An AMSR-E data unmixing method for monitoring flood and waterlogging disaster

Spectral remote sensing technique is usually used to monitor flood and waterlogging disaster. Although spectral remote sensing data have many advantages for ground information observation, such as real time and high spatial resolution, they are often interfered by clouds, haze and rain. As a result, it is very difficult to retrieve ground information from spectral remote sensing data under those conditions. Compared with spectral remote sensing technique, passive microwave remote sensing technique has obvious superiority in most weather conditions. However, the main drawback of passive microwave remote sensing is the extreme low spatial resolution. Considering the wide application of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data, an AMSR-E data unmixing method was proposed in this paper based on Bellerby’s algorithm. By utilizing the surface type classification results with high spatial resolution, the proposed unmixing method can obtain the component brightness temperature and corresponding spatial position distribution, which effectively improve the spatial resolution of passive microwave remote sensing data. Through researching the AMSR-E unmixed data of Yongji County, Jilin Provinc, Northeast China after the worst flood and waterlogging disaster occurred on July 28, 2010, the experimental results demonstrated that the AMSR-E unmixed data could effectively evaluate the flood and waterlogging disaster.     

分布式“河道-沉陷区-地下水”水循环耦合模型——Ⅱ:模型应用

以淮南潘谢矿区2010年沉陷状况为背景,采用2001—2010年水文气象数据,应用构建的分布式"河道-沉陷区-地下水"耦合模拟模型对沉陷区水循环状况进行了模拟,并对模拟结果进行了检验。结果表明,河道汇流、水面降水、地下水和产流在沉陷区积水补给中所占比例分别为84.6%、8.8%、3.6%和3.0%,河道下泄、水面蒸发、渗漏和引水在沉陷区积水排泄中所占比例分别为80.6%、8.2%、1.3%和9.9%,沉陷区的自然水循环主要依赖于相连河道,其次为水面降水与蒸发,与地下水关联甚微。     

两种被动微波遥感混合像元分解方法比较

被动微波遥感混合像元分解是成功应用被动微波辐射计数据的关键,研究被动微波遥感混合像元分解方法具有重要的应用意义。提出基于地物分类信息的被动微波混合像元分解方法,通过模拟具有不同组分亮温分布的观测地区,将其与具有代表性的Bellerby提出的被动微波混合像元分解方法进行对比研究,分析两种分解方法的适用范围及影响其求解精度的主要因素。Bellerby的方法适合于观测地区中陆地组分亮温不变的情况,当观测地区陆地组分亮温发生变化时,笔者提出的方法能得到更加准确的分解结果。研究结果表明：笔者提出的方法进行混合像元分解后,其陆地组分亮温误差的平均值约为 0.1 K,水体组分亮温误差的平均值约为0.2 K;而采用Bellerby方法计算得到陆地组分亮温误差的平均值约为3.1 K,水体组分亮温误差的平均值约为5.9 K。     

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.     

A building polygonal object matching method based on minimum bounding rectangle combinatorial optimization and relaxation labeling

Volunteered geographic information (VGI) is an emerging phenomenon where anyone can create geographic information and share it with others. Compared with traditional authoritative geospatial data, it has several advantages, such as enriched data, instant updates, and low cost. The object matching method is widely used in VGI quality assessment and data updates. However, VGI matching faces certain challenges, such as the levels of detail that vary from object to object, the uneven distribution of data quality, and the automated matching requirement. To resolve these problems, this article proposes a new matching method that effectively combines the advantages of minimum bounding rectangle combinatorial optimization (MBRCO) and relaxation labeling. The proposed method (1) avoids setting the similarity threshold and weights and does not require training samples. This process is realized based on contextual information and optimization. (2) It overcomes the disadvantage that the MBRCO algorithm cannot distinguish adjacent buildings with similar shapes. Our approach is experimentally validated using two publicly available spatial datasets: OpenStreetMap and AutoNavi map. The experimental studies show that the proposed automatic matching method outperforms all the threshold-based MBRCO methods and achieves high accuracy with a precision of 97.8% and a recall of 99.2%.