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Acta Oceanologica Sinica 2018年第5期37卷 67-76页

作者： feng yongjiu CHEN Xinjun GAO feng LIU Yang College of Marine Sciences Shanghai Ocean University Laboratory for Marine Fisheries Science and Food Production Processes Qingdao National Laboratory for Marine Science and Technology National Distant-water Fisheries Engineering Research Center Shanghai Ocean University Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Shanghai Ocean University) Ministry of Education

We examined the scale impacts on spatial hot and cold spots of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. The original fishery data were tessellated to 18 spatial scales from 5′×5′ to 90′×90′ with a scale interval of 5′ to identify the local clusters. The changes in location, boundaries, and statistics regarding the Getis-Ord Gi＊ hot and cold spots in response to the spatial scales were analyzed in detail. Several statistics including Min, mean, Max, SD, CV, skewness, kurtosis, first quartile（Q1）, median, third quartile（Q3）, area and centroid were calculated for spatial hot and cold spots. Scaling impacts were examined for the selected statistics using linear, logarithmic, exponential, power law and polynomial functions. Clear scaling relations were identified for Max, SD and kurtosis for both hot and cold spots. For the remaining statistics, either a difference of scale impacts was found between the two clusters, or no clear scaling relation was identified. Spatial scales coarser than 30′ are not recommended to identify the local spatial patterns of fisheries because the boundary and locations of hot and cold spots at a coarser scale are significantly different from those at the original scale.

关键词： Ommastrephes bartramii scale impacts local clusters Getis-Ord Gi* spatial hotspots

Modeling Monthly Spatial Distribution of Ommastrephes bartramii CPUE in the Northwest Pacific and Its Spatially Nonstationary Relationships with the Marine Environment

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Journal of Ocean University of China 2018年第3期17卷 647-658页

作者： feng yongjiu LIU Yang CHEN Xinjun College of Marine Sciences Shanghai Ocean University Function Laboratory for Marine Fisheries Science and Food Production Processes Qingdao National Laboratory for Marine Science and Technology Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Shanghai Ocean University National Distant-Water Fisheries Engineering Research Center Shanghai Ocean University

There are substantial spatial variations in the relationships between catch-per-unit-effort(CPUE) and oceanographic conditions with respect to pelagic species. This study examines the monthly spatiotemporal distribution of CPUE of the neon flying squid, Ommastrephes bartramii, in the Northwest Pacific from July to November during 2004–2013, and analyzes the relationships with oceanographic conditions using a generalized additive model(GAM) and geographically weighted regression(GWR) model. The results show that most of the squids were harvested in waters with sea surface temperature(SST) between 7.6 and 24.6℃, chlorophyll-a(Chl-a) concentration below 1.0 mgm^(-3), sea surface salinity(SSS) between 32.7 and 34.6, and sea surface height(SSH) between-12.8 and 28.4 cm. The monthly spatial distribution patterns of O. bartramii predicted using GAM and GWR models are similar to observed patterns for all months. There are notable variations in the local coefficients of GWR, indicating the presence of spatial non-stationarity in the relationship between O. bartramii CPUE and oceanographic conditions. The statistical results show that there were nearly equal positive and negative coefficients for Chl-a, more positive than negative coefficients for SST, and more negative than positive coefficients for SSS and SSH. The overall accuracies of the hot spots predicted by GWR exceed 60%(except for October), indicating a good performance of this model and its improvement over GAM. Our study provides a better understanding of the ecological dynamics of O. bartramii CPUE and makes it possible to use GWR to study the spatially nonstationary characteristics of other pelagic species.

关键词： Ommastrephes bartramii catch-per-unit-effort (CPUE) oceanographic conditions geographically weighted regression (GWR) spatial nonstationary Northwest Pacific

The impact of spatial scale on local Moran's I clustering of annual fishing effort for Dosidicus gigas offshore Peru

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Journal of Oceanology and Limnology 2019年第1期37卷 330-343页

作者： feng yongjiu CHEN Lijuan CHEN Xinjun College of Surveying and Geo-Informatics Tongji University Laboratory for Marine Fisheries Science and Food Production Processes Qingdao National Laboratory for Marine Science and Technology National Distant-water Fisheries Engineering Research Center Shanghai Ocean University Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Shanghai Ocean University) Ministry of Education School of Earth and Environmental Sciences the University of Queensland Key Laboratory of Ecohydrology of Inland River Basin Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences

The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to *** a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to *** skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.

关键词： Dosidicus gigas fishing ef fort high-high(HH) cluster scale impact local Moran's I

To move or stay?A cellular automata model to predict urban growth in coastal regions amidst rising sea levels

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International Journal of Digital Earth 2021年第9期14卷 1213-1235页

作者： Siqin Wang Yan Liu yongjiu feng Zhenkun Lei School of Earth and Environmental Sciences The University of QueenslandSt LuciaAustralia Spatio-Temporal Analytics Research Laboratory(STAR Lab) The University of QueenslandSt LuciaAustralia College of Surveying and Geo-Informatics Tongji UniversityShanghaiPeople’s Republic of China College of Marine Sciences Shanghai Ocean UniversityShanghaiPeople’s Republic of China

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement;they are also more vulnerable to risks resulting from intensive land use and land cover change,human activities,global climate change,and the rising sea *** study aims to predict how urban growth is affected by sea level rise(SLR)in the Australian *** develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption *** on data capturing the socioeconomic and environmental factors in South East Queensland,Australia,our model is positioned to address one core research question:how does SLR affect future urban growth and human resettlement?Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation *** study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

关键词： Climate change Cellular automata Sea level rise Urban growth Human settlement

A Comparative Study of Spatially Clustered Distribution of Jumbo Flying Squid(Dosidicus gigas)Offshore Peru

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Journal of Ocean University of China 2017年第3期16卷 490-500页

作者： feng yongjiu CUI Li CHEN Xinjun LIU Yu College of Marine Sciences Shanghai Ocean UniversityShanghai 201306P.R.China The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources Ministry of EducationShanghai Ocean UniversityShanghai 201306P.R.China National Engineering Research Center for Oceanic Fisheries Shanghai Ocean UniversityShanghai 201306P.R.China Collaborative Innovation Center for Distant-Water Fisheries Shanghai 201306P.R.China

We examined spatially clustered distribution of jumbo flying squid(Dosidicus gigas) in the offshore waters of Peru bounded by 78?–86?W and 8?–20?S under 0.5?×0.5? fishing grid. The study is based on the catch-per-unit-effort(CPUE) and fishing effort from Chinese mainland squid jigging fleet in 2003–2004 and 2006–2013. The data for all years as well as the eight years(excluding El Ni?o events) were studied to examine the effect of climate variation on the spatial distribution of D. gigas. Five spatial clusters reflecting the spatial distribution were computed using K-means and Getis-Ord Gi* for a detailed comparative study. Our results showed that clusters identified by the two methods were quite different in terms of their spatial patterns, and K-means was not as accurate as Getis-Ord Gi*, as inferred from the agreement degree and receiver operating characteristic. There were more areas of hot and cold spots in years without the impact of El Ni?o, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Ni?o conditions and high water temperature were less favorable for D. gigas offshore Peru. The results suggested that the use of K-means is preferable if the aim is to discover the spatial distribution of each sub-region(cluster) of the study area, while Getis-Ord Gi* is preferable if the aim is to identify statistically significant hot spots that may indicate the central fishing ground.

关键词： Dosidicus gigas spatial cluster K-means Getis-Ord Gi^(*) El Nino sea surface temperature(SST) offshore Peru

Examining spatiotemporal distribution and CPUE-environment relationships for the jumbo flying squid Dosidicus gigas offshore Peru based on spatial autoregressive model

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Journal of Oceanology and Limnology 2018年第3期36卷 942-955页

作者： feng yongjiu CHEN Xinjun LIU Yang College of Marine Sciences Shanghai Ocean UniversityShanghai 201306China The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources(Shanghai Ocean University) Ministry of EducationShanghai 201306China National Engineering Research Center for Oceanic Fisheries(Shanghai Ocean University) Shanghai 201306China Collaborative Innovation Center for Distant-water Fisheries Shanghai 201306China

The spatiotemporal distribution and relationship between nominal catch-per-unit-ef fort(CPUE) and environment for the jumbo flying squid( Dosidicus gigas) were examined in of fshore Peruvian waters during 2009–2013. Three typical oceanographic factors aff ecting the squid habitat were investigated in this research, including sea surface temperature(SST), sea surface salinity(SSS) and sea surface height(SSH). We studied the CPUE-environment relationships for D. gigas using a spatially-lagged version of spatial autoregressive(SAR) model and a generalized additive model(GAM), with the latter for auxiliary and comparative purposes. The annual fishery centroids were distributed broadly in an area bounded by 79.5°–82.7°W and 11.9°–17.1°S, while the monthly fishery centroids were spatially close and lay in a smaller area bounded by 81.0°–81.2°W and 14.3°–15.4°S. Our results show that the preferred environmental ranges for D. gigas offshore Peru were 20.9°–21.9°C for SST, 35.16–35.32 for SSS and 27.2–31.5 cm for SSH in the areas bounded by 78°–80°W/82–84°W and 15°–18°S. Monthly spatial distributions during October to December were predicted using the calibrated GAM and SAR models and general similarities were found between the observed and predicted patterns for the nominal CPUE of D. gigas. The overall accuracies for the hotspots generated by the SAR model were much higher than those produced by the GAM model for all three months. Our results contribute to a better understanding of the spatiotemporal distributions of D. gigas off shore Peru, and off er a new SAR modeling method for advancing fishery science.

关键词： Dosidicus gigas spatiotemporal distribution generalized additive model (GAM) spatial autoregressive(SAR) model offshore Peru

Automatic selection of permanent scatterers-based GCPs for refinement and reflattening in InSAR DEM generation

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International Journal of Digital Earth 2022年第1期15卷 954-974页

作者： yongjiu feng Yilun Zhou Yanling Chen Pengshuo Li Mengrong Xi Xiaohua Tong College of Surveying & Geo-Informatics Tongji UniversityShanghaiPeople’s Republic of China The Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration Tongji UniversityShanghaiPeople’s Republic of China Shanghai Astronomical Observatory Chinese Academy of SciencesShanghaiPeople’s Republic of China

In DEM generation using interferometric synthetic aperture radar(InSAR),the ground control points(GCPs)for refinement and reflattening are usually selected by manual selection,field surveying,GPS points and existing basemaps,which may not be completely suitable for consequent *** proposed a new method(auto-PS-GCP)of GCP selection based on permanent scatterers,which automatically defines the thresholds for the coherence,amplitude,and amplitude dispersion index to select permanent scatterer as the *** GCP thinning(auto-PS-GCP-Thin)was further conducted considering the point density,distances among points and terrain *** used a three-stage assessment that includes:(1)phase stability and intensity of the GCPs,(2)RMSEs of the elevations between GCPs and homonymous points in the reference DEM,and(3)generated DEM *** areas respectively in the plain,hilly and mountainous regions were selected to verify the proposed *** assessment using both SRTM DEM andICESat-2 points shows that the DEM accuracy of auto-PS-GCP-Thin was improved by 20%∼30%for different areas compared to the manual,where the best DEM accuracy of 4.71 m was found in the plain *** is concluded that the proposed methods are effective and reliable in various areas with different terrain conditions.

关键词： InSAR permanent scatterers automatic threshold definition ground control points DEM generation

The effects of factor generalization scales on the reproduction of dynamic urban growth

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Geo-Spatial Information Science 2022年第3期25卷 457-475页

作者： yongjiu feng Peiqi Wu Xiaohua Tong Pengshuo Li Rong Wang Yilun Zhou Jiafeng Wang Jinyu Zhao College of Surveying&Geo-Informatics Tongji UniversityShanghaiChina The Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration Tongji UniversityShanghaiChina

The production and selection of driving factors are essential to building a strong Cellular Automata(CA)model of dynamic urban growth simulation.A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation *** is challenging to evaluate the effectiveness of the selected driving factors because they have no true *** explore the impacts of the generalization scales,we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization(CAPSO)and applied them to simulate urban growth of Suzhou during *** results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance(ERD)of the Generalized Additive Models(GAMs).Specifically,the ERD declined from 51.9%to 45.9%as the factor scale became larger during 2000-2020,but there was a peak value(52.2%)at *** all simulations during 2000-2020,the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit(FOM),which respectively decreased by 3.1%and 4.4%as compared to the CA models with scale-free *** concluded that the driving factors at a smaller scale(200~400 m for point-like facilities and 7~14 m for line-like facilities)can build more accurate CA models to simulate urban growth patterns,and the optimal scale for factors can be identified using the *** study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.

关键词： Cellular automata urban growth driving factors scale effects generalized additive model

Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

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Journal of Arid Land 2024年第4期16卷 580-601页

作者： TANG Xiaoyan feng yongjiu LEI Zhenkun CHEN Shurui WANG Jiafeng WANG Rong TANG Panli WANG Mian JIN Yanmin TONG Xiaohua College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration Tongji UniversityShanghai 200092China

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future *** simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid *** developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,*** calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in *** results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,*** inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity *** suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation *** BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in *** the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong *** exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological *** results of this research can help to adjust urban planning and development *** mode

关键词： bat algorithm cellular automata(CA) probability-of-occurrence drought intensity algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model arid areas

海岸线遥感信息提取的元胞自动机方法及其应用

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中国图象图形学报 2012年第3期17卷 441-446页

作者：冯永玖韩震上海海洋大学海洋科学学院上海201306

从遥感资料中准确提取海岸线信息,是海岛海岸带遥感调查与海岸带综合管理的重要内容。为了对多类型海岸线进行准确提取,基于"自下而上"演化的元胞自动机方法,结合遥感图像方向信息权重,构建了一种新的岸线提取算法,并在Matlab环境下开... 详细信息

从遥感资料中准确提取海岸线信息,是海岛海岸带遥感调查与海岸带综合管理的重要内容。为了对多类型海岸线进行准确提取,基于"自下而上"演化的元胞自动机方法,结合遥感图像方向信息权重,构建了一种新的岸线提取算法,并在Matlab环境下开发实现包括均值平滑、海陆分离、离散地物去除和目标追踪的功能。以LandsatETM+遥感影像作为数据源,分别对人工海岸上海市长兴岛-横沙岛和淤泥质海岸上海市崇明东滩岸线进行了检测,验证了该岸线信息提取的元胞自动机算法。研究表明,基于元胞自动机的岸线提取方法,能够以较高精度提取人工海岸和淤泥质海岸的岸线信息。

关键词：海岸线提取元胞自动机方向信息遥感影像