Validation of Landslide Susceptibility Maps; Examples and Applications from a Case Study in Northern Spain

A procedure for validating landslide susceptibility maps wasapplied in a study area in northern Spain and the results obtained compared. Validationwas used to carry out sensitivity analysis for individual variables and combinationsof variables. The validity of different map-making methods was tested, as well as theutility of different types of Favourability Functions. The results obtained show thatvalidation is essential to determine the predictive value of susceptibility maps. Italso helps to better select the most suitable function and significant variables, thus improving the efficiency of the mapping process. Validation based on a temporal strategy makes it possible to derive hazard maps from susceptibility maps.     

FY2海面温度产品质量检验方法与误差分析

主要介绍了风云二号静止气象卫星海面温度产品的质量检验方法及检验流程,给出了FY2E业务海面温度产品和FY2F海面温度算法的质量检验结果,并从误差统计、反演算法、系统设计等方面分析了目前FY2海面温度产品与国外卫星海面温度产品差异大的原因。现场海面温度质量检验和分析场海面温度交叉检验各有优缺点。现场海面温度质量检验能够对FY2 SST产品给于客观的评价。国外分析场海面温度具有时效性好、全球覆盖且质量均一的优点,分析场海面温度交叉检验能满足FY2 SST质量检验的时效性,是对FY2 SST的相对检验。通过静止卫星海面温度产品质量检验信息可以为产品研发人员和相关用户提供参考。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

基于客观分析的多源卫星叶绿素a浓度产品融合方法研究

本文基于Aqua/MODIS、Terra/MODIS和Envisat/MERIS多源卫星叶绿素a浓度产品,研究了客观分析融合方法,制作了西北太平洋海域(0°~50°N,100°~150°E)叶绿素a浓度融合产品,并从有效数据空间覆盖率和产品精度两个方面对融合方法进行了评价。与单传感器以及欧洲太空局发布的GSM模型业务化融合产品相比,客观分析融合产品空间覆盖率明显提高;与收集的2002-2012年间叶绿素a浓度实测数据比较,GSM模型业务化融合产品的匹配数据点为578个,偏差为-0.20 mg/m3,均方根误差为0.37 mg/m3,客观分析法融合产品的匹配数据点为1432个,偏差为-0.21 mg/m3,均方根误差为0.36 mg/m3。结果表明:本文研究的客观分析融合方法在保证融合产品精度的同时可显著提高产品的空间覆盖率,在海洋水色融合应用前景广阔。     

Global Statistical Assessment and Cross-Calibration with Jason-2 for Reprocessed HY-2 A Altimeter Data

HY-2 A (Haiyang-2 A) satellite was launched on August 16, 2011 and radar altimeter is one of its main payloads. We reprocessed two years of HY-2 A altimeter sensor geophysical dataset records (SGDR) data. This paper presents the main results in terms of reprocessed HY-2 A altimeter data quality: verification of data availability and validity, monitoring several relevant altimeter parameters, and assessment of the HY-2 A altimeter system performances. A cross-calibration analysis of reprocessed HY-2 A altimeter data with Jason-2 was conducted. The reprocessed HY-2 A altimeter data show good quality and have a low level of noise with respect to Jason-2. The same geophysical correction methods were used to calculate the sea surface height (SSH) for the two missions. The mean standard deviations of the crossover differences for HY-2 A and Jason-2 are 5.24 cm and 5.34 cm, respectively. The mean standard deviation of the crossover differences between HY-2 A and Jason-2 is 5.37 cm. These show that HY-2 A can provide SSH measurements at almost the same level of accuracy as Jason-2. The relative SSH bias between HY-2 A and Jason-2 due to the Ultra Stable Oscillator (USO) drift is obviously observed, and it can affect the calculation of mean sea level and should be further studied and corrected.     

近岸波生流运动三维数值模拟及验证

开发建立了近岸波生流运动三维数值计算模式。模式中,引入了三维时均剩余动量、破波表面水滚、波浪水平与垂向紊动作为主要驱动力,同时考虑了波流共同作用的底部剪切力。推导了可综合反映底坡、能量传递率和密度影响的水滚能量传输方程;将Larson-Kraus的二维波浪水平紊动系数表达式拓展至三维。采用大量实测数据和文献资料测试验证了所建模式,表明所建模式可有效模拟波浪增减水、底部离岸流、沿岸流、裂流、堤后环流等不同维度的波生流现象。此外,研究也表明破波水滚效应可解释波生流峰值向岸推移的物理现象,从而在模拟中不能忽略;破波带内沿岸流速垂向较为均匀的现象与波浪附加垂向紊动有关。     

黔北遵义团溪铝土矿勘探网密度探讨

以黔北遵义团溪铝土矿后槽矿区一矿块为例,用50m×50m工程间距进行施工成果与100m×50m和100m×100m工程间距施工成果,对其矿体厚度、矿体形态、主要化学成分含量及储量变化进行分析对比,以探寻此类矿床应如何选择工程间距,才能更合理准确地对矿体进行圈定控制,以满足矿山开采设计的要求。通过资料分析对比认为,对黔北喀斯特型沉积铝土矿进行勘查而言,以≤50m×50m工程间距控制矿体并求取储量,效果较为理想。     

基于AMSR-E的北疆地区积雪深度反演

利用北疆地区2007/2008-2009/2010年度积雪季(12月至次年2月)的AMSR-E降轨19 GHz与37 GHz波段的水平极化亮温数据, 结合北疆地区45个气象台站的实测雪深数据, 建立了北疆地区基于AMSR-E亮度温度数据的雪深反演模型, 并对模型的精度进行评价. 结果显示: 雪深在3~10 cm时, 模型反演的雪深值负向平均误差为-5.1 cm, RMSE值为6.1 cm; 雪深在11~30 cm时, 模型反演雪深值的平均误差仅为2.6 cm, RMSE、 正向平均误差、 绝对平均误差均较小; 雪深大于30 cm时, 模型反演的各项误差较大. 用合成方法反演北疆地区2006/2007-2010/2011年度5个积雪季的平均雪深分布和最大雪深分布, 结果显示北疆地区积雪主要分布于北部阿尔泰山和南部天山一带, 其中阿勒泰地区所占比重最大, 中部的准噶尔盆地腹地、 克拉玛依地区雪层较浅.     

Is Prediction of Future Landslides Possible with a GIS?

This contribution explores a strategy for landslide hazard zonation inwhich layers of spatial data are used to represent typical settings inwhich given dynamic types of landslides are likely to occur. Theconcepts of assessment and prediction are defined to focus on therepresentation of future hazardous events and in particular on themyths that often provide obstacles in the application of quantitativemethods. The prediction rate curves for different applications describethe support provided by the different data layers in experiments inwhich the typical setting of hazardous events is approximated bystatistically integrating the spatial information.