青海省北部重点成矿带与矿集区矿山地质环境遥感监测研究

为了查明青海省重点成矿带与矿集区矿山地质环境的现状及变化趋势,利用DEM和IKO-NOS、QUICKBIRD、WORLDVIEW-2、SPOT-5等卫星遥感解译资料对该地区的矿山地质环境进行了研究。结果表明,盐湖矿产开发占地面积最大,其次为煤矿,且煤矿区和盐湖矿区占地面积扩展迅速。矿山开发所引发的地质灾害(隐患)集中分布于煤矿开采区,主要表现为塌陷坑、地裂缝、崩塌(隐患)、泥石流(隐患)和地面塌陷区(隐患)等。环境污染主要表现为水体污染、粉尘污染和尾矿污染,而尾矿污染较为突出。     

基于遥感影像的地理特征信息的提取与分析

以云南省会泽县为例,基于ETM+、ASTER、DEM等遥感影像数据运用ArcGIS、ERDAS等遥感软件提取研究区的植被、地形、坡度坡向等一系列地理特征信息,并生成相关图件,为本区的农业规划、水土流失、地质灾害、资源开发、土壤侵蚀等相关研究提供多方面的空间信息参考.     

实测与卫星观测德基水库水质变化

德基水库集水区的气候条件适合温带水果及高冷蔬菜、茶叶的种植,德基水库集水区上游遍布果园、菜园与茶园。因此,每逢大雨来临时,大量的残留肥料常遭冲刷至水库中。这些残留的肥料中含有大量的氮、磷等营养盐,足以提供水库微生物的生长繁殖所需,也因此造成水库中二角多甲藻的大量繁殖,进而形成藻华现象。研究使用Landsat TM卫星数据,搭配传统监测模式,以监督式影像分类法分析与监测德基水库1995年的藻华现象。研究结果显示,以Landsat TM卫星数据监测德基水库的藻华现象,其准确率可达87.5%以上。由影像分类的结果可得知,德基水库二角多甲藻之藻华现象发生较严重的时期为夏天,而冬天则较为轻微。此现象发生的原因,应与集水区上游的农业经营模式息息相关。此外,德基水库藻华现象发生的区域及其程度也经由实地调查与卫星影像监测,一一获得证实。     

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework,Chania, Northwestern Crete,Greece

Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year⁻¹) for some watersheds showing extended erosion and demanding the attention of local administrators.     

基于分布式光纤传感技术的渗流监测理论研究

从阐述多孔介质传热过程出发,逐一分析了在非渗流及渗流情况下,光纤与多孔介质之间的传热过程,详细分析了光纤与水流对流的传热过程,推导了多孔介质中渗流水和光纤之间的换热系数计算公式。在一定假设条件下,推导了导热系数（渗流流速）、加热功率和温升的非渗流情况与渗流情况监测理论方程,为分布式光纤传感技术监测渗流的应用提供了坚实的理论基础。     

A modified land surface temperature split window retrieval algorithm and its applications over China

Due to the difficulties in correcting the influences of the atmosphere absorbability and the Earth surface emissivity diversification, the retrieval of LST (land surface temperature) from satellite data is a challenging task. In this paper, a modified Becker's split window LST inversion algorithm is developed for retrieving LST from the NOAA-16/17 AVHRR data. A new set of parameters for Becker's LST algorithm is proposed. The algorithm is developed from a surface brightness temperature dataset generated from the MODTRAN program, which uses a range of surface parameters and atmospheric quantities as inputs. The 10-day composites of the channels 4 and 5 brightness temperature data of NOAA-17 AVHRR (1-km resolution) are used to generate the clear-sky LST. As a validation of the algorithm, the retrieved LST is compared with MODIS LST of same period and area. The two LST products are found to be consistent, with the absolute difference being about 2.5 K for most areas. The NOAA retrieved LST is also compared with in-situ ground surface 0-cm measurements taken from 257 meteorological stations, which cover overall China area for the three periods of satellite observations. The comparison shows that the correlation between the retrieved LST and in-situ measurements is over 0.90 and the RMSE (root mean square error) is about 3.4 K.     

Bayesian foreground analysis with CMB data

The quality of CMB observations has improved dramatically in the last few years, and will continue to do so in the coming decade. Over a wide range of angular scales, the uncertainty due to instrumental noise is now small compared to the cosmic variance. One may claim with some justification that we have entered the era of precision CMB cosmology. However, some caution is still warranted: The errors due to residual foreground contamination in the CMB power spectrum and cosmological parameters remain largely unquantified, and the effect of these errors on important cosmological parameters such as the optical depth τ and spectral index n_s is not obvious. A major goal for current CMB analysis efforts must therefore be to develop methods that allow us to propagate such uncertainties from the raw data through to the final products. Here we review a recently proposed method that may be a first step towards that goal.     

基于声波探测海底浅层沉积物特性的方法研究

利用声波在水中的传输特性进行海底浅层沉积物特性的探测。在实践应用中有实验室方法、现场测量方法和海底声学遥测方法,并研制出大量的仪器。由于海上和海底实际条件的限制,声波探测方法都存在一定的不足之处,由此提出了声衰减测试沉积物特性以及建立实验室模拟海底环境探测方法的设想。     

Influence of the vertical distribution of cyanobacteria in the water column on the remote sensing signal

An increased intensity of cyanobacterial blooms and their potentially harmful effects have attracted the attention of environmental agencies, water authorities and the general public worldwide. Reliable operational monitoring methods of coastal waters, lakes and ponds are needed. Mapping of the surface extent of cyanobacterial blooms with remote sensing is straightforward, but recognizing waters dominated by cyanobacteria throughout the water column and quantitative mapping of cyanobacterial biomass with remote sensing is more complicated. Unlike most algae, cyanobacteria can regulate their buoyancy and move vertically in the water column. We used the Hydrolight 4.2 radiative transfer model and the specific optical properties of three species of cyanobacteria to study the impact of vertical distribution of cyanobacteria on the remote sensing signal. The results show that the vertical distribution of cyanobacteria in the water column has a significant impact on the remote sensing signal. This result indicates that developing remote sensing methods for quantitative mapping of cyanobacterial biomass is much more complex than quantitative mapping of an algal biomass that is uniformly distributed in the top mixed layer of water column.