Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches

The geographically weighted regression (GWR) has been widely applied to many practical fields for exploring spatial non-stationarity of a regression relationship. However, this method is inherently not robust to outliers due to the least squares criterion in the process of estimation. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression relationship. Using the least absolute deviation criterion, we propose two robust scenarios of the GWR approaches to handle outliers. One is based on the basic GWR and the other is based on the local linear GWR (LGWR). The proposed methods can automatically reduce the impact of outliers on the estimates of the regression coefficients and can be easily implemented with modern computer software for dealing with the linear programming problems. We then conduct simulations to assess the performance of the proposed methods and the results demonstrate that the methods are quite robust to outliers and can retrieve the underlying coefficient surfaces satisfactorily even though the data are seriously contaminated or contain severe outliers.     

Spectral indicators of forage quality in West Africa’s tropical savannas

Forage is among the essential ecosystem services provided by tropical savannas. Expected changes in climate and land use may cause a strong decline in herbaceous forage provision and thus make it advisable to monitor its dynamics. Spectroscopy offers promising tools for fast and non-destructive estimations of forage variables, yet suffers from unfavourable measurement conditions during the tropical growing period such as frequent cloud cover and high humidity. This study aims to test whether spatio-temporal information on the quality (metabolisable energy content, ME) and quantity (green biomass, BM) of West African forage resources can be correlated to in situ measured reflectance data. We could establish robust and independent models via partial least squares regression, when spectra were preprocessed using second derivative transformation (ME: max. adjusted R² in validation (_adjR²_VAL) = 0.83, min. normalised root mean square error (nRMSE) = 7.3%; BM: max. _adjR²_VAL = 0.75, min. nRMSE = 9.4%). Reflectance data with a reduced spectral range (350–1075 nm) still rendered satisfactory accuracy.Our results confirm that a strong correlation between forage characteristics and reflectance of tropical savanna vegetation can be found. For the first time in field spectroscopy studies, forage quality is modelled as ME content based on 24-h in vitro gas production in the Hohenheim gas test system and crude protein concentration of BM. Established spectral models could help to monitor forage provision in space and time, which is of great importance for an adaptive livestock management.     

Total least square method applied to rating curves

The ordinary least square method (OLS) has been the most frequently used least square method in hydrological data analysis. Its computational algorithm is simple, and the error analysis is also simple and clear. However, the primary assumption of the OLS method, which states that the dependent variable is the only error‐contaminated variable and all other variables are error free, is often violated in hydrological data analyses. Recently, a matrix algorithm using the singular value decomposition for the total least square (TLS) method has been developed and used in data analyses as errors‐in‐variables model where several variables could be contaminated with observational errors. In our study, the algorithm of the TLS is introduced in the evaluation of rating curves between the flow discharge and the water level. Then, the TLS algorithm is applied to real data set for rating curves. The evaluated TLS rating curves are compared with the OLS rating curves, and the result indicates that the TLS rating curve and the OLS rating curve are in good agreement. The TLS and OLS rating curves are discussed about their algorithms and error terms in the study. Copyright © 2013 John Wiley & Sons, Ltd.     

Shallow geology characterization using Rayleigh and Love wave dispersion curves derived from seismic noise array measurements

The local geology and shallow S-wave velocity structure of a site are recognized to be key factors for the increase in the damaging potential of seismic waves. Indeed, seismic amplitudes may be amplified in frequency ranges unfavorable for building stock by the presence of soft sedimentary covers over lying hard bedrock. Hence, microzonation activities, which aim at assessing the site response as accurately as possible, have become a fundamental task for the seismic risk reduction of urbanized areas. Methods based on the measurement of seismic noise, which typically are fast, non-invasive, and low cost, have become a very attractive option in microzonation studies.Using observations derived from seismic noise recordings collected by two-dimensional arrays of seismic stations, we present a novel joint inversion scheme for surface wave curves. In particular, the Love wave, the Rayleigh wave dispersion and the HVSR curves are innovatively combined in a joint inversion procedure carried out following a global search approach (i.e., the Genetic Algorithm).The procedure is tested using a data set of seismic noise recordings collected at the Bevagna (Italy) test-site. The results of the novel inversion scheme are compared with the inversion scheme proposed by Parolai et al. (2005), where only Rayleigh wave dispersion and HVSR curves are used, and with a cross-hole survey.     

山东地区b值的统计分析

利用山东省内及其邻近海域(34°～39°N,114°～125°E),1996年1月至2009年5月的地震资料,通过最小二乘法及极大似然法对山东地区近年来的b值进行了求解及分析,给出了本区b值的计算结果,讨论了两种方法得到结果的差异,并对近年来6值时序变化的一些特点及其所反映的问题进行了讨论.     

Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models

Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R² between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R² = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R² = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.     

从激光点云中提取古建筑线性和圆形特征的比较

对古建筑激光扫描点云进行分割、识别,并利用Hough变换和最小二乘法从点云中提取直线和圆,取得了较满意的结果。对两种算法的提取效果进行了比较。     

基于抗差最小二乘估计的载体速度计算方法

针对相位率值的特点,引入抗差最小二乘估计进行速度计算,根据IGGⅢ方案选取合适的等价权因子来降低存在小周跳影响的观测值对结果的贡献,利用航空飞行实验的数据进行了计算分析,结果表明经过抗差后的速度仍可保持在mm/s的量级。     

CASIO fx—4850P计算器高程控制网按间接平差计算程序

介绍了用CASIO fx—4850P计算器按间接法平差高程控制网,利用法方程中系数矩阵对称正定的特点,直接生成法方程式的系数矩阵。由于CASIO fx—4850P计算器程序容量28500字节,考虑到野外工作受环境制约及其工作的流动性,该计算器能够容纳一般的常用测量计算程序,携带和计算都非常方便。程序经过实例验算,精度很...     

基于VB的水准网平差软件的设计与实现

根据最小二乘平差原理,以Visual Basic 6.0为开发平台,设计并实现了水准网平差软件.该软件可完成水准网的平差计算和精度评定计算.