Response to the paper by H. Zia,B. Lecampion “Explicit versus implicit front advancing schemes for the simulation of hydraulic fracture growth” (Int. J. Numer. Anal. Methods Geomech., 2019, 43 (6), 1300–1315)

The response presents three comments, which (i) explain theoretical background for the success of the explicit front advancing scheme by Zia and Lecampion (comment 1), (ii) suggest possible extensions (comment 2), and (iii) pay attention on expected limitations of the scheme to be checked by the authors in numerical experiments (comment 3). The response does not intend to present novel results: it is entirely based on known results published in the references cited.     

Integration schemes with substepping algorithms for creep analysis in geomaterials

This paper describes a robust and efficient methodology for predicting displacements, deformations, and stresses in geomaterials that are susceptible to creep. The methodology is based on two integration schemes, which consider substepping algorithms. The first scheme is used for integrating space-time relations in a global sense, whereas the second scheme is used for integrating stress-stain relations in a local sense. Different from previous studies, both integration schemes are easy to implement and general in the sense that they can be applied to any type of creep law. Through an in-house finite element simulator, several numerical tests are performed. They include triaxial and wellbore closure analyses considering soft soils and salt rocks. The results show that the combination of both schemes leads to stable and accurate solutions with reduced computational time.     

波段选择协同表达高光谱异常探测算法

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。     

Evaporation modelling by heuristic regression approaches using only temperature data

Accurate estimation of pan evaporation (E_pan) is very important in water resources management, irrigation scheduling and water budget of lakes. This study investigates the accuracy of two heuristic regression approaches, multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in estimating pan evaporation using only temperature data as input. Monthly minimum temperature, maximum temperature and E_pan data from three Turkish stations were used, with month number (periodicity information) added as input to see its effect on estimation accuracy. The models were compared with the calibrated Hargreaves-Samani (CHS), Stephens-Stewart (SS) and multiple linear regression methods. Three different train-test splitting strategies (50%–50%, 60%–40% and 75%–25%) were employed for better evaluation of the applied methods. The results show that the MARS method generally estimated monthly E_pan with higher accuracy compared to the M5Tree, CHS and SS methods. When extraterrestrial radiation, calculated from Julian date and latitude information, was used as input to the SS instead of solar radiation, satisfactory estimates were obtained. A positive effect on model accuracy was observed when involving periodicity information in inputs and increasing training data length.     

Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.     

基于CMIP5模式鄱阳湖流域未来参考作物蒸散量预估

预测未来气候情境下鄱阳湖流域参考作物蒸散量(Reference crop Evapotranspiration,ET0)的时空分布可为流域水资源的优化管理,为科学应对气候变化对农业生产的影响提供基础数据支撑.利用鄱阳湖流域14个气象站点1961-2014年逐日气象数据,采用Penman-Monteith公式计算出历史ET0;基于同期美国环境中心(NCEP)再分析数据及2006-2100年CMIP5中CNRM-CM5模式在RCP4. 5和RCP8. 5情景下的预测数据,经统计降尺度模型(statistical downscaling model,SDSM)模拟和偏差校正,预测流域未来ET0;通过Mann-Kendall检验、普通克里金插值和空间自相关法分析了流域1961-2100年ET0的时空演变特征.结果表明:NCEP再分析资料与流域ET0建立的逐步回归降尺度模型模拟效果较好,CNRMCM5模式降尺度模拟结果经偏差校正后,精度明显提高,适宜流域未来ET0的预估.鄱阳湖流域在基准期1961-2010年ET0整体上呈减小趋势,空间分布上呈南北高、中间低的特点,表现出明显的空间差异性.RCP4.5、RCP8.5情景下未来3个时期鄱阳湖流域ET0较基准期均呈不同程度的增加趋势,其空间分布整体表现为东高西低、局地略有突出;无论是在基准期或是未来情景下的3个时期,ET0均具有较强的空间自相关性.在RCP8.5情景下,鄱阳湖1961-2100年干旱指数呈现出较为明显的上升趋势,流域的干旱状况随时间加剧,2011-2100年间流域绝大部分地区由湿润区转为半湿润区,干旱指数自南向北递减,赣江流域将是鄱阳湖流域未来干旱风险的重点防范区.     

基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合

针对空间分辨率比率较大尺度差异下的高分五号(GF-5)与高分一号(GF-1)卫星影像的空—谱融合问题,提出多传感器影像融合策略:一方面,通过现有空—谱融合方法的分步融合得到融合影像;另一方面,在分步融合理论基础上,推导得出一体化融合基础框架,并进一步提出基于多分辨率分析的多传感器一体化融合方法,缓解现有方法因空间分辨率比率过大导致影像空、谱互补信息难以有效集成的问题。其中,提出的一体化融合方法基于调制传递函数MTF (Modulation Transfer Function)滤波对多传感器影像空间(高频)和光谱(低频)分量进行分解提取,并充分考虑多传感器高空间分辨率影像与高光谱分辨率影像之间的关系,以及高光谱分辨率影像波段间关系,设计合理的融合权重,最终可得到具有最高空间分辨率和最高光谱分辨率的融合影像。通过GF-1全色影像、GF-1多光谱影像、GF-5高光谱影像数据对提出方法进行实验验证,结果表明:本文方法可有效集成多传感器影像间的空、谱互补信息,得到较优融合结果。     

Reply to comments on “Explicit versus implicit front advancing schemes for the simulation of hydraulic fracture growth” (Int. J. Numer. Anal. Methods Geomech., 2019, 43 (6), 1300–1315)” by Prof. Linkov

We reply to Prof. Linkov comments on our article entitled “Explicit versus implicit front advancing schemes for the simulation of hydraulic fracture growth” (Int. J. Numer. Anal. Methods Geomech., 2019, 43 (6), 1300–1315). We present additional results indicating that both the implicit and explicit front advancement schemes are robust even in the case of a large stress contrast.     

高分五号全谱段光谱成像仪地表温度与发射率反演

地表温度在全球能量平衡和气候变化研究中具有重要意义。中国新一代高分辨率卫星高分五号卫星(GF-5)搭载的全谱段成像光谱仪有4个40 m空间分辨率的热红外波段,可以提供高空间分辨率的地表温度信息。本文提出了适用于全谱段成像光谱仪的温度与发射率分离TES(Temperature and Emissivity Separation)算法同时反演地表温度和发射率,为了提高大气校正精度,算法加入了水汽缩放WVS(Water Vapor Scaling)大气校正方法。首先利用Seebor V5.0全球大气廓线库构建模拟数据对算法精度进行了评价;然后利用张掖地区11景ASTER影像作为替代数据和同步的地面实测数据对算法精度进行了验证。模拟数据结果表明加入WVS方法后TES算法反演地表温度的RMSE由2.59 K降低到1.54 K,4个波段地表发射率的RMSE分别从0.122、0.12、0.102和0.037降低到0.042、0.04、0.028和0.026;地表验证结果表明本文算法反演的地表温度与站点实测值具有更好的一致性,平均Bias由1.08 K降低到0.47 K,RMSE由2.17 K降低到1.7 K;反演的各波段地表发射率与地面实测结果误差均小于1%。因此,本文提出的温度与发射率分离算法具有较高精度,可以利用GF-5数据获取高精度高空间分辨率的地表温度和发射率数据,服务于其他相关研究。     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.