Reconstructing High-Precision Coral Reef Geomorphology from Active Remote Sensing Datasets: A Robust Spatial Variability Modified Ordinary Kriging Method

Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.

DEM Generation with ICESat-2 Altimetry Data for the Three Antarctic Ice Shelves: Ross, Filchner–Ronne and Amery

The ice shelf is an important component of the Antarctic system, and the interaction between the ice sheet and the ocean often proceeds through mass variations of the ice shelf. The digital elevation model (DEM) of the ice shelf is particularly important for ice shelf elevation change and mass balance estimation. With the development of satellite altimetry technology, it became an important data source for DEM research of Antarctica. The National Aeronautics and Space Administration (NASA) Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) launched in 2018 is a significant improvement in along-track sampling rate and measurement accuracy compared with previous altimetry satellites. This study uses ordinary kriging interpolation to present new DEMs (ICESat-2 DEM hereinafter) for the three ice shelves (Ross, Filchner–Ronne and Amery) in Antarctica with ICESat-2 altimetry data. Two variogram models (linear and spherical) of ordinary kriging interpolation are compared in this paper. The result shows that the spherical model generally shows better performance and lower standard deviation (STD) than the linear models. The precision of the ultimate DEM was evaluated by NASA Operation IceBridge (OIB) data and compared with five previously published Antarctic DEM products (REMA, TanDEM-X PolarDEM, Slater DEM, Helm DEM, and Bamber DEM). The comparison reveals that the mean difference between ICESat-2 DEM of the Ross ice shelf and OIB is −0.016 m with a STD of 0.918 m, and the mean difference between ICESat-2 DEM of the Filchner–Ronne ice shelf and OIB is −0.533 m with a STD of 0.718 m. The three ICESat-2 DEMs show higher spatial resolution and elevation accuracy than five previously published Antarctic DEMs.

Development of a Kriging Assessment Framework and Impact Pathway for Researching Contamination With Potentially Toxic Elements in Urban Landfill Soil

Landfill post-closure with contaminant concentration in soil below permissible limit assessed at limited spot does not represent the contamination issue. Assessment limit to professionals also does not gives a potential of change to practice constant assessment to a wider context of assessor - citizen living nearby - as a collaborative effort to sustain a safe environment. Therefore sizeable, qualitative, and cost-effective analysis of the concentrations of contaminants is needed and this work recommends kriging assessment and the logical impact pathway framework as factors of change in landfill aftercare management. The kriging framework is developed utilising lead (Pb) and chromium (Cr) data from inductively coupled plasma mass spectrometry (ICP-MS) analysis. The development of the kriging framework is conducted based on the observation of censored data from ICP-MS analysis. The estimation analysis involves the analysis of ordinary kriging with regression analysis, showing the interpolation of spatial correlation and regression error. Hence, ordinary kriging with regression of the variable of interest, i.e., Pb, using the data of the explanatory variable, i.e., Cr, is inappropriate. Further investigation with the utilisation of guess-field kriging analysis hypothetically exposed a potential contaminated area using an existing but limited number of explanatory variables; although, guess-field kriging may possibly result immense uncertainty at the area where the explanatory variable does not exist. Besides, this work anticipated outcomes in societal impact and sustainability practices from the proposed kriging framework by recommending a logical impact pathway. The development of the kriging framework and impact pathway reassure the necessary actions to be executed by responsible parties and act as the stimulus of a wider spectrum of improvement initiatives to oversee real issues, such as the time of occurrence, and to prevent negative impacts on the environment and humans.

Review of the Kriging Technique Applications to Groundwater Quality

Kriging, a geostatistical technique, has been used for many years to evaluate groundwater quality. The best estimation data for unsampled points were determined by using this method depending on measured variables for an area. The groundwater contaminants assessment worldwide was found through many kriging methods. The present paper shows a review of the most known methods of kriging that were used in estimating and mapping the groundwater quality. Indicator kriging, simple kriging, cokriging, ordinary kriging, disjunctive kriging and lognormal kriging are the most used techniques. In addition, the concept of the disjunctive kriging method was explained in this work to be easily understood.

The Comparison of Spatial Models in Peak Ground Acceleration (PGA) Study

This study was conducted to compare the performance of three different spatial analysis models: Inverse Distance Weighted (IDW), Ordinary Kriging,­­ and Regularized Spline interpolation technique to determine the best fit model representing Peak Ground Acceleration (PGA) in West Java Province, Indonesia. The three models are commonly used in spatial visualization, but have different calculation methods. The calculations were performed using available formulas while the spatial modeling was conducted using the algorithms in GIS software. Meanwhile, the accuracy of the spatial model and factual calculation was determined through the Root Mean Square Error (RMSE). The results showed differences for both spatial distribution and maximum and minimum values for each model. However, IDW was observed to be the model which approaches the factual value of the PGA calculation as indicated by its RMSE value of 0.772352 in comparison with the 7.169879 (Ordinary Kriging) and 1.140802 (Regularized Spline).

Efficiency of Ni Content in Laterite Nickel Deposits through The Least Square Method Approach on Semivariogram

Ordinary kriging is one of the geostatistical techniques used for spatial prediction on a spatially distributed random plane. Ordinary kriging is a linear unbiased estimator which is part of a semivariogram system of equations that minimizes errors of variance in estimating mineral resources. The semivariogram model shows optimal results in the estimation using the least square method, the effective minimization method smoothes the data points against the curve on a semivariogram graph, the least square makes the size error efficient in the semivariogram model and has been proven to be effective in reducing errors in the semivariogram model in the case of laterite nickel deposits. at PT. Vale Indonesia Tbk. Thus, conclusively the prediction of unsampled Ni content results is very accurate. This is indicated by the lowest root mean square error (RMSE) in limonite in the exponential model, saprolite in the spherical model, and bedrock in the gaussian model. The greatest value of Ni content in this study was in the saprolite layer.

The Application of Semi Variogram and Ordinary Kriging in Determining the Cohesion and Clay Percentage Distribution in Hilly Area of Sabah, Malaysia

Ordinary Kriging (OK) is one of the geostatistical methods, which were used in the variation types of mapping, which related to the soil. Compliment by semi variogram models, OK has become one of the most sought out method for the digital mapping, which applied Geographical Information System (GIS) as a main approach. Four semi variogram models, which are spherical, exponential, circular and gaussian would be applied to determine the best model for the mapping purposes, with Root-Mean-Squared-Error (RMSE) as a performance indicator. The value of the cohesion and clay percentage will be based according to the related depth. Each semi variogram model will be applied to determine the best model for each depth, whether it is cohesion or clay percentage, and producing a map, as a result. This mapping would be an alternative to the geological mapping, whereby it would show the range of the cohesion and clay percentage values rather than soil types.

Prediction of land degradation by Machine Learning Methods

In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

Evaluating Spatial Regression-Informed Cokriging of Metals in Soils Near Abandoned Mines in Bumpus Cove, Tennessee, USA

Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is important for risk assessment and remedial planning of sites where this has occurred. This study evaluated the prediction accuracy of optimized ordinary kriging compared to spatial regression-informed cokriging for PTMs (Zn, Mn, Cu, Pb, and Cd) in soils near abandoned mines in Bumpus Cove, Tennessee, USA. Cokriging variables and neighborhood sizes were systematically selected from prior statistical analyses based on the association with PTM transport and soil physico-chemical properties (soil texture, moisture content, bulk density, pH, cation exchange capacity (CEC), and total organic carbon (TOC)). A log transform was applied to fit the frequency histograms to a normal distribution. Superior models were chosen based on six diagnostics (ME, RMS, MES, RMSS, ASE, and ASE-RMS), which produced mixed results. Cokriging models were preferred for Mn, Zn, Cu, and Cd, whereas ordinary kriging yielded better model results for Pb. This study determined that the preliminary process of developing spatial regression models, thus enabling the selection of contributing soil properties, can improve the interpolation accuracy of PTMs in abandoned mine sites.

SITE INDEX IN EUCALYPTUS STANDS APPLYING ORDINARY KRIGING: AN APPROACH WITH DIFFERENT MODELS AND METHODS OF CLASSIFICATION

The objective of this work was to evaluate the efficiency of models and methods to obtain the site index, associated with ordinary kriging, to classify productive capacity in eucalyptus stands. Thus, the site quality was performed considering the traditional modeling in clonal stands (2,119 hectares) located in Minas Gerais state, Brazil. 170 plots of 400m2 were randomly allocated, representing a sampling intensity of 0.32%. The dominant height of trees (Assmann) was measured at 24, 36, 48, 60, 72, and 84 months. The site index (S) was estimated by the guide curve and algebraic difference methods, using the models of Schumacher, Chapman and Richards, and Bailey and Clutter. 136 plots were used in the fit and 34 plots in the predictive validation. The spatial dependence of site index was evaluated by experimental semivariogram and adjustment of exponential, spherical, and gaussian models. After confirming the spatial dependence, ordinary kriging was performed to spatialize the site index. For the predictive validation, the dominant height values at 72 months were used. The algebraic difference method provided excellent estimates of site index, which showed spatial dependence in all adjustments, from moderate to strong. In most cases, the gaussian model was the most accurate. It is concluded that the algebraic difference method was more efficient and the site index showed strong spatial dependence at all ages, regardless of the model used. Thus, regression models for site index estimation can be used in combination with ordinary kriging techniques.