods for rural ecological products’ biophysical quantity and monetary value are established. Finally, we conducted an empirical study of Nanshi Village in central China as a case. The results showed that: The benefits provided by ecosystems to the development of human society would be underestimated if it is measured only by the provisioning services; the per capita Gross Ecosystem Product (GEP) of the case area was three times the per capita disposable income of rural permanent residents in the same period. Taking advantage of the rural ecological environment to promote the actual transformation of the potential value of ecological products is the feasible path for rural revitalization. One of the implications of this study is that it links the rural ecological and environmental advantages with social and economic development from the perspective of ecological economics and provides decision-making support for this case and other similar rural ecological industry revitalization practices.
期刊：
International Journal of Environmental Research and Public Health,2022年19(22):14958- ISSN：1661-7827
通讯作者：
Chan Xu
作者机构：
[Zhao, Min; Yu, Bin; Liu, Siyao; Guo, Jing] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Zhao, Min; Yu, Bin; Liu, Siyao; Guo, Jing] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Xu, Chan] Sichuan Normal Univ, Fac Geog & Resource Sci, Chengdu 610101, Peoples R China.;[Xu, Chan] Sichuan Normal Univ, Key Lab Evaluat & Monitoring Southwest Land Resou, Minist Educ, Chengdu 610068, Peoples R China.
通讯机构：
[Chan Xu] T;The Faculty of Geography & Resource Sciences, Sichuan Normal University, Chengdu 610101, China<&wdkj&>Key Laboratory of the Evaluation and Monitoring of Southwest Land Resources, Ministry of Education, Sichuan Normal University, Chengdu 610068, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Collective resilience is the ability of human beings to adapt and collectively cope with crises in adversity. Emotional expression is the core element with which to characterize the psychological dimension of collective resilience. This research proposed a stage model of collective resilience based on the temporal evolution of the public opinions of COVID-19 in China’s first anti-pandemic cycle; using data from hot searches and commentaries on Sina Weibo, the changes in the emotional patterns of social groups are revealed through analyses of the sentiments expressed in texts. A grounded theory approach is used to elucidate the factors influencing collective resilience. The research results show that collective resilience during the pandemic exhibited an evolutionary process that could be termed, “preparation–process–recovery”. Analyses of expressed sentiments reveal an evolutionary pattern of “positive emotion prevailing–negative emotion appearing–positive emotion recovering Collective resilience from a psycho-emotional perspective is the result of “basic cognition-intermediary condition-consequence” positive feedback, in which the basic cognition is expressed as will embeddedness and the intermediary conditions include the subject behavior and any associated derived behavioral characteristics and spiritual connotation. These results are significant both theoretically and practically with regard to the reconstruction of collective resilience when s‘ force majeure’ event occur.
作者机构：
[Peng Qing-qing; Li Shuo] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Chen Song-chao] ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China.;[Zhou Ming-hua] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Surface Proc & Ecol Regulat, Chengdu 610041, Peoples R China.
通讯机构：
[Li, S.] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, China
关键词：
光谱库;相异度;距离矩阵;容量;偏最小二乘
摘要：
掌握土壤在空间和时间上的表征至关重要。 土壤可见-近红外(Vis-NIR)光谱可以估算土壤有机碳(SOC)等属性, 与传统的实验室理化分析相比, 光谱技术能有效实现土壤信息的快速获取。 土壤光谱库为建立经验模型提供了大量具有丰富变异性和多样性的样本作数据基础。 但受限于库中土壤样本的异质性和模型的适应性, 通常区域或局部尺度模型的稳健性欠佳。 已有的研究主要通过目标样本部分入库的方式改善库的性能, 但影响了光谱技术的低成本优势。 该研究在不入库的前提下基于土壤光谱的相异度, 探究经典距离算法结合土壤光谱库构建局部预测模型的可行性, 并比较分析局部模型样本容量对预测精度的响应。 基于全球土壤光谱库(GSSL)的677个土柱, 从每个国家随机取十分之一的土柱(97个)组成局部目标测试集(Test), 其余580个作土壤光谱库(SSL)。 分别采用欧氏距离(ED)、 马氏距离(MD)、 和光谱角(SAM)来分别度量Test与SSL间的光谱相异度并生成距离矩阵。 按距离矩阵的前0.04%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%和5%从SSL中提取与Test最相似的光谱样本构建共计9个容量的局部建模集(Local), 使用偏最小二乘回归(PLSR)建立Vis-NIR和SOC含量的预测模型并通过Test验证模型精度, 通过光谱的主成分空间考察并解释各种距离算法下Local的“容量-精度”变化。 结果表明, 在待测样本不入库的情况下, 三种距离算法构建的Local模型相较于全局模型的预测精度均有一定提升, 但三者的“容量-精度”的拐点存在显著差异。 SAM兼顾了光谱的波形和幅度因此较MD、 ED更具优势; 其前0.2%比例的Local不仅预测精度最优, 且用于建模所需的样本容量最少。 因此认为, SAM法更适用于从土壤光谱库中构建局部模型, 距离矩阵的前0.2%可作为局部模型的容量参考。 It is vital to understand the characteristics of soils and their distribution in space and over time. Spectroscopy in the visible-near-infrared (Vis-NIR) can estimate soil properties (e.g., SOC). Compared with traditional laboratory physical and chemical analysis, spectral technology enables the practical acquisition of soil information rapidly. The development of a soil spectral library (SSL) can provide large amounts of soil data with variability and diversity for empirical calibration. Calibrations derived with these SSLs, however, at the very least, help to improve the robustness of spectroscopic models at regional and local scales due to high soil heterogeneity and model adequateness. Previous studies usually put several target samples into SSL, called spiking; however, the cost-efficiency of spectral techniques was offset more or less. Without spiking samples, we aim to explore the feasibility of developing a local model by constraining the SSL with spectral dissimilarities using classical distance methods. The response between the capacity of the local model with prediction accuracy was also compared and analyzed. In this study, we built a local test set (Test) with the amount of spectral variation from 97 cores, divided by one-tenth of each country from the global soil spectral library (677 cores), and the remaining 580 cores were used as the SSL. We used Euclidean distance (ED), Mahalanobis distance (MD) and Spectral Angle Mapper (SAM) to measure the spectral dissimilarity between Test and SSL and to generate the distance matrix. For each method, nine Local subsets were selected and developed by selecting the spectra of SSL, which were considered similar to the Test. The selection based on the first 0.04%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1% and 5% of the distance matrix. The statistical models were built to predict SOC concentrations from the spectra by partial least-squares regression. We decomposed the spectra using principal components analysis (PCA) to identify those variables of Local derived from ED, MD and SAM. Our results showed that all the Local models developed by the three distance algorithms without spiking samples still can improve the accuracy compared to the global one, but the inflection points of a sample size of Local with accuracy were significantly different. The SAM considers the waveform and amplitude of the spectrum, so it has more advantages than MD and ED. Its Local, with the first 0.2% ratio, performed the best prediction accuracy, also required the least samples for modeling. We conclude that SAM is more suitable for developing local models from SSL. The first 0.2% of the distance matrix can be used as a reference for the capacity of the local model.
通讯机构：
[Songchao Chen] Z;ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China<&wdkj&>Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
open soil spectral library;electrical conductivity;machine learning;environmental covariates
摘要：
Soil salinization is one of the major degradation processes threatening food security and sustainable development. Detailed soil salinity information is increasingly needed to tackle this global challenge for improving soil management. Soil-visible and near-infrared (Vis-NIR) spectroscopy has been proven to be a potential solution for estimating soil-salinity-related information (i.e., electrical conductivity, EC) rapidly and cost-effectively. However, previous studies were mainly conducted at the field, regional, or national scale, so the potential application of Vis-NIR spectroscopy at a global scale needs further investigation. Based on an extensive open global soil spectral library (61,486 samples with both EC and Vis-NIR spectra), we compared four spectral predictive models (PLSR, Cubist, Random Forests, and XGBoost) in estimating EC. Our results indicated that XGBoost had the best model performance (R-2 of 0.59, RMSE of 1.96 dS m(-1)) in predicting EC at a global scale, whereas PLSR had a relatively limited ability (R-2 of 0.39, RMSE of 2.41 dS m(-1)). The results also showed that auxiliary environmental covariates (i.e., coordinates, elevation, climatic variables) could greatly improve EC prediction accuracy by the four models, and the XGBoost performed best (R-2 of 0.71, RMSE of 1.65 dS m(-1)). The outcomes of this study provide a valuable reference for improving broad-scale soil salinity prediction by the coupling of the spectroscopic technique and easily obtainable environmental covariates.
期刊：
International Journal of Environmental Research and Public Health,2022年19(19):12867- ISSN：1661-7827
通讯作者：
Jiaxing Cui
作者机构：
[Zhu, Yuanyuan; Cui, Jiaxing; Zhang, Rui] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Zhu, Yuanyuan; Cui, Jiaxing; Zhang, Rui] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Jiaxing Cui] K;Key Laboratory for Geographical Process Analysis and 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.
关键词：
sustainable development;ecological well-being performance;hierarchical effect;resource and environmental consumption;human-environment interactions;threshold regression model
摘要：
Improving the ecological well-being performance (EWP) of natural resources and environmental consumption in relation to human well-being, within the ecological boundary, is necessary for sustainable development. This study used the Super-SBM model to measure the urban EWP of urban agglomeration in the middle reaches of the Yangtze River (MRYRUA) in 2020. The spatial differentiation characteristics of EWP in the MRYRUA were identified. The heterogeneity in the direction and size of the influencing factors of EWP at different urban hierarchy (UH) levels was empirically tested by establishing a threshold model. The results are as follows: (1) In 2020, the EWP of the study area showed a trend of high levels in the southwest and low levels in the northeast. The EWP presented a multi-center "core-periphery" distribution, and the characteristic of "central collapse" was evident. The UH level of the middle and lower hierarchy-level cities was inconsistent with its EWP. (2) A non-single linear relationship was found between the influencing factors of the EWP of the MRYRUA and the EWP. The impacts of technological progress, industrial structure, environmental regulation, and population density on the EWP of the MRYRUA all showed threshold characteristics. (3) Heterogeneity and stages were both observed for the influencing factors of EWP under different UH levels. The effect of technological progress on EWP presented the characteristics of bidirectional and two-stage developments, and environmental regulation presented the features of a significant positive three-stage development. Both industrial structure and population density presented two-stage aspects, but the former acted in a negative direction, while the latter served in a positive order. This study provides a theoretical basis for the government to formulate differentiated regional policies and promote the coordinated improvement of EWP among cities at all hierarchy levels in the urban agglomeration. This study is of great significance to the sustainable development of urban agglomerations. Its results can provide a reference for other urban agglomerations, metropolitan areas, and city clusters worldwide to coordinate economic development, ecological protection, and to improve people's well-being.
摘要：
Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML-dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R-2 of 0.85 and RMSE of 0.78 t/ha 3-4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.
通讯机构：
[Feng Zhao] K;Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan, 430079, China
关键词：
Tree species mapping;Plantation forests;Red-edge features;Temporal frequency of data acquisition;Fusion of Landsat-8 and Sentinel-2
期刊：
ISPRS International Journal of Geo-Information,2022年11(3):170- ISSN：2220-9964
通讯作者：
Jing Luo
作者机构：
[Tian, Lingling; Luo, Jing; Chen, Jie] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan 430079, Peoples R China.;[Jiang, Liang] Xuchang Univ, Coll Urban & Environm Sci, 88 Bayi Rd, Xuchang 461000, Peoples R China.;[Tian, Ye] Hubei Univ Econ, Inst Adv Studies Finance & Econ, 8 Yangqiaohu Rd, Wuhan 430205, Peoples R China.;[Chen, Guolei] Guizhou Normal Univ, Coll Geog & Environm Sci, 116 North Baoshan Rd, Guiyang 500025, Peoples R China.
通讯机构：
[Jing Luo] C;College of Urban and Environmental Sciences, Central China Normal University, No.152 Luoyu Road, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
作者机构：
[Wang, Cong; Hu, Qiong; Wu, Yijin] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Cong; Hu, Qiong; Wu, Yijin] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Hu, Jie; Chen, Yunping] Huazhong Agr Univ, Coll Plant Sci & Technol, Macro Agr Res Inst, Wuhan 430070, Peoples R China.;[Lin, Shangrong] Sun Yat Sen Univ, Sch Atmospher Sci, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China.;[Xie, Qiaoyun] Univ Technol Sydney, Fac Sci, Sch Life Sci, Sydney, NSW 2007, Australia.
通讯机构：
[Qiong Hu] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province & School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
vegetation phenology;climatic limitation;solar-induced chlorophyll fluorescence;enhanced vegetation index
摘要：
Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.
作者：
Jiang, Zhimeng;Wu, Hao;Lin, Anqi;Shariff, Abdul Rashid Mohamed;Hu, Qiong;...
期刊：
Science of The Total Environment,2022年843:156971 ISSN：0048-9697
通讯作者：
Hao Wu
作者机构：
[Song, Danxia; Zhu, Wenchao; Hu, Qiong; Wu, Hao; Jiang, Zhimeng; Lin, Anqi] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Shariff, Abdul Rashid Mohamed] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Shariff, Abdul Rashid Mohamed] Univ Putra Malaysia, Fac Engn, Dept Biol & Agr Engn, Serdan 43400, Malaysia.
通讯机构：
[Hao Wu] C;College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China<&wdkj&>Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430079, China
关键词：
Grain producing;Land function;Resource and environmental carrying capacity;Spatial optimization;Sustainable development
摘要：
Spatial patterns are essential for examining the sustainability derived from land systems. Constructing spatial patterns for sustainable land development is now high on the global agenda to guarantee human welfare. However, there is as yet no consensus on the comprehensive framework for optimizing the spatial pattern of land development (SPLD) contrapose a prominent grain-producing area (PGPA). To narrow this gap, we propose a synthetic framework to shape a more reasonable SPLD for a sustainable development strategy by measuring the equilibrium between the production-living-ecological space (PLES) functions and the resource and environment carrying capacity (RECC). Taking a prominent grain-producing area (PGPA) as the object, a case study involving the Jianghan Plain (JHP) in China is conducted, leading to the following novel insights. (i) The quality of PLES and RECC in a PGPA is affected by multiple dimensions: agriculture, ecology, environment, and society. The indices of the PLES function and the RECC have significant spatial heterogeneity. SPLD in regions with fragile ecological environments and strong development is often under overload pressure. (ii) Based on the spatial zoning results of SPLD, the five partitions were taken as the optimized objects, including zones of the eco-economic, model-agricultural, core-living, eco-conservation, and coordinated-development. The land function definition of these five types of zoning covers the production-living-ecological function orientation in a PGPA. (iii) The SPLD optimization framework proposed above has strong universality because it comprehensively considers the multi-dimensional spatial functional needs of PGPA. In this study, an optimization decision framework of SPLD based on measurement and zoning was established for a PGPA. Significantly, the introduced framework is applicable and practical for optimizing SPLD from a sustainable equilibrium perspective, and the findings have considerable implications for sustainable development in prominent grain-producing areas.
作者机构：
[Yang, Peng; Wu, Wenbin; Lu, Miao; Wei, Yanbing] Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Remote Sensing, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China.;[Xue, Bing; Zhang, Mengjie; Lu, Miao; Bi, Ying] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington 6140, New Zealand.;[Bi, Ying] Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China.;[Hu, Qiong] Cent China Normal Univ, Sch Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Wenbin Wu] K;Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China<&wdkj&>Author to whom correspondence should be addressed.
摘要：
Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree's width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection.
作者机构：
[Meng, Ran; Wang, Chong (Alex); Zhou, Longfei; Xu, Binyuan; Sun, Rui] Huazhong Agr Univ, Macro Agr Res Inst, Interdisciplinary Sci Res Inst, Coll Resources & Environm, Wuhan 430070, Peoples R China.;[Gong, Shengsheng; Zhou, Yu; Zhao, Feng] Cent China Normal Univ, Coll Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Chong (Alex); Dong, Yuntao] Peking Univ, Guanghua Sch Management, Beijing 100871, Peoples R China.;[Zhang, Dawei] Cent China Normal Univ, Inst China Rural Studies, Wuhan 430079, Peoples R China.;[Zhang, Dawei] Cent China Normal Univ, Inst China Urban Governance Studies, Fac Polit Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Feng Zhao] K;[Dawei Zhang] I;Institute for China Rural Studies and Institute for China Urban Governance Studies, Faculty of Political Science, Central China Normal University, Wuhan 430079, China<&wdkj&>Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
关键词：
Big cities;Children's health and well-being;Education inequality;Environmental justice;School greenspace;Vegetation fraction cover
作者机构：
[Hu, Zukang] Hohai Univ, Coll Comp & Informat, Nanjing, Peoples R China.;[Tan, Debao; 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.;[Tan, Debao; Chen, Beiqing] Changjiang River Sci Res Inst, Spatial Informat Technol Applicat Dept, Wuhan, Peoples R China.;[Chen, Wenlong] Jiangsu Prov Planning & Design Grp, Nanjing, Peoples R China.
通讯机构：
[Dingtao Shen] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
