典型网状河网区域土地利用和景观格局对地表季节水质的影响——以江苏省溧阳市为例

地表水质受区域景观组成及其空间配置的影响较大,了解景观特征与水质之间的关系可以极大地提高潜在污染的可预测性和污染物输出的评估能力. 以典型网状河网区域(江苏省溧阳市)为例,研究了土地利用和景观格局特征对地表季节水质的影响. 本研究基于2017年每单月从12个监测断面收集的21个水质指标,首先分析了多尺度缓冲区(500、1000、1500、2000、2500和3000 m)内土地利用和景观格局特征,然后通过主成分分析选取12个指标作为主要水质因子,采用冗余分析确定景观因子对水质指标的最佳影响尺度,最后采用偏最小二乘回归(PLSR)探究了最佳影响尺度下景观因子对季节水质的影响. 结果表明,2500 m缓冲区是该区域景观因子对水质指标的最佳影响尺度,旱季大多数水质指标PLSR模型的显著性和预测能力比雨季强. 雨季大多数水质指标都受园地、林草地、散布与并列指数(IJI)和香农均匀度指数(SHEI)的重要影响,并且这些景观因子与除pH和溶解氧浓度之外的其他水质指标均呈负相关. 在旱季,溶解氧、石油类、化学需氧量、总氮和总磷浓度受土地利用的影响最大; 另外,IJI是电导率、硫酸盐和亚硝酸盐氮浓度的最重要影响因子,而SHEI对硫化物和总悬浮物浓度的影响最大. 此外,景观指数对雨季水质的影响更大. 本研究结果揭示了网状河网区域土地利用/景观格局与季节性水质的关系,为区域水环境管理和景观格局优化提供了科学依据.     

Spectral behavior of Persian oak under compound stress of water deficit and dust storm

Persian oak (Quercus Brantii Lindl.) which is the most widely distributed tree in the Zagros Mountain forests is affected by western dust storms, mostly originating in Iraq, and harsh water stress as well. The objective of this research is to analyze the spectral behavior of Persian oak under water and dust stress scenarios, aiming to pave the way for modeling the stresses of drought and dust storms on oak trees using remote sensing images. Experiments were carried out on 54 two-year old oak tree seedlings, using a portable wind tunnel in greenhouse conditions. Water stress was induced on seedlings by means of changes in irrigation practices, i.e. well-watered (100 % field capacity), medium water deficit condition (40 % field capacity), and severe water deficit condition (20 % field capacity) treatments. Dust stress is also investigated by using three different dust particle concentrations, i.e. 350, 750 and 1500 (μg/m³). The spectrometry experiments were carried out at leaf and canopy levels in dark room by Fieldspec-3-ASD spectrometer. Spectral analysis was conducted using four procedures: (i) narrow-band spectral indices analysis, (ii) geometric indicators extraction from absorption features, (iii) Partial Least Squares Regression (PLSR), and SVM classifier. Results show that water stress could be modeled much better using PLSR statistic (R² = 0.87, RMSE = 0.12), narrow-band indices analysis (R²_cv = 0.75, RMSE_cv = 0.17), and continuum removal (R² = 0.71, RMSE = 0.20), respectively. For dust stress, PLSR (R² = 0.83, RMSE = 0.14) and narrow-band indices (R² _cv = 0.7, RMSE _cv = 0.30) showed the best results, respectively. SVM could successfully separate stressed and not-stressed samples and also the stress types at both leaf and canopy levels, but it could not distinguish the different levels of stresses.     

Quantifying engineering parameters of expansive soils from their reflectance spectra

Occurrence of expansive soils in construction sites has serious implications on planning, design, construction, maintenance, and overall performance especially of lightweight engineering infrastructures. Such soils are particularly susceptible to large volume changes in response to moisture content fluctuations following seasonal climatic variations. This can lead to deformation of structures built up on them. For this study, soil samples were collected from the eastern part of Addis Ababa. Specific expansive soil engineering parameters namely; Atterberg limits (LL, PL and PI), free swell and cation exchange capacity were measured in a soil mechanics laboratory. Reflectance spectra of each soil sample were acquired in a remote sensing laboratory using ASD fieldspec full range spectrometer. A multivariate calibration method, partial least squares regression (PLSR) analysis, through simple wavelength approach, was used to construct empirical prediction models for estimating engineering parameters of expansive soils from their respective reflectance spectra. Correlation coefficients of 0.90, 0.87, 0.71, 0.81 and 0.71 for CEC, LL, PL, PI and FS respectively were obtained. The correlation coefficients showed that large portion of variations in engineering parameters of expansive soils could be accounted for by spectral parameters. Apart from these high correlation coefficients, small root mean square errors of calibration (RMSEC) and prediction (RMSEP), standard error of calibration (SEC) and prediction (SEP) and minimum bias were obtained. The results indicate potential of spectroscopy in deriving engineering parameters of expansive soils from their respective reflectance spectra and hence, its potential applicability in supporting geotechnical investigations of such soils.     

基于包络线法的滨海滩涂PLSR盐分模型研究

以辽河三角洲滨海湿地滩涂为研究区域,首先利用8种常用光谱变换方法和包络线法进行光谱特征分析,发现光谱数据经包络线法去包络后与盐分含量的相关性显著提高,并确定其对盐分敏感的最佳波段,利用PLSR建立敏感波段的土壤盐分反演模型。研究表明,模型由于保留了对土壤含盐量贡献大的波段,反演精度较高,同时减少了计算量。     

偏最小二乘降维法在粤西层控型铅锌矿床发育区水系沉积物地球化学测量数据分析中的应用

在当今信息时代,以地球科学数据为基础,数据挖掘技术及方法逐渐成为地球科学领域研究的重要方向。本文以粤西丰村–园垌层控型铅锌矿床发育区水系沉积物微量元素测量数据为研究对象,首先对原始数据进行清洗、变换和规约算法等数据处理,其次应用主成分分析法、聚类分析法及偏最小二乘降维(PLSR)算法挖掘地球化学元素组合异常信息。分析结果表明,基于等距对数比变换的PLSR建模,可以在不损失数据完整性的前提下,充分挖掘元素内部的相关性,并提取组合元素异常。PLSR法与主成分分析和聚类分析的结果对比分析显示,PLSR法提取出的元素组合与研究区铅锌矿床地球化学特征接近,可以充分挖掘地球化学数据的潜在信息,其分析结果与研究区地质条件耦合度高,可以为粤西地区层控型铅锌矿床下一步找矿提供可靠的数据支撑。     

基于高光谱的土壤养分含量反演模型研究

为实现土壤养分（有机质SOM、全氮TN、全磷TP、全硫TS）含量的快速测定,以建三江创业农场为例,对土壤原始反射率进行了一阶微分（FD）、倒数对数（RL）、倒数一阶微分（FDR）、多元散射校正（MSC）和连续统去除（CR）变换,分析6种光谱变量与土壤养分的相关性,将在α=0.01水平上显著相关的波段作为特征波段,运用多元逐步回归（SMLR）、偏最小二乘回归（PLSR）和BP神经网络（BPNN）三种分析方法分别建立有机质、全氮、全磷和全硫的高光谱预测模型,并利用决定系数（R²）、均方根误差（RMSE）和相对分析误差（RPD）对预测模型进行评价.结果显示,PLSR和BPNN建立的土壤养分含量预测模型均优于SMLR,能极好地预测有机质和全氮含量,同时具有粗略估算全硫含量的能力.三种方法中仅有CR-BPNN能对全磷含量进行粗略估算.对有机质、全氮、全磷和全硫预测效果最佳的模型及其验证集决定系数分别为：MSC-PLSR （0.86）、MSC-PLSR （0.75）、CR-BPNN （0.56）、FDR-BPNN （0.67）.     

Solving multicollinearity in dam regression model using TSVD

Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process,we built a regularized regression model using Truncated Singular Value Decomposition(TSVD).An earth-rock dam in China is presented and discussed as an example.The analysis consists of three steps:multicollinearity detection,regularization pa-rameter selection,and crack opening modeling and forecasting.Generalized Cross-Validation(GCV) function and L-curve criterion are both adopted in the regularization parameter selection.Partial Least-Squares Regression(PLSR) and stepwise regression are also included for comparison.The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models.However,no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem.Both fitting accuracy and coefficients’ reasonability should be considered when evaluating the model reliability.     

马达加斯加索菲亚省BINARA地区铬铁矿遥感找矿预测

马达加斯加北部地区地广人稀、交通不便、植被稀疏、矿产资源丰富,利用遥感技术提取矿化蚀变信息具有独特的优势和较好的效果。为了对BINARA地区铬铁矿进行遥感找矿预测,文章首先利用Landsat 8遥感数据2、4、5、6波段进行主成分分析提取研究区内基性-超基性岩信息;然后基于实测高光谱数据,采用偏最小二乘回归法建立Fe含量定量反演模型,并利用该模型对Hyperion高光谱遥感数据进行计算,提取研究区内Fe含量异常信息;最后根据综合遥感异常信息以及野外调查验证结果,确定研究区内的Fe含量为一级异常的区域作为预测区。