Unfolding relations between land cover and farm management: high nature value assessment in complex silvo-pastoral systems

The high nature value (HNV) concept, proposed by the European Environment Agency, recognizes that specific farming systems support high biodiversity levels, mainly as a result of extensive management practices. The Portuguese montado is one of the most significant HNV systems in southern Europe. However, considering the great complexity characterizing these systems both in land management and in landscape structure, a specific context-oriented methodology to assess which montado areas are likely to be classified as HNV farmland is needed. In this sense, the aim of this study is to explore a methodological approach which makes it possible to assess land management pressures through land cover information on these complex silvo-pastoral systems. The proposed methodology was tested through a local case study in a montado area in southern Portugal, assessing the relation between management practices and a vegetation cover index. Results show that in similar montado areas different land management strategies varying in stocking density, but also in type of grazing animals and shrub control practices, configure different vegetation cycles. These results indicate there is a way to develop a straightforward methodology to assess the HNV value of Mediterranean silvo-pastoral systems based on land cover indicators. These would make it possible to assess the HNV of montado areas with direct and objective information and independent of farmer’s surveys or other farm-based data.     

“Follow That Car!” Mobilities of Enthusiasm in a Rare Car's Restoration

This article follows one object, a rare car, through its life cycle and in so doing engages the literatures in cultural geography on mobilities, materiality, and enthusiasm to show how all three are linked in the restoration of this one automobile, a Czechoslovakian-built Tatra T87 from 1941. Through archival and autoethnographic research, we trace the history of the Tatra company and then follow the production, sale, and subsequent ownerships of this one particular car through two wars and across two continents. We detail its restoration, tracing a geography of automobile enthusiasm that brought this rusted hulk back to life, one facilitated internationally by the Internet but that nevertheless demands extraordinary mobilities of cars, people, and parts. In following this one thing, we join other scholars in complicating the commodity- or global-value chains so often described as linear, to uncover instead complex entanglements of reuse, repurposing, and restoration. Because the car in question is our own, we mobilize our experiences with the car to reveal how the geographies of enthusiasms (automotive and otherwise) involve sustained and profound emotional engagements—emotional engagements often left out of academic accounts where, we suggest, they might be desirable as well.     

Using geospatial technology to strengthen data systems in developing countries: The case of agricultural statistics in India

Despite significant progress in the development of quantitative geography techniques and methods and a general recognition of the need to improve the quality of geographic data, few studies have exploited the potential of geospatial tools to augment the quality of available data methods in developing countries. This paper uses data from an extensive deployment of geospatial technology in India to compare crop areas estimated using geospatial technology to crop areas estimated by conventional methods and assess the differences between the methods. The results presented here show that crop area estimates based on geospatial technology generally exceed the estimates obtained using conventional methods. This suggests that conventional methods are unable to respond quickly to changes in cropping patterns and therefore do not accurately record the area under high-value cash crops. This finding has wider implications for commercializing agriculture and the delivery of farm credit and insurance services in developing countries. Significant data errors found in the conventional methods could affect critical policy interventions such as planning for food security. Some research and policy implications are discussed.     

Linear interpolation and joint model fitting of experimental transiograms for Markov chain simulation of categorical spatial variables

In Markov chain random field (MCRF) simulation of categorical spatial variables with multiple classes, joint modeling of a large number of experimental auto and cross-transiograms is needed. This can be tedious when mathematical models are used to fit the complex features of experimental transiograms. Linear interpolation can be used to perform the joint modeling quickly regardless of the number and the complexity of experimental transiograms. In this paper, we demonstrated the mathematical validity of linear interpolation as a joint transiogram-modeling method, explored its applicability and limitations, and tested its effect on simulated results by case studies with comparison to the joint model-fitting method. Simulations of a five-class variable showed little difference in patterns for interpolated and fitted transiogram models when samples were sufficient and experimental transiograms were in regular shapes; however, they neither showed large difference between these two kinds of transiogram models when samples were relatively sparse, which might indicate that MCRFs were not much sensitive to the difference in the detail of the two kinds of transiogram models as long as their change trends were identical. If available, expert knowledge might play an important role in transiogram modeling when experimental transiograms could not reflect the real spatial variation of the categorical variable under study. An extra finding was that class enclosure feature (i.e., a class always appears within another class) was captured by the asymmetrical property of transiograms and further generated in simulated patterns, whereas this might not be achieved in conventional geostatistics. We conclude that (i) when samples are sufficient and experimental transiograms are reliable, linear interpolation is satisfactory and more efficient than model fitting; (ii) when samples are relatively sparse, choosing a suitable lag tolerance is necessary to obtain reliable experimental transiograms for linear interpolation; (iii) when samples are very sparse (or few) and experimental transiograms are erratic, coarse model fitting based on expert knowledge is recommended as a better choice whereas both linear interpolation and precise model fitting do not make sense anymore.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.     

基于虚拟土理念的中国耕地资源价值核算初探

在经济高速发展带来土地资源日趋紧张的形势下,深化对耕地资源价值的认识,有助于实现耕地资源空间布局优化和耕地市场化流转.该文在明确虚拟土内涵与类型划分的基础上,从经济、社会和生态价值属性出发,运用收益还原法、替代法等方法,以省级为单位,分别测算了中国产能虚拟土、资源虚拟土和生态虚拟土价值,通过SPSS聚类分析和GIS空间分析,从虚拟土角度分析我国耕地资源价值的空间分布差异.研究结果表明,产能虚拟土得分最高的广东省是得分最低的内蒙古自治区的8.94倍;资源虚拟土得分最高的上海市是得分最低的黑龙江省的96.10倍;生态虚拟土得分最高的上海市是得分最低的贵州省的9.25倍.在空间分布上,产能虚拟土与纬度气候带分布基本一致,资源虚拟土分布受经济影响较大,而生态虚拟土分布则受气候和经济因素共同作用.     

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

ABSTRACT

An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfall–runoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in forecasting accuracy. The results reveal that SVD was highly effective in pre-processing of data-driven evaluations.     

Spatio-temporal evaluation of global gridded precipitation datasets across Iran

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.     

一次全球性新的地震活动高潮

This paper studies the temporal and spatial distribution of great global earthquakes( M W≥8.0) since 1900.We compare the two periods of upsurges of great earthquakes occurring in the middle of last century and beginning of this century.The former period took place between 1950 and 1965 during which 13 great earthquakes( M W≥ 8.0) occurred,including three events with moment magnitude greater than 9.0.The largest magnitude in this period reached 9.6.The latter period starts from the beginning of this century.In less than 12 years,15 great earthquakes have attacked the world with the largest magnitude being M W9.1.On the basis of comparison between these two upsurges of global earthquake activity,we infer that the ongoing high level of earthquake activity may continue for another five years or so.Numerous great earthquakes( M W≥8.0) and many large earthquakes( M W6.0~7.0) will occur globally in these five years.In addition,this paper also discusses the relationships between earthquake activity along the Sumatra segment of the Indian-Australia plate boundary and that in the Bayankala block in the middle of Qinghai-Tibetan plateau as well as in the blocks of the southern plateau.The results indicate that the Qinghai-Tibetan plateau,in particular its middle and southern parts,is a likely place for future earthquakes of magnitude over 7.0.     

影响b值计算误差的Monte Carlo实验研究

本文假设地震的发生与时间的关系遵循稳态泊松模型,假设地震震级M服从参数为1/β的指数分布,其中β=b/loge,在此基础上生成地震目录.用最小二乘法估算b值,分别从样本数量、震级间隔、震级误差三方面分析b值误差的大小.统计分析结果表明,b值的标准偏差随着样本数量的增多而减小;随着震级间隔的增大而减小;震级误差使b值的标准误差变大.本文可以为利用真实地震目录求b值时选择适当的方法提供参考.