A GIS-based decision support system for risk assessment of wind damage in forest management

In this study a GIS-based decision support system (DSS) was built for assessing the short- and long-term risk of wind damage in boreal forests. This was done by integrating a forest growth model SIMA and a mechanistic wind damage model HWIND into geographical information system software (ArcGIS 8.2) as a toolbar (DLL) using ArcObjects in ArcGIS and Visual Basic 6. In this DSS complex problems are solved within program so that forest gaps, edge stands and edges are automatically tracked when the forest structure changes over time as a result of forest growth dynamics and management. This DSS can be used to assess the risk of wind damage to Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and birch (Betula spp.) stands, regarding the number of stands and area at risk and length of vulnerable edges of these risk stands at certain critical wind speed classes (i.e. corresponding the maximum wind speed a tree/stand can resist). This DSS can helps forest managers to analyse and visualise (charts, maps) the possible effects of forest management, such as clear-cuts, on both the immediate and long-term risks of wind damage at both stand and regional level.     

基于Vega的虚拟城市仿真系统设计研究

虚拟城市仿真系统是当今虚拟现实技术研究的一个重要热点,它既具备场景表达的直观友好,也具备空间分析的抽象特征．即综合三维场景漫游与二维抽象分析的优势。该文阐述了在VC环境,对Vega所提供的API进行二次开发．实现虚拟城市三维场景的漫游,多种场景的渲染,并提供GIS基本查询的系统设计方案。设计思路包括三个过程：三维场景部分、二维分析部分、数据库管理部分,文章分别对它们的关键技术加以论述。     

太湖蓝藻水华遥感监测信息系统设计及其关键技术

快速提取太湖蓝藻水华空间分布信息对于制定相关应急预案有重要意义,构建太湖蓝藻水华遥感监测信息系统可为之提供有力支持。从系统总体结构和系统流程角度介绍系统设计思路,并阐述研发本系统的关键技术。采用系统配置与单体软件模式技术实现菜单驱动的系统架构,并在影像数据自动化预处理基础上,运用蓝藻水华遥感监测模型,实现蓝藻水华空间分布信息自动化提取,使得本系统在太湖蓝藻水华遥感监测的业务化运行中取得明显效果。     

江苏省南水北调受水区需水计算模型初探

选取南水北调东线江苏省受水区作为研究区域,对受水区2020规划年用水户进行概化,概化为工业用水户23个、农业用水户92个、生活用水户25个、生态用水户11个和船闸用水户23个．建立研究区需水模型,计算本研究区在3种年型下的需水量．研究表明：2020规划水平年在P=95％、P=75％和P=50％年型下,受水区需水量分别为239．15×10^8m3、195．94×10^8m3和184．76×10^8m3;与参考值相比,文章计算需水量相对误差分别为＋12．53％（P=95％）、＋2．64％（P=75％）和＋6．58％（P=50％）,误差不大,说明文章建立的需水模型能较好的模拟研究区的需水量．     

信江流域土地利用变化的径流响应研究

针对信江流域土地利用变化的径流响应,基于分布式水文模型（SWAT）及SUFI 2算法,利用流域DEM、土地利用、土壤、气象等时空数据并结合GIS和RS技术,对流域土地利用变化的径流响应进行定量研究。结果表明,率定期和验证期的模拟值与实测流量过程拟合程度较优,相关系数（R2）和纳西效率系数ENS均高于0.90、相对误差|Re|均小于2%,表明SWAT模型应用于该流域径流模拟的精度较高;在相同气候条件下,径流年内分配规律为2000年土地利用情景较1990年土地利用条件下月径流增加,年际分配规律为两期土地利用情景下的年际径流量变化趋势较为一致,2000年略大于1990年。     

From blueprint to action: The transformation of the planning paradigm for desakota in China

With a case study of Chaonan in Guangdong Province, this study examines China's desakota and its planning paradigm. After three decades of high-speed growth, the marginal areas of Guangdong Province — the lab of China's market-oriented reform and open door policy, are still fraught with desakotas, semi-urbanized areas of mixed landscape, economic and administrative systems. Chaonan faces a number of challenges, such as economic slowdown, poor infrastructure, environmental pollution, and so on. We found that China's desakota is driven by decades of rural industrialization, and this is now becoming a major challenge to master planning, the paradigm of which mainly comes from the rational theories of early western countries. As the traditional paradigm emphasizes the overall blueprint, there is a mismatch between planning orientation and spontaneous investment demand. Planning implementation thus is almost impossible. As such, we take the master plan of Chaonan as a case to integrate both blueprints and actions. To turn traditional blueprint-led planning into action-led planning, we highlight both recent and long-term actions, to make practical improvements to various facets, socio-economic upgrading, socio-spatial transformation, and environmental sustainability. Above all, we argue that action-led planning is the key to the transformation of desakotas in China.     

Classification of urban morphology with deep learning: Application on urban vitality

There is a prevailing trend to study urban morphology quantitatively thanks to the growing accessibility to various forms of spatial big data, increasing computing power, and use cases benefiting from such information. The methods developed up to now measure urban morphology with numerical indices describing density, proportion, and mixture, but they do not directly represent morphological features from the human's visual and intuitive perspective. We take the first step to bridge the gap by proposing a deep learning-based technique to automatically classify road networks into four classes on a visual basis. The method is implemented by generating an image of the street network (Colored Road Hierarchy Diagram), which we introduce in this paper, and classifying it using a deep convolutional neural network (ResNet-34). The model achieves an overall classification accuracy of 0.875. Nine cities around the world are selected as the study areas with their road networks acquired from OpenStreetMap. Latent subgroups among the cities are uncovered through clustering on the percentage of each road network category. In the subsequent part of the paper, we focus on the usability of such classification: we apply our method in a case study of urban vitality prediction. An advanced tree-based regression model (LightGBM) is for the first time designated to establish the relationship between morphological indices and vitality indicators. The effect of road network classification is found to be small but positively associated with urban vitality. This work expands the toolkit of quantitative urban morphology study with new techniques, supporting further studies in the future.     

Nonparametric estimation of pair-copula constructions with the empirical pair-copula

A pair-copula construction is a decomposition of a multivariate copula into a structured system, called regular vine, of bivariate copulae or pair-copulae. The standard practice is to model these pair-copulae parametrically, inducing a model risk, with errors potentially propagating throughout the vine structure. The empirical pair-copula provides a nonparametric alternative, which is conjectured to still achieve the parametric convergence rate. Its main advantage for the user is that it does not require the choice of parametric models for each of the pair-copulae constituting the construction. It can be used as a basis for inference on dependence measures, for selecting an appropriate vine structure, and for testing for conditional independence.     

基于MSWEP数据的太湖流域降水特性分析

基于高密度的地表雨量站网资料生成基准降水量,采用分类指标、体积分类指标和定量指标,在0.1°×0.1°的栅格尺度上解析了1979-2016年汛期MSWEP(multi-source weighted-ensemble precipitation)日降水数据的误差特征,并剖析了MSWEP对于太湖流域多种特征降水要素的辨识能力。结果表明:MSWEP对日降水量存在一定程度低估,且在山丘区和太湖湖区的降水精度相对偏低,但与地表降水之间总体上具有较强一致性;MSWEP对太湖流域不同时段的极值降水量和台风降水量、梅雨量和汛期降水量也具有相当表征能力;MSWEP既反映了太湖流域西部特别是浙西山丘区地形对降水的强化作用,还反映了太湖湖区大型水域对降水的抑制作用;MSWEP也探测到受城市化影响太湖流域年最大1 d降水量在平原区东部存在高值地带。