基于半变异函数的多极化SAR图像地表淹没程度分析

洪涝灾害范围的提取能够为灾害的动态监测、评估提取重要的数据。由于半变异函数能够反映图像数据的随机性和结构性,可以很好的体现地物的空间自相关性。而地物在SAR图像上也表现出很好的空间自相关性和纹理特征,各种淹没程度的地物在图像上也表现出不同的空间自相关性,所以将特定窗口下特定步长的半变异函数应用到地表淹没程度分析,证明该方法的可行性。     

结构化地物的异质性分析及其计算方法

将具有明显空间结构的非连续性地物数据的结构分析研究,归纳为空间数据的结构化异质性分析问题。在此概念的基础上引用曲线论的方法分析了结构化异质性的变异函数的构成,得出变异距离受样本点间地物弧长及其空间复杂度共同影响的结论,由此推出变异距离函数的解析表达公式。之后给出了结构化异质性的实验变异函数的一种近似计算方法,该方法在样本合理分布的前提下一致收敛于真实变异值。最后介绍了结构化异质性模型在青藏铁路路基沉降数据的结构分析计算中的一个应用实例。     

Geostatistical assessment of rock zones for tunneling

Geostatistical assessments can be used for understanding the distribution of regionalized variables (ReVs) in any spatial study. Many researchers have used the theory of ReVs for different modeling purposes such as hydraulics, mining deposits, etc. In this paper, ReV technique is used for understanding the mechanical variation properties of rock zones under sampled and unsampled conditions in sewer tunnels for Istanbul. This study has been carried out for three different rock properties that are compressive strength, rock quality designation (RQD), Schmidt hammer hardness, and one rock cuttability property, net cutting rate. In order to define ReVs distribution, first semivariogram functions are determined and then kriging maps are generated for identifying the regional dependence. Furthermore, it is possible to make estimations for every point within the study area, and prepare maps for the interpretation of regional features of the ReV concerned. Net cutting rate is a cuttability property of rock, which can be regionalized and compared with the mechanical properties of rocks. This can be used for predicting the performance of excavator.     

地统计学在土壤空间变异研究中的应用

地统计学应用于土壤科学中,探索其空间分布特征及其变异规律,已为越来越多的学者所推崇。本文对地统计学基本原理及地统计学在土壤物理特性、土壤养分、土壤盐分、土壤重金属等土壤空间变异研究中的应用进行了论述,并针对存在的问题及发展趋势提出了一些看法。     

稻田土壤养分的空间变异性研究

本研究以嘉善县陶庄农场内一块5.1hm2的水稻田为研究区,区内不规则采样,共128个采样点。以土壤的pH、有机质、全磷、全氮、碱解氮、速效钾和速效磷含量为研究对象,研究其相应的统计规律,结果表明土壤各养分属性的变异系数在6.3%~35.61%之间,pH为5.3%;采用地统计的半方差函数分析,发现有机质,全氮,碱解氮和速效钾在一定范围内存在空间相关性;采用克立格方法进行最优内插,绘制了养分含量分布图,并对其空间变异进行了初步分析。     

Regularization of a semivariogram

The production of estimates using the technique of kriging, and the evaluation of the accuracy of these estimates depends completely on the production of a model for the semivariogram of the deposit. The process of choosing such a model can be complicated in practice by the bulk and geometry of the samples taken. Some aspects of this problem are discussed and some formulae and a FORTRAN IV subroutine are presented to ease the task of modeling.     

Spatial structure and landscape associations of SRTM error

This paper evaluates the spatial structure of Shuttle Radar Topography Mission (SRTM) error and its associations with globally available topographic and land cover variables across a wide range of landscapes. Two continental-scale SRTM elevation data samples were extracted, along with collocated National Elevation Dataset (NED) elevations, MODIS composite forest cover percentage, and global ecoregion major habitat type codes. The larger punctual sample contained nearly 247,000 sites on a regular grid across the conterminous United States, while the smaller areal sample consisted of 37,500 45″ × 45″ rectangular regions on a regular grid. Sub-pixel positional mismatch was accounted for by finding and using the best local fit between the 1 arc sec horizontal resolution NED product and the 3 arc sec (3″) horizontal resolution SRTM product. Slope and aspect were calculated for all samples. Using the larger point sample, we identified associations between SRTM error, defined as NED-SRTM 3″ differences, with these land cover and terrain derivative variables. Using the areal sample, we developed semivariograms of elevation error for tens of thousands of small regions across the United States, as well as for sets of these regions with common slope and landcover properties. This facilitated a more comprehensive evaluation of the spatial structure of SRTM error than has previously been done. The punctual sample RMSE was 8.6 m, conforming to previous estimates of SRTM error, but many errors in excess of 50 m were identified. Nearly 90% of these large errors were positive and correlated with high forest cover percentage. Overall, SRTM elevations consistently overestimated the surface. Forest cover and slope were positively correlated with positive bias. A strong association of aspect with SRTM error was noted, with positive error magnitudes peaking for aspects oriented to the northwest and negative error magnitudes peaking for slopes facing southeast. Error bias, standard deviation, and semivariograms differed substantially across ecoregion types. These variables were incorporated in a regression model to predict SRTM error: this model explained nearly 60% of the total error variation and has the potential to substantially improve the SRTM data product worldwide using globally available datasets.     

Practical kriging in three dimensions

Little has been published to date on the practical difficulties of ore reserve estimation in three-dimensional deposits. Some authors have suggested condensing the problem into two dimensions, but this is not always practicable or desirable. A suggestion and a FORTRAN IV Function Segment are provided which may alleviate some of these difficulties.     

Scales of spatial variability in Lake Michigan glider-based limnological observations

This study quantified spatial variability in temperature, colored dissolved organic matter, and chlorophyll-α fluorescence in the epilimnion and hypolimnion of southern Lake Michigan using observations collected by an autonomous underwater vehicle (glider) deployed multiple times per year during 2012–2019. We also include similar analyses of the deep chlorophyll maximum during the summer of those years. Ranges based on semivariance analysis and decorrelation distances generally were 5–15 km, with greater differences observed among seasons in the epilimnion and less unresolved error found for the temperature calculations. Our analysis provides information on submesoscale variation in limnological conditions that may be useful for several applications, including planning field sampling, observation networks, and data assimilation.     

Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images

This paper analyzes four geostatistical functions —semivariogram, semimadogram, covariogram, and correlogram—with the purpose of characterizing lung nodules as malignant or benign in computerized tomography images. The tests described in this paper were carried out using a sample of 30 nodules, 24 benign and 6 malignant. Stepwise discriminant analysis was used to determine which combination of measures were best able to discriminate between the benign and malignant nodules. Then, a linear discriminant analysis procedure was performed using the selected features to evaluate the ability of these features to predict the classification for each nodule. A leave-one-out procedure was used to provide a less biased estimate of the linear discriminator’s performance. All analyzed functions have value area under receiver operation characteristic (ROC) curve above 0.800, which means results with accuracy between good and excellent. The preliminary results of this approach are very promising in characterizing nodules using geostatistical functions.