Standardizing CPUE of <Emphasis Type="Italic">Ommastrephes bartramii</Emphasis> for Chinese squid-jigging fishery in Northwest Pacific Ocean

Generalized linear models (GLM) and generalized additive models (GAM) were used to standardize catch per unit fishing effort (CPUE) of Ommastrephes bartramii for Chinese squid-jigging fishery in Northwest Pacific Ocean. Three groups of variables were considered in the standardization: spatial variables (longitude and latitude), temporal variables (year and month) and environmental variables, including sea surface temperature (SST), sea surface salinity (SSS) and sea level height (SLH). CPUE was treated as the dependent variable and its error distribution was assumed to be log-normal in each model. The model selections of GLM and GAM were based on the finite sample-corrected Akaike information criterion (AICC) and pseudo-coefficient (Pcf) combined P-value, respectively. Both GAM and GLM analysis showed that the month was the most important variable affecting CPUE and could explain 21.3% of variability in CPUE while other variables only explained 8.66%. The interaction of spatial and temporal variables weakly influenced the CPUE. Moreover, spatio-temporal factors may be more important in influencing the CPUE of this squid than environmental variables. The standardized and nominal CPUEs were similar and had the same trends in spatio-temporal distribution, but the standardized CPUE values tended to be smaller than the nominal CPUE. The CPUE tended to have much higher monthly variation than annual variations and their values increased with month. The CPUE became higher with increasing latitude-high CPUE usually occurred in 145°E–148°E and 149°E–162°E. The CPUE was higher when SST was 14–21°C and the SLH from −22 cm to −18 cm. In this study, GAM tended to be more suitable than GLM in analysis of CPUE.     

Effect of spatial and temporal scales on habitat suitability modeling: A case study of Ommastrephes bartramii in the northwest pacific ocean

Temporal and spatial scales play important roles in fishery ecology, and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution. The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling, with the western stock of winter-spring cohort of neon flying squid(Ommastrephes bartramii) in the northwest Pacific Ocean as an example. In this study, the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature(SST) from remote sensing during August to October of 2003–2008 were used. We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales(0.5°, 1° and 2°), four longitude scales(0.5°, 1°, 2° and 4°), and three temporal scales(week, fortnight, and month). The coefficients of variation(CV) of the weekly, biweekly and monthly suitability index(SI) were compared to determine which temporal and spatial scales of SI model are more precise. This study shows that the optimal temporal and spatial scales with the lowest CV are month, and 0.5° latitude and 0.5° longitude for O. bartramii in the northwest Pacific Ocean. This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts. We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.     

Standardization of CPUE for Chilean jack mackerel (Trachurus murphyi) from Chinese trawl fleets in the high seas of the Southeast Pacific Ocean

The generalized linear model (GLM) and generalized additive model (GAM) were applied to the standardization of catch per unit effort (CPUE) for Chilean jack mackerel from Chinese factory trawl fishing fleets in the Southeast Pacific Ocean from 2001 to 2010 by removing the operational, environmental, spatial and temporal impacts. A total of 9 factors were selected to build the GLM and GAM, i.e., Year, Month, Vessel, La Niña and El Niño events (ELE), Latitude, Longitude, Sea surface temperature (SST), SST anomaly (SSTA), Nino3.4 index and an interaction term between Longitude and Latitude. The first 5 factors were significant components in the GLM, which in combination explained 27.34% of the total variance in nominal CPUE. In the stepwise GAM, all factors explained 30.78% of the total variance, with Month, Year and Vessel as the main factors influencing CPUE. The higher CPUE occurred during the period April to July at a SST range of 12–15°C and a SSTA range of 0.2–1.0°C. The CPUE was significantly higher in normal years compared with that in La Niña and El Niño years. The abundance of Chilean jack mackerel declined during 2001 and 2010, with an increase in 2007. This work provided the relative abundance index of Chilean jack mackerel for stock assessment by standardizing catch and effort data of Chinese trawl fisheries and examined the influence of temporal, spatial, environmental and fisheries operational factors on Chilean jack mackerel CPUE.     

The impact of spatial autocorrelation on CPUE standardization between two different fisheries

Catch per unit of eff ort(CPUE) data can display spatial autocorrelation. However, most of the CPUE standardization methods developed so far assumes independency of observations for the dependent variable, which is often invalid. In this study, we collected data of two fisheries, squid jigging fishery and mackerel trawl fishery. We used standard generalized linear model(GLM) and spatial GLMs to compare the impact of spatial autocorrelation on CPUE standardization for different fisheries. We found that spatialGLMs perform better than standard-GLM for both fisheries. The overestimation of precision of CPUE estimates was observed in both fisheries. Moran's I was used to quantify the level of autocorrelation for the two fisheries. The results show that autocorrelation in mackerel trawl fishery was much stronger than that in squid jigging fishery. According to the results of this paper, we highly recommend to account for spatial autocorrelation when using GLM to standardize CPUE data derived from commercial fisheries.     

Modeling a habitat suitability index for the eastern fall cohort of Ommastrephes bartramii in the central North Pacific Ocean

The eastern fall cohort of the neon flying squid, Ommastrephes bartramii, has been commercially exploited by the Chinese squid jigging fleet in the central North Pacific Ocean since the late 1990s. To understand and identify their optimal habitat, we have developed a habitat suitability index (HSI) model using two potential important environmental variables - sea surface temperature (SST) and sea surface height anomaly (SSHA) - and fishery data from the main fishing ground (165°-180°E) during June and July of 1999-2003. A geometric mean model (GMM), minimum model (MM) and arithmetic weighted model (AWM) with different weights were compared and the best HSI model was selected using Akaike’s information criterion (AIC). The performance of the developed HSI model was evaluated using fishery data for 2004. This study suggests that the highest catch per unit effort (CPUE) and fishing effort are closely related to SST and SSHA. The best SST- and SSHA-based suitability index (SI) regression models were SISST-based = 0.7SIeffort-SST + 0.3 SICPUE-SST, and SISSHA-based = 0.5SIeffort-SSHA + 0.5SICPUE-SSHA, respectively, showing that fishing effort is more important than CPUE in the estimation of SI. The best HSI model was the AWM, defined as HSI=0.3SISST-based+ 0.7SISSHA-based, indicating that SSHA is more important than SST in estimating the HSI of squid. In 2004, monthly HSI values greater than 0.6 coincided with the distribution of productive fishing ground and high CPUE in June and July, suggesting that the models perform well. The proposed model provides an important tool in our efforts to develop forecasting capacity of squid spatial dynamics.     

A comparative study of spatially clustered distribution of jumbo flying squid (<Emphasis Type="Italic">Dosidicus gigas</Emphasis>) offshore Peru

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 Niño 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 Niño, suggesting that such large-scale climate variations could reduce the clustering level of D. gigas. The catches also showed that warm El Niño 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.     

基于Shapefile的基态修正模型设计与实现

 本文通过对Shapefile文件进行扩展,使用改进的基态修正模型,构建了一种ArcGIS的时空数据模型,支持对时空变化过程的存储、分析和模拟,同时支持传统的GIS空间分析功能。本研究还设计和实现了一种基于多时态矢量数据集的时空数据变化过程发现和时空数据转换算法,此算法可以自动提取多个时态之间的变化过程,将多时态数据转换成基于Shapefile的时空数据模型存储。鉴于该数据模型,设计和实现了时空数据的快照恢复、变化查询、历史演化分析和数据更新方法,验证了模型的可应用性。     

一种逐级合并OD流向时空联合聚类算法

现有OD流向聚类多将O点和D点相分离或者将OD流向看作4维空间的数据点进行聚类处理,忽视了流向长度、方向、时间对流向聚类的影响。本文以流向作为研究对象,提出一种基于流向间相似性度的逐级合并OD流向时空联合聚类算法。首先在充分研究OD流向的空间信息和时间信息的基础上,构建合理的OD流向间时空相似性度量方法,对OD流向间的时空相似性进行量化;然后提出逐级合并OD流向聚类策略,优化类簇合并的顺序,以减少层次聚类的时间开销,实现OD流向的时空联合聚类。以成都市的滴滴出行OD数据和纽约市出租车数据为例对本文方法进行了验证,结果表明：① 本算法聚类获得的流向类簇不仅带有空间特征还具备时间特征;② 在不同参数下本方法可以得到不同时空尺度的聚类结果;③ 与现有较高水平的流向聚类算法相对比,本文方法的聚类效果更好。这体现在流向类簇内部的流向之间有着充分的相似性,以及本文方法不仅可以提取出显著的流向类簇,还可以提取出非热点区域之间的流向类簇。本算法顾及空间因素和时间因素,可以通过调整时空相似性度量方法中的时间参数和空间参数以实现不同时空尺度的流向聚类,这使得从不同时空角度研究城市居民出行模式成为可能。本文提出的OD流向时空联合聚类算法从联合时间信息和空间信息的角度获得对运动数据的新见解,有助于合理全面地研究居民的移动模式、区域之间的空间联系、已知出行结构的确定以及出行目的的探索,是后续一系列分析工作的基础。     

Spatial and temporal patterns of soil nitrogen distribution under different land uses in a watershed in the hilly area of purple soil, China

Quantification of soil spatial and temporal variability at watershed scale is important in ecological modeling, precision agriculture, and natural resources management. The spatio-temporal variations of soil nitrogen under different land uses in a small watershed (12.10 km²) in the hilly area of purple soil at the upper reaches of the Yangtze River in southwestern China were investigated by using conventional statistics, geostatistics, and a geographical information system in order to provide information for land management and control of environmental issues. A total of 552 soil samples (0 to 15 cm) from 276 sites within the watershed were collected in April and August of 2011, and analyzed for soil total nitrogen (STN) and nitrate nitrogen (NO3-N). We compared spatial variations of STN and NO3-N under different land uses as well as the temporal variations in April (dry season) and August (rainy season). Results showed that STN contents were deeply affected by land-use types; median STN values ranged from 0.94 to 1.27 g·kg^?1, and STN contents decreased in the following order: paddy field > forestland > sloping cropland. No significant difference was found for STN contents between April and August under the same land use. However, NO3-N contents were 23.26, 10.58, and 26.19 mg·kg^?1 in April, and 1.34, 8.51, and 3.00 mg·kg^?1 in August for the paddy field, sloping cropland and forestland, respectively. Nugget ratios for STN indicated moderate spatial dependence in the paddy field and sloping cropland, and a strong spatial dependence in forestland. The processes of nitrogen movement, transformation, absorption of plant were deeply influenced by land use types; as a result, great changes of soil nitrogen levels at spatial and temporal scales were demonstrated in the studied watershed.     

顾及语义尺度的时空对象属性特征动态表达

全空间地理信息系统的建设和智慧城市等GIS应用的发展,需对各类地理实体或地理现象抽象成的时空对象采集多类型的属性数据,这些属性数据一般具有多尺度、多维度、动态性的特点,带给时空对象属性特征的表达和管理一定的挑战。本文针对当前时空对象属性特征表达方法中存在的组织结构不清晰、存储冗余、语义异构等表达问题,提出了一套顾及语义尺度和动态特性的属性特征的表达和操作方法。该方法基于概念分类理论实现时空对象属性信息的分类组织,在属性特征表达中引入独立于时空对象空间特征的时间标记,结合属性表达的原始集族和更新集族来记录和管理不同时间节点下的时空对象属性特征;进而面向具有不同语义尺度表达需求的属性特征,设置知识参考表达集合,并在属性特征表达中增加语义尺度标识,对其语义尺度及不同语义尺度间的转换关系进行描述。最后,基于独立时间标记、知识参照表达集合及语义尺度标识,给出属性信息表达在时间尺度转换和语义尺度转化的操作方法,并举例给出了该属性特征动态表达和操作方法的一种实现。本文提出的方法有助于减少时空对象数据模型的存储冗余,提高其属性信息的操作效率,初步实现了时空对象属性特征的多时间尺度和多语义尺度的认知方式,为时空对象属性特征的精细化管理提供了新思路。     

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2004年北太平洋柔鱼钓产量分析及作业渔场与表温的关系

根据2004年5~11月我国鱿钓船在北太平洋生产数据,结合表温资料,按经纬度1°×1°的格式,利用Marineexplorer 4.0软件作图进行柔鱼钓产量及渔场与表温的关系分析。结果表明,5~7月在160°E以东海域作业,产量较低;8~10月在150°~160°E海域作业,为生产作业的产量高峰期,占总产量的62.5%;11月在150°E以西海域作业,产量也较低。在150°E以西海域CPUE最高,150°~160°E中部海域次之,160°E以东海域最低。作业渔场的适宜表温呈现出季节性变化。各月适宜表温分别为:5月12~14℃;6月15~16℃;7月14~16℃;8月18~19℃;9月16~17℃;10月15~16℃;11月12~13℃。     

Monitoring Spatio-temporal Variance of an Extreme Heat Event Using Multiple-source Remote Sensing Data

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening(Ts HARP) method and a binary linear model are compared to downscale the original daytime FengY un 2 F(FY-2 F) land surface temperature(LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land(SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error(RMSE) of the LST downscaled from the binary linear model is 1.30℃ compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78℃. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37℃ in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37℃ and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data.     

Regional Differences of China′s Urban Expansion from Late 20th to Early 21st Century Based on Remote Sensing Information

This study investigated the regional differences of China′s urban land expansion from the late 1980s to the year of 2008, based on the spatio-temporal analysis of CLCD (China′s land cover/land use database) datasets which were mainly produced from remote sensing imagery data. A newly defined urbanization level index (UI), based on urban land area, is proposed to describe Chinese urban expansion process at 1 kilometer, provincial, regional, and na-tional scales, together with the absolute urban expansion index (UEa) and the relative urbanization expansion index (UEr). The results indicate that the percentages of total land area occupied by urban in the late 1980s, 1995, 2000, 2005, and 2008 were approximately 0.25%, 0.32%, 0.33%, 0.43% and 0.52% of China′s total land area, respectively. Between the late 1980s and 2008, the total urban expansion in the mainland of China was 2.645 × 104 km2, resulting in an annual urban expansion area of about 1322.7 km2/yr, with the UEr of 111.9%. This study also finds that there has been an obvious spatial gradient of urbanization ratio running from the east coast to the west inland, and the urbanization gaps among different regions have persisted over the past two decades. The study also reveals obvious temporal varia-tions of the urbanization rates. There was very little urban growth during the period of 1995-2000 due to the governmental policy factors.     

Diagnostic model construction and example analysis of habitat degradation in enclosed bay: II. spatiotemporal variations in habitat degradation in Sansha Bay

The process of habitat degradation varies in habitat type and driving force which shows certain spatial and temporal heterogeneity on regional scales. In the present study, a new diagnostic model for enclosed bay habitat degradation was established, with which the spatial and temporal variation patterns of habitat degradation during 1991–2012 in Sansha Bay, Fujian, China was investigated. The results show that anthropogenic disturbance is the major controlling factor for the habitat degradation in large temporal heterogeneity in the bay. On the other hand, the habitat degradation experienced signifi cant spatial variations among six sub-bays. Under the joint action of temporal and spatial heterogeneity, the degradation trend in growing scale shows a more signifi cant correlation with the distribution of local leading industries along shorelines. Therefore, we quantifi ed the main characters of habitat degradation in Sansha Bay, and have understood the relationship between the status of habitats spatio-temporal variation value and the main controlling factor leading to the changes. However, a defi ciency of this research is the lack of or inaccessible to the detailed data, which shall be better solved in the future study for accessing more data from more sources.     

Spatiotemporal factors affecting fish harvest and their use in estimating the monthly yield of single otter trawls in Putuo district of Zhoushan, China

We used generalized additive models (GAM) to analyze the relationship between spatiotemporal factors and catch, and to estimate the monthly marine fishery yield of single otter trawls in Putuo district of Zhoushan, China. We used logbooks from five commercial fishing boats and data in government’s monthly statistical reports. We developed two GAM models: one included temporal variables (month and hauling time) and spatial variables (longitude and latitude), and another included just two variables, month and the number of fishing boats. Our results suggest that temporal factors explained more of the variability in catch than spatial factors. Furthermore, month explained the majority of variation in catch. Change in spatial distribution of fleet had a temporal component as the boats fished within a relatively small area within the same month, but the area varied among months. The number of boats fishing in each month also explained a large proportion of the variation in catch. Engine power had no effect on catch. The pseudo-coefficients (PCf) of the two GAMs were 0.13 and 0.29 respectively, indicating the both had good fits. The model yielded estimates that were very similar to those in the governmental reports between January to September, with relative estimate errors (REE) of <18%. However, the yields in October and November were significantly underestimated, with REEs of 36% and 27%, respectively.     

Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model

This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.     

多模态地理大数据时空分析方法

多模态地理大数据时空分析旨在融合地理大数据的多模态信息发现有价值的时空分布规律、异常表现、关联模式与变化趋势,是全空间信息系统的核心研究内容,并有望成为推进地理学人地关系研究的重要突破口。为应对地理大数据时代的新机遇与挑战,本文围绕4类核心的时空分析方法（时空聚类分析、时空异常分析、时空关联分析与时空预测分析）,系统归纳了国内外研究现状,探讨了时空分析中多尺度建模、多视角协同、多特征认知与多特性表达的研究难点。进而,介绍了多模态地理大数据时空聚类、异常、关联与预测分析模型,更加全面、客观、精准地认知与理解时空大数据中潜在的地理知识,并且能够在气象环境监测、公共安全管理、城市设施规划等多个应用领域发挥关键作用。     

面向对象的地理实体时空位置多粒度表达

同一地理实体在不同的时空粒度下会表现出相异的位置动态变化规律。近年来,如何对地理实体在不同时空粒度下的时空位置进行组织和表达成为GIS研究的热点之一。本文基于面向对象的思想,设计了“三级空间”和“0-1位置变化序列”,并由此提出一种地理实体时空位置的多粒度表达方法。在实体时空位置的多粒度描述方面,对于任一地理实体,空间维度上构建一种具有不同空间粒度的“全局—相对—对象”三级空间;时间维度上将不同时段或时刻转换为一系列不同时间粒度的离散时间点。在实体时空位置的多粒度存储组织方面,将地理实体时空位置的变化过程划分为不同阶段,对该实体在不同时间点下的空间位置信息设置不同的存储方式,可合理减少信息冗余。在实体时空位置的多粒度转换方面,提出基于三级空间的递进认知、时间点与时段之间快速转换等策略,初步实现了地理实体时空位置在不同时空粒度下的转换。该方法可有效地描述地理实体在可变时空粒度下的时空位置,为时态GIS和多粒度时空数据库的建立提供参考。     

Simulating impact of larch caterpillar (<Emphasis Type="Italic">Dendrolimus superans</Emphasis>) on fire regime and forest landscape in Da Hinggan Mountains,Northeast China

Larch caterpillar (Dendrolimus superans) is very common in the Da Hinggan Mountains, Northeast China, affecting fire regime and forest ecosystem change at large spatio-temporal scales. In this study, we used a spatially explicit landscape model, LANDIS, to simulate the changes of fire regime and forest landscape under four larch caterpillar disturbance intensity levels scenarios in Huzhong forest area, northern of Da Hinggan Mountains. The results indicate that larch caterpillar disturbances would decrease ...