遥感技术在古丹阳湖演变研究中的应用

根据1987—1988年太湖实测水质数据,利用系统动态聚类分析方法,将17个指标按营养盐、有机质、有毒有害物质三种污染类型进行综合评估,获得了太湖水域各类污染指标空间分布态势及差异特点,揭示了太湖现存的三种污染模式及地域分布状况,为进一步治理和改善太湖水质提供依据。     

石家庄地震前兆台网月评价系统

基于VB语言,利用C/S架构,编写石家庄地震前兆台网月评价系统,设置不同功能单元,开展仪器运行监控、台网日常运维监控,通过界面选择相应日期,读取省前兆台网中心发布的《运行监控日报》等Excel文件,使用input box函数等在文本框中输入、统计、计算,并显示地震前兆台网月评价成绩及台网运行情况。     

软土动力特性动三轴试验研究

许多学者对软土在动力作用下的变形特性进行了大量的研究,并提出了相应的经验公式,但其经验公式参数较多且试验复杂,不易被一般工程技术人员掌握运用。本文采用天津滨海地区软土为研究对象,进行钻探取样、波速测试、室内静力学和动三轴试验,获取该地区软土的静、动力学参数,并利用相对简单的模型对残余应变和软化指数随振次的变化规律采用最小二乘法进行拟合,取得很好的效果。成果表明,利用简单的模型对软土的动力特性进行区域性研究是可行的。该成果可为今后软土动力特性和软土震陷计算方法研究提供参考和基础性资料。     

Characteristics and Analysis of the Seismic Damage from the MS7.0 Jiuzhaigou Earthquake in the Area between Songpan and Jiuzhaigou

This paper expounds the features of the buildings and analyzes the seismic disaster characteristics of the Jiuzhaigou M_S7. 0 earthquake in the area between Songpan and Jiuzhaigou. New buildings (especially the frame structure) had good anti-seismic performance,but brick-wood structures and brick-concrete structures sustained large amounts of damage in the earthquake. By computing the seismic damage index,we found that the seismic damage index of the frame structure was far less than that of civil structures and brick-wood structures. The seismic damage index of frame structures were all zero in the Ⅵ area,and increased rapidly with the increase of intensity,but the increasing range was reduced. We also discussed how to evaluate the intensity in areas where there was a lack of buildings or there was only one structure type,which can be referenced in future field work.     

Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Extracting water-related features using reflectance data and principal component analysis of Landsat images

This study aimed to map water features using a Landsat image rather than traditional land cover. We involved the original bands, spectral indices and principal components (PCs) of a principal component analysis (PCA) as input data, and performed random forest (RF) and support vector machine (SVM) classification with water, saturated soil and non-water categories. The aim was to compare the efficiency of the results based on various input data. Original bands provided 93% overall accuracy (OA) and bands 4–5–7 were the most informative in this analysis. Except for MNDWI (modified normalized differenced water index, with 98% OA), the performance of all water indices was between 60 and 70% (OA). The PCA-based approach conducted on the original bands resulted in the most accurate identification of all classes (with only 1% error in the case of water bodies). We therefore show that both water bodies and saturated soils can be identified successfully using this approach.     

大气散射对采用归一化植被指数进行赤潮遥感监测的影响研究

1　引言近年来赤潮灾害在我国海域频繁发生,对沿海环境、经济和人民生产生活都造成一定的影响,因此对赤潮进行实时监测并业务化运行就显得尤其重要.赤潮遥感监测是目前常用手段之一,在实践中取得了一定的效果,但到目前为止还没有一种成熟的算法能用于业务化监测赤潮的发生.很多研究人员借鉴归一化植被指数(NDVI)阈值法、海表温度(SST)异常等来判断赤潮的发生,但归一化植被指数阈值法经常会出现误判或无法区分正常与异常水体的情况.该阈值范围由先验知识得到,没有定量的理论依据.归一化植被指数是陆地遥感中用以表征地表植物覆盖程度和健…     

近岸海水质量量化方法及分类评价

近岸海域是陆地、海洋和大气之间各种过程相互作用最活跃的界面[1],沿海100km以内区域聚积着约60%的世界人口[2],其环境质量强烈受人类活动影响.对近岸海域进行水质环境管理,应依据海水物理、化学和生物学性质对海水进行分类评价[3,4].