On the hydro-spectrum of deep wells and the response ability of well-aquifer system

onclusions 1) A set of period components corresponding well to those of Earth tides and barometric pressure can be separated from the hydro-spectra of deep well. The well-aquifer system responds to these components in some degree, which shows that groundwater regime is influenced by Earth tides and barometric pressure. However, in different frequency bands a well-aquifer system possesses different transfer ability for various inputs. 2) The transfer function quantitatively describes the response ability of well-aquifer system to Earth tides and barometric pressure. It is a characteristic parameter that the system itself possesses. According to the transfer function value of well-aquifer system to a certain input factor, we can objectively understand the influence of the input on the groundwater regime in a deep well. This is of important significance for revealing the formation of groundwater regime in a deep well and for evaluating the seismic supervising well. The work was supported by National Natural Science Foundation of China (No. 49372149).     

上海地区地磁转换函数变化及其与近震的关系

计算分析了上海地区佘山台１９７７年１月－１９８６年１２月和崇明台１９８３年１月－１９８９年９月的地磁周期Ｔ＝９－２００ｍｉｎ的复转换函数Ａ的模│Ａ│，总方差σｚ和帕金森林矢量或韦氏矢量，并将其在上海地区９０－３００ＫＭ范围内４次５－６级近震发生时的烨变化进行了研究。结果表明周期为１５、２０和３５ｍｉｎ的│Ａ│和σｚ值在震前明显异常，帕金森矢量或韦氏矢量在震前指向震中，震后回到正常位置，在此基础上，     

森林间隙率模型及模型验证

植被冠层间隙率的研究至今已有３０多年的历史。间隙率模型的研究一直沿着辐射传输方向及几何光学方向发展，本文试图将两者结合起来得到一个更为一般的间隙率模型。要验证模型，实验数据的获取是极其重要的，为了获得准确的数据，我们对观测进行了精心的设计，并在测量方法上有所创新，如“摄影法”，“平行光孔孔法”等都是经济而方便的方法。     

一个检验太阳光度计Langley定标结果可靠性的方法

准确反演气溶胶光学厚度（AOD）在气溶胶气候效应和环境效应研究中至关重要，仪器定标是目前AOD反演过程中最大的不确定性来源。Langley法作为应用最广泛的光度计定标方法，其对天气条件和大气洁净度要求苛刻，这在大气污染较重的地区难以实现，并可能导致AOD反演误差偏大。为了降低由不恰当的Langley定标所引起的AOD反演误差，本文提出一个利用辐射传输模式结合地面太阳直接辐射观测数据检验Langley定标结果合理性的方法，并利用西安2013年6月至2015年12月多滤波旋转遮光带辐射计（MFRSR）和直接辐射表（NIP）观测资料，探讨了方法的可行性。结果表明，该方法能够将Langley定标结果限定在较小的波动范围内，有效降低由定标不确定性引起的AOD反演误差，有助于提高单站点AOD观测精度。     

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants

ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO_x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO₂, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O₃ and NO_x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.     

Multi-source spatial data-based invasion risk modeling of Striga (Striga asiatica) in Zimbabwe

ABSTRACT

Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12^th and 28^th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment     

Increasing the detail of European land use/cover data by combining heterogeneous data sets

ABSTRACT

Data on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their interactions. In Europe, CORINE Land Cover has been the only data set covering the entire continent consistently, but with rather limited spatial detail. Other data sets have provided much better detail, but either have covered only a fraction of Europe (e.g. Urban Atlas) or have been thematically restricted (e.g. Copernicus High Resolution Layers). In this study, we processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries. By doing so, we increased the spatial detail (from 25 to one hectare) and the thematic detail (by seven additional LULC classes) compared to the CORINE Land Cover. Importantly, we decomposed the class ‘Industrial and commercial units’ into ‘Production facilities’, ‘Commercial/service facilities’ and ‘Public facilities’ using machine learning to exploit a large database of points of interest. The overall accuracy of this thematic breakdown was 74%, despite the confusion between the production and commercial land uses, often attributable to noisy training data or mixed land uses. Lessons learnt from this exercise are discussed, and further research direction is proposed.     

基于Sentinel-1A数据的多种机器学习算法识别冰山的比较

冰山识别对于海洋环境监测和船只安全运行等具有重要的意义,是北极航道开通和北极开发过程中的重要内容。采用合成孔径雷达(SAR)影像进行冰山识别具有独特的优势,多种机器学习算法均可用于SAR影像的冰山识别中。为了最大限度地发挥机器学习算法的性能,有必要对不同机器学习算法及其搭配使用的特征与特征标准化方法进行评估,从而进行最优冰山识别方法的选择。因此,本文基于Sentinel-1A SAR影像,采用多种机器学习方法、多种特征组合及多种特征标准化方法进行冰山识别,并比较各流程方法的识别性能差异。采用的机器学习算法包括贝叶斯分类器(Bayes)、反向神经网络(BPNN)、线性判别分析(LDA)、随机森林(RF)以及支持向量机(SVM);特征标准化方法包括Min-max标准化、Z-score标准化及log函数标准化;数据集是含有12个SAR影像特征的969个冰山与非冰山样本,样本主要位于格陵兰岛东海岸。分类效果采用接收者操作特性(ROC)曲线下的面积(AUC)进行衡量。结果显示,最佳搭配下的RF的AUC值最高,达到了0.945,比最差的Bayes高出0.09。从识别率上来看,RF在冰山查全率为80%的情况下非冰山查全率达到92.6%,效果最好,比第2位的BPNN高出1.4%,比最差的Bayes高出2.6%;BPNN在冰山查全率为90%的情况下非冰山查全率达到87.4%,比第2位的RF高出0.8%,比最差的Bayes高出2.7%。上述结果表明,对冰山识别而言,选择最优的机器学习算法和最佳的特征与特征标准化方法都是十分重要的。     

应用随机辐射传输模型反演云南松林分郁闭度

随机辐射传输模型可用于模拟水平分布不均一森林的辐射传输过程。本文以云南松林分为研究对象，提出一种应用随机辐射传输模型的郁闭度反演方法。该方法以随机辐射传输模型中参数与林分郁闭度的定量关系为基础，提出了针对云南松的冠型等效模型，构建了郁闭度和卫星反射率（GF-1和Landsat 8卫星影像）的查找表，并实施了反演。基于野外实测的30个样地进行了郁闭度数据验证，并和基于NDVI回归模型的反演方法进行对比。结果表明，反演结果能够较准确反映云南松林分郁闭状况（R²=0.8345，RMSE=0.0688），通过冠型修正能够降低反演误差，冠型等效模型是合理的。反演方法机理清晰且适用范围广，研究成果可为大面积森林郁闭度反演提供模型和方法支持。     

NOAA－AVHRR图像大气影响校正方法研究及软件研制第一部分 原理和模型

大气影响是遥感定量化应用的主要障碍之一。本文首先分析了大气对遥感图像产生的各种影响并对影响程度进行了一般的阐述，然后在原理和方法上对典型的大气影响校正算法进行了总结。通过分析我们认为，为了校正遥感图像所受到的大气影响，定量地获取当时当地的大气条件是完成这一工作的必要条件，而且是非常重要的条件之一。为此，我们提出了利用ＮＯＡＡ星载垂直探测器资料即ＴＯＶＳ资料，结合使用气溶胶模型，通过大气辐射传输模型，建立地面辐射量与卫星测量值之间的关系，并完成了对ＮＯＡＡ－ＡＶＨＲＲ图像的大气影响校正。在此基础上对我们的模型和算法进行了详细论述。这一模型是一套适用于发展业务软件的校正模型，软件发展完善后将为ＮＯＡＡ－ＡＶＨＲＲ数据的定量化应用打下基础。