利用卫星立体影像生产DEM的关键技术研究

在利用国产资源三号卫星立体影像制作DEM产品过程中,通过遥感手段利用NDVI和ND-WI对植被、水域等要素范围边界进行快速提取,实现了数据制作过程中滤波环节准确性和适应性的增强,进而达到了对DEM产品精度提升和制作效率提高的目的.由此形成的技术方案可以为全球地理信息资源建设与应用提供参考.     

扩散系数反演及其差分格式研究

空气污染预报属于正问题，而从污染物浓度来求解扩散系数则属于反问题。正问题和反问题有着本质的不同，在解的定义和求解方法上也有很大的区别。从最优控制的角度定义了大气边界层中垂直扩散系数反演问题的解，用伴随模式方法得到目标函数的梯度并求解反问题。研究中发现，反演的结果与模式差分格式的选取有关，与测试源的设置也有直接的关系。在经过多次数值试验后，对于误差的来源进行了理论分析，发现了反演结果与差分格式及测试源之间的联系，得到了满意的反演结果，并为实验测定扩散系数提供了依据。     

北京气温日变化特征的城郊差异及其季节变化分析

本文利用北京地区近4年67个自动气象站的逐小时气温观测资料,基于北京地区气温的日变化特征,通过分析日最高、最低气温出现时间的概率分布,研究了城区、郊区气温的日变化差异及季节特征.此外,进一步分析研究了不同单位时间间隔变温的日变化特征,及最大变温出现时间的概率分布情况.研究结果表明:平均而言,城区最高温度出现的时间偏晚,而最低温度出现的时间城区偏早于郊区,与郊区相比,北京城区站点温度的日变化特征更为一致,最高(低)温度出现的时间更加集中;温度日变化的特征随季节有明显的变化,最高温度出现时间在秋、冬两季最为集中,在春季和夏季较为分散;而最低温度出现时间在春、夏两季最为集中,在秋季和冬季最为分散.一天中正、负变温过程具有非对称特征,正变温是比较急剧的过程,负变温相对比较缓慢,北京城区站点的变温幅度小于郊区,春、秋和冬季变温幅度较大,夏季变温幅度最小.不同单位时间内变温速率的分析表明,最强的变温过程一般在3小时以内;最大变温出现时间的概率分布分析表明,最大正变温出现时间在冬季最为集中,夏季最为分散;而最大负变温在秋季最为集中,在春季最为分散.最高(低)温度、变温的城、郊特征差异主要是由于城市热容量比郊区大,且具有更多变化的复杂性而形成的.温度日变化的特征和其区域、季节差异性的揭示,不仅有助于更好地认识和理解区域气候特征和城市化对气温的影响,也可以为做好精细化的天气预报提供气候背景参考.     

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.