Search Results (30)

This study examines the spatial distribution of grain yield in the Songnen Plain Agro-Pastoral Zone in Heilongjiang Province from 2015, 2017, 2019 and 2021, using Kriging interpolation as the primary method. Ordinary Kriging (exponential kernel/semivariogram, step = 13) achieved optimal accuracy (RMSE = 0.856), outperforming Co-Kriging. Incorporating all covariates lowered precision due to weak spatial autocorrelation in slope and aspect, while limiting covariates to elevation and soil type improved results. Spatial patterns revealed a southwest-to-northeast gradient. Over time, yields increased notably in the southwest and northern areas, with Wudalianchi rising by 259.71%, but declining locally, such as a 12.20% drop in Shuangcheng. Environmental factors like slope and soil showed spatially heterogeneous influences, interacting with policies and socioeconomic variables. The grain yield center shifted slightly northward. Geographically Weighted Regression (GWR) further validated these spatial patterns. These findings provide valuable insights into covariate selection and spatial drivers, supporting more precise agricultural planning and management in the region. Full article

In low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with spatiotemporal stability. Spatiotemporal Stable Peak (SSP) maps were generated using the Ratio Vegetation Index (RVI) time-series data of rice and wheat, and they were used to represent crop growth information with spatiotemporal stability. Eighty-three soil sampling sites were arranged on the SSP maps with a regular grid. Ridge Regression, Ordinary Kriging, and Co-Kriging were adopted to map soil texture. The results showed that the SSP was closely related to clay and sand contents, with Pearson’s |r| ranging from 0.57 to 0.67. SSP-based Ridge Regression yielded better prediction accuracy (MAE = 3.95 and RMSE = 4.57) than Ordinary Kriging (MAE = 4.45 and RMSE = 5.19) in predicting clay content. The comparison between Ordinary Kriging and SSP-based Co-Kriging further demonstrated the effectiveness of SSP in improving clay content prediction accuracy, with an increase in R² of 70% and a reduction in RMSE of 3.85%. Similar results were obtained for sand content prediction. These results suggest that SSP can serve as an effective environmental variable for predicting soil texture spatial variation in low-relief agricultural areas. Full article

In the realm of hydrological engineering, integrating extensive geospatial raster data from remote sensing (Big Data) with sparse field measurements offers a promising approach to improve prediction accuracy in groundwater studies. In this study, we integrated multisource data by applying the LMC to model the spatial relationships of variables and then utilized block support regularization with collocated block cokriging (CBCK) to enhance our predictions. A critical engineering challenge addressed in this study is support homogenization, where we adjusted punctual variances to block variances and ensure consistency in spatial predictions. Our case study focused on mapping groundwater table depth to improve water management and planning in a mixed land use area in Southeast Brazil that is occupied by sugarcane crops, silviculture (Eucalyptus), regenerating fields, and natural vegetation. We utilized the 90 m resolution TanDEM-X digital surface model and STEEP (Seasonal Tropical Ecosystem Energy Partitioning) data with a 500 m resolution to support the spatial interpolation of groundwater table depth measurements collected from 56 locations during the hydrological year 2015–16. Ordinary block kriging (OBK) and CBCK methods were employed. The CBCK method provided more reliable and accurate spatial predictions of groundwater depth levels (RMSE = 0.49 m), outperforming the OBK method (RMSE = 2.89 m). An OBK-based map concentrated deeper measurements near their wells and gave shallow depths for most of the points during estimation. The CBCK-based map shows more deeper predicted points due to its relationship with the covariates. Using covariates improved the groundwater table depth mapping by detecting the interconnection of varied land uses, supporting the water management for agronomic planning connected with ecosystem sustainability. Full article

Assessing and mapping the geographical variation of soil properties is essential for precision agriculture to maintain the sustainability of the soil and plants. This study was conducted in El-Ismaillia Governorate in Egypt (arid zones), to establish site-specific management zones utilizing certain soil parameters in the study area. The goal of the study is to map out the variability of some soil properties. One hundred georeferenced soil profiles were gathered from the study area using a standard grid pattern of 400 × 400 m. Soil parameters such as pH, soil salinity (EC), soil organic carbon (SOC), calcium carbonate (CaCO₃), gravel, and soil-available micronutrients (Cu, Zn, Mn, and Fe) were determined. After the data were normalized, the soil characteristics were described and their geographical variability distribution was shown using classical and geostatistical statistics. The geographic variation of soil properties was analyzed using semivariogram models, and the associated maps were generated using the ordinary co-Kriging technique. The findings showed notable differences in soil properties across the study area. Statistical analysis of soil chemical properties showed that soil EC and pH have the highest and lowest coefficient of variation (CV), with a CV of 110.05 and 4.80%, respectively. At the same time Cu and Fe had the highest and lowest CV among the soil micronutrients, with a CV of 171.43 and 71.43%, respectively. Regarding the physical properties, clay and sand were the highest and lowest CV, with a CV of 177.01 and 9.97%, respectively. Moreover, the finest models for the examined soil attributes were determined to be exponential, spherical, K-Bessel, and Gaussian semivariogram models. The selected semivariogram models are the most suitable for mapping and estimating the spatial distribution surfaces of the investigated soil parameters, as indicated by the cross-validation findings. The results demonstrated that while Fe, Cu, Zn, gravel, silt, and sand suggested a weak spatial dependence, the soil variables under investigation had a moderate spatial dependence. The findings showed that there are three site- specific management zones in the investigated area. SSMZs were classified into three zones, namely high management zone (I) with an area 123.32 ha (7.09%), moderate management zone (II) with an area 1365.61ha (78.49%), and low management zone (III) with an area 250.8162 ha (14.42%). The majority of the researched area is included in the second site zone, which represents regions with low productivity. Decision-makers can identify locations with the finest, moderate, and poorest soil quality by using the spatial distribution maps that are produced, which can also help in understanding how each feature influences plant development. The results showed that geostatistical analysis is a reliable method for evaluating and forecasting the spatial correlations between soil properties. Full article

Due to their complex morphology, karst terrains are particularly more fragile and vulnerable to environmental damage compared to most natural systems. Their hydraulic properties, such as their transmissivity (T) and spatial variability, can be relevant for understanding groundwater flow and, consequently, for the sustainable management of water resources. The application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with uncertainty quantification. Direct measurements of T are typically scarce, while those of the specific capacity (S_c) are more frequent. We established a linear and spatial relationship between the logarithms of T and S_c measured in 174 wells in a semi-arid karst region in northeastern Brazil. These relationships were used to construct a cross-variogram, whose Linear Model of Coregionalization proved valid. The values and the cross-variogram of logT and logS_c were used to generate interpolations over 2554 values of logS_c, which did not spatially coincide with logT. We used ordinary co-kriging (CO-OK) and conditional sequential Gaussian co-simulation (CO-SGS) to generate the interpolations. The cross-variogram of logT and logS_c, when considering 174 wells, was isotropic with an exponential structure, a nugget effect of approximately 20% of the sill, and a range of 5 km. Cross-validation indicated an optimal number of 10 neighboring wells used in CO-OK, and we used 500 stochastic realizations in CO-SGS, which were then used to generate maps of logT estimates, deviations derived from the interpolations, and probabilistic scenarios. The resulting transmissivity maps are relevant for the design of groundwater management strategies, including stochastic approaches where the transmissivity realizations can be used to parameterize multiple executions of numerical flow models. Full article

Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data’s spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available. Full article

Forest above-ground biomass (AGB) is an excellent indicator for the health status and carbon sink potential of forest ecosystems, as well as the effectiveness of sustainable forest management practices. However, due to the strong heterogeneity of forest structures, acquiring high-accuracy and high-resolution AGB distributions over wide regions is often prohibitively expensive. To fill the resulting gap, this paper uses part of Lishui city, Zhejiang province as the study area, based on 168 forest sample observations, and proposes a novel integrated framework that combines a multi-scale geographically weighted regression (MGWR) with the co-kriging algorithm to refine the spatial downscaling of AGB. Specifically, optimal predictor variable sets identified by random forest importance ranking, multiple stepwise regression, and Pearson VIF methods were first assessed based on their total explanatory power (R square), followed by reconfirmation of the optimal predictor variable set based on the non-stationarity impact of each variable’s action scale (bandwidth) on the output pattern of AGB downscaling. The AGB downscaling statistical algorithms included MGWR, GWR, random forest (RF), and the ordinary least square (OLS), and their downscaling performances were quantitatively compared to determine the best downscaling method. Ultimately, the downscaled AGB pattern was produced using the best method, which was further refined by considering the spatial autocorrelation in AGB samples by implementing a co-kriging interpolation analysis of the predicted AGB downscaling residuals. The results indicated that the variable set selected by random forest importance ranking had the strongest explanatory power, with a validation R square of 0.58. This was further confirmed by the MGWR analysis which showed that the set of variables produced a more spatially smooth downscaled AGB pattern. Among the set of optimal variables, elevation and aspect affected AGB at local scales, representing a strong spatial heterogeneity. Some textural features and spectral features showed a smooth action scale relative to AGB, showing insignificant spatial scale processes. In the study area with complex terrain, using aspect as a covariant, the co-kriging (CK) model achieved a higher simulation accuracy for the MGWR-predicted AGB residuals than the ordinary kriging model. Overall, the proposed MGWR-CK model, with a final validation R square value of 0.62, effectively improved the spatial distribution characteristics and textural details of AGB mapping without the additional costs of procuring finer satellite images and GIS-based features. This will contribute to the accurate assessment of carbon sinks and carbon stock changes in subtropical forest ecosystems globally. Full article

The safe operation of dams is related to the lifeline of the national economy, the safety of the people, and social stability, and dam safety monitoring plays an essential role in scientifically controlling the safety of dams. Since the effects of environmental variables were not considered in conventional monitoring data repairing methods (such as the single time series model and spatial interpolation model), a spatial model for repairing monitoring data combining the variable importance for projection (VIP) method and cokriging was put forward in this paper. In order to improve the accuracy of the model, the influence of different combinations of covariates on it was discussed, and the VIP_j value greater than 0.8 was proposed as the threshold of covariates. The engineering verification shows that the VIP-cokriging spatial model had the advantages of high precision and strong applicability compared with the inverse distance weighting (IDW) model, the ordinary kriging model, and the universal kriging model, and the overall error can be reduced by more than 60%, which could better realize the expansion of the monitoring effect variable to the whole area of the dam space. The engineering application of the PBG dam showed that the model scientifically correlated the existing monitoring points with the spatial location of the dam, and reasonably repaired the measured values of the stopping and abnormal measured points, effectively ensuring that the spatial regular of the monitoring data could truly reflect the actual safety and operational status of the dam. Full article

Salt mining and shrimp farming have been practiced in the Non Thai district and the surrounding areas for more than 30 years, creating saline soil problems. To solve the soil salinity problem, soil salinity prediction and mapping utilizing the electromagnetic induction method (EMI) and spatial interpolation methods were examined in the Non Thai district, Nakhon Ratchasima province, Thailand. The research objectives were (1) to predict soil salinity using spatial interpolation methods and (2) to identify a suitable spatial interpolation method for soil salinity severity mapping. The research methodology consisted of five steps: apparent electrical conductivity (ECa) measurement using an electromagnetic induction (EMI) method; in situ soil sample collection and electrical conductivity of the saturated soil paste extract (ECe) measurement; soil electrical conductivity estimation using linear regression analysis (LRA); soil salinity prediction and accuracy assessment; and soil salinity severity classification and overlay analysis with relevant data. The result of LRA showed a strong positive relationship between ECe and ECa. The correlation coefficient (R) values of a horizontal measuring mode (HH) and a vertical measuring mode (VV) were 0.873 to 0.861, respectively. Four selected interpolation methods—Inverse Distance Weighting (IDW), Ordinary Kriging (OK), Ordinary CoKriging (OCK) with soil moisture content, and Regression Kriging (RK) without covariable factor—provided slightly different patterns of soil salinity prediction with HH and VV modes. The mean values of the ECe prediction from the four methods at the district level varied from 2156.02 to 2293.25 mS/m for HH mode and from 2377.38 to 2401.41 mS/m for VV mode. Based on the accuracy assessment with the rank-sum technique, the OCK is a suitable interpolation method for soil salinity prediction for HH mode. At the same time, the IDW is suitable for soil salinity prediction for the VV mode. The dominant soil salinity severity classes of the two measuring modes using suitable spatial interpolation methods were strongly and very strongly saline. Consequently, the developed research methodology can be applied to conduct soil salinity surveys to reduce costs and save time in other areas by government agencies in Thailand. Nevertheless, to apply the EMI method for soil salinity survey, the users should understand the principle of EMI and how to calibrate and operate the EM device properly for accurate ECa measurement. Full article

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R², whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management. Full article

ASTM D6433 is used to assess the need for maintenance of pavement sections. Although the Pavement Condition Index (PCI) factor calculation method provides reliable values, this method analyzes sections and defects individually and indicates current maintenance needs, but it cannot be used to predict the occurrence of new defects. Therefore, it is necessary to complement this method by considering variables that influence the occurrence of faults, among which are the geospatial distribution and the specific characteristics of the slabs. This research focuses on the identification of multiple types of disturbances that exist in Portland Cement Pavements (PCC), located in a high traffic area in the city of Valdivia (Chile). A spatial geostatistical relationship is established through visual inspection using geographical maps, as well as distribution, using the kriging method. This technique makes use of variograms that allow quantifying the parameters used in this study, thus expressing the spatial autocorrelation of the faults analyzed. From the results obtained by spatial geostatistics and kriging, it is possible to generate a data correlation for the distribution and characteristics of the streets considered. In addition, a co-kriging method is established instead of an ordinary kriging method. The relationship between observed and predicted values improved from 0.3327 to 0.5770. The width of the slabs, as well as some streets, is shown in our analysis to be unimportant. For better model accuracy, the number of covariates associated with the type of vehicle traffic, the age and shape of the slabs, and the construction techniques used for the pavement needs to increase. Full article

Auxiliary data has usually been incorporated into geostatistics for high-accuracy spatial prediction. Due to the different spatial scales, category and point auxiliary data have rarely been incorporated into prediction models together. Moreover, traditionally used geostatistical models are usually sensitive to outliers. This study first quantified the land-use type (LUT) effect on soil total nitrogen (TN) in Hanchuan County, China. Next, the relationship between soil TN and the auxiliary soil organic matter (SOM) was explored. Then, robust residual cokriging (RRCoK) with LUTs was proposed for the spatial prediction of soil TN. Finally, its spatial prediction accuracy was compared with that of ordinary kriging (OK), robust cokriging (RCoK), and robust residual kriging (RRK). Results show that: (i) both LUT and SOM are closely related to soil TN; (ii) by incorporating SOM, the relative improvement accuracy of RCoK over OK was 29.41%; (iii) by incorporating LUTs, the relative improvement accuracy of RRK over OK was 33.33%; (iv) RRCoK obtained the highest spatial prediction accuracy (RI = 43.14%). It is concluded that the recommended method, RRCoK, can effectively incorporate category and point auxiliary data together for the high-accuracy spatial prediction of soil properties. Full article

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors. Full article

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity. Full article

Remote sensing can be fruitfully used in the characterization of metals within stockpiles and tailings, produced from mining activities. Satellite information, in the form of band ratio, can act as an auxiliary variable, with a certain correlation with the ground primary data. In the presence of this auxiliary variable, modeled with nested structures, the spatial components without correlation can be filtered out, so that the useful correlation with ground data grows. This paper investigates the possibility to substitute in a co-kriging system, the whole band ratio information, with only the correlated components. The method has been applied over a bauxite residues case study and presents three estimation alternatives: ordinary kriging, co-kriging, component co-kriging. Results have shown how using the most correlated component reduces the estimation variance and improves the estimation results. In general terms, when a good correlation with ground samples exists, co-kriging of the satellite band-ratio Component improves the reconstruction of mineral grade distribution, thus affecting the selectivity. On the other hand, the use of the components approach exalts the distance variability. Full article