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Compared to inland areas, meteorological stations in the island regions appear scarce and unevenly distributed, which leads to noteworthy uncertainty in detailed characterisation of various meteorological elements. For the Zhoushan Islands, located in Southeast China, there exist many islands and islets, and the local terrain is quite complex. Therefore, different interpolation strategies usually generate diverse gridded results, which largely influence the reliability and accuracy of operational climate monitoring and diagnosing. Under the background of climate change, the Zhoushan region is frequently invaded by cold waves in recent years, so how to scientifically choose an interpolation scheme to reasonably represent spatial distribution characteristics of temperature becomes an important issue in local climate operations. To solve this problem, based on the index of root mean square error (RMSE), the interpolation effect of Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and ANUSPLIN (ANU) are comparatively analysed for 8 cold wave processes influencing Zhoushan during 2014-2021. Two subdivided indices, i.e., temporal RMSE (TRMSE) and spatial RMSE (SRMSE) are further designed to evaluate the interpolation results on temporal and spatial dimensions respectively. Eleven stations are randomly selected from the total 53 meteorological observational stations to test the interpolation results of OK, IDW, and ANU for the minimum temperature, reduction of daily minimum temperature and daily-mean temperature in the 8 processes. It can be found that the bias in the ANU case is higher than that in the OK and IDW cases. To explain such a phenomenon, 3 interpolation experiments with dense surrounding stations, sparse surrounding stations, and specific distribution of examining stations (all the examining stations are not distributed in the main island of Zhoushan) are further designed. The results demonstrate that the performance of the ANU strategy is closely linked to the spread situation of peripheral stations. When the surrounding stations are concentrated, the interpolation bias of ANU is usually smaller than that of OK and IDW. However, if the surrounding stations appear sparse, the bias of ANU exhibits much larger. In the scenario of dense peripheral stations, regardless of the examining sites distributed over the main island or not, the ANU solution can always get the optimal interpolation results, which implies that the impact of topography on the performance of ANU in temperature interpolation is of less importance. Also, the influence of horizontal resolution for interpolation seems secondary. When the horizontal resolution for three interpolation schemes falls down to 1 km×1 km from 30 m×30 m, the change of RMSE is generally less than 0.1 ℃ for most circumstances, so the impact of interpolation resolution can be neglected.

Recently, spaceborne LiDAR data have been increasingly utilized for large-scale forest aboveground biomass (AGB) estimation. However, due to the discontinuity of laser spot sampling points, it is often necessary to integrate auxiliary data to estimate the continuous distribution of forest AGB, resulting in uncertainties in the estimation method. In this study, we focus on the Qilian Mountains National Park in northwestern China as a sample area. Three forest AGB estimation models (Ordinary Kriging (OK), Support Vector Regression (SVR), and Random Forest (RF)) were developed based on nonparametric algorithms by integrating ICESat/GLAS data, Landsat OLI imagery, and field inventory data. The accuracy of the estimation results generated by these three models was independently verified using forest inventory data. The results indicated that the root mean square error (RMSE) of the three models, from low to high, were SVR(19.053 t·hm-2), RF(21.074 t·hm-2), and OK(26.362 t·hm-2). The mean relative error (MRE), from low to high, were SVR (31.890%), RF (33.314%) and OK (55.398%). Moreover, except for the OK model, the total relative error (TRE) of the SVR and RF models falls within an acceptable range. Further verification of the accuracy of the spatial distribution of forest AGB was conducted for the SVR and RF model. It was found that the forest AGB spatial distribution generated by the SVR model was closer to the forest resource inventory data than that generated by the RF model. Therefore, we conclude that the SVR forest AGB estimation model demonstrates better quantitative accuracy and distribution accuracy. We also anticipate that these results can serve as a reference for forest AGB estimation based on spaceborne LiDAR data in future studies. [ABSTRACT FROM AUTHOR]

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In order to optimize the data analysis of radon detection, the ordinary Kriging interpolation pertaining to Geostatistics was introduced to avoid the interpolation distortion caused by the minimum curvature fitting method currently in effect. The radon detection data was collected from the Zhangling fire area of the Gushuyuan Coal Mine, the isolated outliers caused by human factors in the raw data were deleted through boxplot, and the concentration intervals that characterizes the spontaneous combustion area were obtained eventually. The statistical characteristics of the normal distribution for radon detection data were obtained through exploratory analysis and normal test and the data conversion was sorted out according to the statistical characteristics. Then, the variogram model for converted data was constructed to interpolate as the ordinary Kriging. The locations of suspected fire sources delineated by ordinary Kriging were contrasted with the locations by minimum curvature fitting method and then the accuracy was verified through drills. The results show that compared with the 4 suspected fire areas and 6 high temp fire sources delineated by the minimum curvature method, 2 areas and 4 sources were fixed with the ordinary Kriging. The borehole results for high temp fire source locations are consistent with the ordinary Kriging inferences, while excluding 2 additional locations delineated by the minimum curvature fitting, which proves that the ordinary Kriging can improve the accuracy of the judgements for fire source locations and can be a guide for the prevention of coal spontaneous combustion.

Based on Open CASCADE and Ordinary Kriging interpolation algorithm, a software development framework adding geostatistical interpolation algorithm to CAD geometry operation core for three-dimensional hydrogeological modeling is designed. Using three-dimensional graphics rendering of Open CASCADE, visual interaction, editing functions and geo-statistics interpolation function of Ordinary Kriging, we designed the hydrogeology modeling software for mine by taking Visual Studio as development tool, C++ and Python as development language, and SQLite as geological database, developed and Hydrogeo3D mine hydrogeology modeling software to realize the editable function of local details in the modeling process.

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study on soil zinc distribution and its influencing factors in Zunyi City is of great significance for mastering the spatial distribution of zinc in the region, soil environmental protection and human health. We collected 701 representive surface soil samples in Zunyi, and analyzed their zinc concentrations. GS+ software was used to build a semi variant function model, and the ordinary kriging method was used to predict the spatial distribution of zinc in the surface soil of Zunyi. In addition, the potential effects of physical and chemical properties of soil on the concentration and distribution of zinc in soil were also discussed. The results showed that the total zinc content of soil in Zunyi was ranged from 40.51 mg·kg-1 to 315.74 mg·kg-1, with the mean value of 107.9 mg·kg-1, The high and low value areas of soil zinc concentration were mainly concentrated in southeastern and southwestern Zunyi. The content of total zinc in soil had no significant correlation with pH, but had a significant positive correlation with soil organic matter and altitude. The study found that zinc in Zunyi area had obvious distribution in high and low value areas by analyzing the distribution of zinc and its influencing factors in Zunyi. The main factors causing this difference were parent material, soil organic matter and altitude. Spatial prediction of soil properties was complicated. In the future, more attention should be paid to the correlation among soil attributes and the influence of environmental factors, so as to obtain more accurate results. [ABSTRACT FROM AUTHOR]

The existing coal seam modeling methods using discrete points or geological boundaries from multi-source data lack enough attention to the dynamic changes of coal seam in the mining process. A new 3D coal seam fine modeling method is proposed. Firstly， creating regional grid of the mining area and converted to triangulated irregular network (TIN)，inter polating attributes of TIN nodes (elevation and thickness)using ordinary Kriging method，and then a generalized tri-prism(GTP) model of coal seam will be built as the initial model. Secondly， the initial model is refined gradually by the dynamic data within the influence domain of these data. A prototype system is created and implemented in Qianjiaying coal mine， Tangshan， China. The result shows that this method can accurately reflect the change of 3D coal seam model from the dynamic data， thus the accuracy of the 3D coal seam model can be improved gradually. [ABSTRACT FROM AUTHOR]

Ore grade estimation is relatively difficult due to the complexity of ore deposit formation process and numerous control factors. Evaluation of ore deposit with low estimation error is crucial in mineral resources development and usage. So far， Kriging， now known as a best estimation method of grade， is based on intrinsic assumption and stationarity about the underlying grade spatial distribution. However， most of ore grade data are spatially sparse， irregularly spaced and have complex distribution， which could result in the Kriging estimation method violating intrinsic assumption and stationarity. This article presented a new method for ore grade estimation based on multi-gene genetic programming and also compared it with ordinary Kriging. The results show that the proposed method makes no assumptions about the spatial distribution of grade data， the condition of implementing ore body grade prediction is simplified， and it can achieve better prediction effect. So， the proposed method can be used to estimate ore grade for complex ore deposit. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High-dimensional data interpolation is a basic content of big data analysis. The computational cost of traditional Kriging is of order ns, which means computational complexity grows as the third power rate along with the increase number of the observations. So Kriging computing time can hardly meet real-time applications. Thus, Fixed Rank Kriging(FRK) rises in response. To achieve high computing speed, it simplifies computational complexity by reducing the dimensions of large-dimensional matrix. Under the background of big data analysis, this paper uses FRK to do the statistical modeling and implement interpolation analysis with the global MODIS temperature. It also gives out Root-Mean-Square standardized which equals to 1. 0003 through the cross-validation method, indicating that FRK can provide high accurate interpolation result. On the side of computational efficiency, with increasing data, the computational cost of FRK increases as a slow and steady speed while ordinary kriging tends to an exponential growth. The conclusion can be drawn from two points above, that is, FRK can reduce computational complexity and shorten interpolation time with high prediction accuracy when compared with ordinary kriging. [ABSTRACT FROM AUTHOR]

Understanding daily minimum temperature is of great importance for assessing low temperature damages to crops and guiding people to take timely remedial measures to ensure food security. Kriging is a widely used technology for mapping the spatial distribution of the near-surface temperature. However, the smoothing effect, commonly found in the Kriging maps, leads to low values to be overestimated and high values to be underestimated. For daily minimum temperature on Hainan Island which was affected by cold air on December 12, 2011, cross-validation was adopted to evaluate the prediction accuracy of ordinary Kriging (OK) and Kriging with external drift (KED). The spatial distribution maps of daily minimum temperature on Hainan Island on December 12, 2011 produced by OK and sequential Gaussian simulation (SGS) were compared. Results showed that the prediction accuracy of KED (r =0. 86) was not superior to OK (r =0. 86) significantly. SGS could generate multiple equiprobable simulation realizations, and the distribution and variance function of the original data could be reproduced in the realizations. The simulation realizations generated by SGS overcame the smoothing effect of Kriging and could more truly reflect the spatial distribution of minimum temperature on the day on Hainan Island. In the region where daily minimum temperature was low, and the temperature change was small, the conditional variance of the SGS results was less than the ordinary Kriging variance. Spatial uncertainty of a potential chilling damage area could be quantified by multiple simulation realizations generated by SGS. SGS was a valuable tool for assessing agro-meteorological disasters caused by low temperature. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)