A geostatistical approach to cork production sampling estimation in Quercus suber forests

2005 ◽  
Vol 35 (12) ◽  
pp. 2787-2796 ◽  
Author(s):  
Fernando Montes ◽  
María José Hernández ◽  
Isabel Cañellas
Keyword(s):  
Ordinary Kriging ◽  
Measurement Errors ◽  
Quercus Suber ◽  
Indicator Kriging ◽  
Surface Distribution ◽  
Geostatistical Approach ◽  
Cork Production ◽  
Cork Thickness ◽  
Productive Area ◽  
Kriging Estimation

The estimation of cork production in cork oak (Quercus suber L.) forests is complex because of the high heterogeneity of stripped surface distribution (the variable used to quantify cork production) and the importance of cork thickness estimation as a determining factor of cork quality. In this study, the different sources of variation in stripped surface ([Formula: see text]d) estimation and the effects of the spatial structure of the variance were analysed. When indicator kriging was used to determine the cork productive area, ordinary kriging and kriging with measurement errors gave better estimations of [Formula: see text]d (ordinary block kriging estimation of 156.16 m2/ha and standard errors (SE) of 16.40 and 15.7 m2/ha, respectively) than the design-based approach for the whole forest area (66.37 m2/ha, SE = 11.34 m2/ha). The SE lying in the second-stage design was 4.93 m2/ha. The ordinary kriging prediction of cork thickness using an XY(λZ) variogram, where λ is the anisotropy coefficient of the Z axis, gives a smaller SE and less bias than the kriging prediction with the XY variogram (for a mean estimation of 21.91 mm, SE = 3.90 and 4.16 mm, respectively, and sum of errors of 0.42 and 0.85 respectively).

scholarly journals Improvement of Spatial Modeling of Cr, Pb, Cd, As and Ni in Soil Based on Portable X-ray Fluorescence (PXRF) and Geostatistics: A Case Study in East China

2019 ◽  
Vol 16 (15) ◽  
pp. 2694 ◽  
Author(s):  
Xia ◽  
Hu ◽  
Shao ◽  
Xu ◽  
Zhou ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Hot Spots ◽  
Ordinary Kriging ◽  
Eastern China ◽  
Indicator Kriging ◽  
Polluted Water ◽  
X Ray ◽  
Soil Heavy Metals ◽  
Metals Pollution ◽  
Heavy Metals Pollution

To verify the feasibility of portable X-ray fluorescence (PXRF) for rapidly analyzing, assessing and improving soil heavy metals mapping, 351 samples were collected from Fuyang District, Hangzhou City, in eastern China. Ordinary kriging (OK) and co-ordinary kriging (COK) combined with PXRF measurements were used to explore spatial patterns of heavy metals content in the soil. The Getis-Ord index was calculated to discern hot spots of heavy metals. Finally, multi-variable indicator kriging was conducted to obtain a map of multi-heavy metals pollution. The results indicated Cd is the primary pollution element in Fuyang, followed by As and Pb. Application of PXRF measurements as covariates in COK improved model accuracy, especially for Pb and Cd. Heavy metals pollution hot spots were mainly detected in northern Fuyang and plains along the Fuchun River in southern Fuyang because of mining, industrial and traffic activities, and irrigation with polluted water. Area with high risk of multi-heavy metals pollution mainly distributed in plain along the Fuchun River and the eastern Fuyang. These findings certified the feasibility of using PXRF as an efficient and reliable method for soil heavy metals pollution assessment and mapping, which could contribute to reduce the cost of surveys and pollution remediation.

scholarly journals Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

2016 ◽  
Author(s):  
Ling Huang ◽  
Hongping Zhang ◽  
Peiliang Xu ◽  
Jianghui Geng ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Ordinary Kriging ◽  
Variance Components ◽  
Harmonic Functions ◽  
Measurement Errors ◽  
Spatial Interpolation ◽  
Interpolation Method ◽  
Single Frequency ◽  
Estimation Accuracy ◽  
Spherical Cap ◽  
Unknown Variance

Ionospheric delay has been a critical issue that limits the accuracy of GNSS precise positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where irregularity of ionosphere is often significant. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on TEC semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospherical delay. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The results have shown that the interpolation accuracy of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy.

scholarly journals Review of the Kriging Technique Applications to Groundwater Quality

2021 ◽  
Vol 27 (12) ◽  
pp. 23-32
Author(s):  
Hayat Azawi ◽  
May Samir Saleh
Keyword(s):  
Groundwater Quality ◽  
Quality Indicator ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Disjunctive Kriging ◽  
Kriging Method ◽  
Lognormal Kriging ◽  
Groundwater Contaminants ◽  
Simple Kriging

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.

scholarly journals Linking Cork to Cork Oak Landscapes: Mapping the Value Chain of Cork Production in Portugal

2021 ◽  
Vol 5 ◽  
Author(s):  
Irene Holm Sørensen ◽  
Mario Torralba ◽  
Cristina Quintas-Soriano ◽  
José Muñoz-Rojas ◽  
Tobias Plieninger
Keyword(s):  
Value Chain ◽  
Global Scale ◽  
Quercus Suber ◽  
Agroforestry Systems ◽  
Cork Oak ◽  
Oak Trees ◽  
Cork Production ◽  
Central Portugal ◽  
Characteristic Features ◽  
Farming Landscapes

Traditional farming landscapes in South and Central Portugal, known as montados, are affected by global socio-economic and biophysical pressures, putting the sustainability of the systems in jeopardy. Cork oak trees (Quercus suber L.) are characteristic features of these complex agro-silvo-pastoral agroforestry systems, delivering a globally important product, cork. The increasingly distant, global scale of decision making and trade can consequently be observed on the local, landscape, scale. In this study, we use a value chain approach to test the concept that landscape products can ensure sustainable management of the landscape of origin. We interviewed agents—cork producers, intermediaries, industrial transformers, and winemakers—about the challenges they perceived in the business and how these were connected to the landscape of origin. We illustrate the network of agents and sub-actors involved in the sector and highlight the most prominent concerns. We conclude that this approach can reveal the major points for determining the future of the montado, and we suggest that collaboration amongst value chain agents can be a pathway to landscape sustainability.

scholarly journals Monitoring the Spatial Variability of Knapweed (Centaurea diluta Aiton) in Wheat Crops Using Geostatistics and UAV Imagery: Probability Maps for Risk Assessment in Site-Specific Control

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 880
Author(s):  
Montserrat Jurado-Expósito ◽  
Francisca López-Granados ◽  
Francisco Manuel Jiménez-Brenes ◽  
Jorge Torres-Sánchez
Keyword(s):  
Risk Assessment ◽  
Spatial Variability ◽  
Weed Management ◽  
Management Strategies ◽  
Indicator Kriging ◽  
Site Specific ◽  
Weed Density ◽  
Density Mapping ◽  
Probability Maps ◽  
Geostatistical Approach

Assessing the spatial distribution of weeds within a field is a key step to the success of site-specific weed management strategies. Centaurea diluta (knapweed) is an emerging weed that is causing a major agronomic problem in southern and central Spain because of its large size, the difficulty of controlling it, and its high competitive ability. The main objectives of this study were to examine the spatial variability of C. diluta density in two wheat fields by multivariate geostatistical methods using unmanned aerial vehicle (UAV) imagery as secondary information and to delineate potential control zones for site-specific treatments based on occurrence probability maps of weed infestation. The primary variable was obtained by grid weed density field samplings, and the secondary variables were derived from UAV imagery acquired the same day as the weed field surveys. Kriging and cokriging with UAV-derived variables that displayed a strong correlation with weed density were used to compare C. diluta density mapping performance. The accuracy of the predictions was assessed by cross-validation. Cokriging with UAV-derived secondary variables generated more accurate weed density maps with a lower RMSE compare with kriging and cokriging with RVI, NDVI, ExR, and ExR(2) (the best methods for the prediction of knapweed density). Cokriged estimates were used to generate probability maps for risk assessment when implementing site-specific weed control by indicator kriging. This multivariate geostatistical approach enabled the delineation of winter wheat fields into two zones for different prescription treatments according to the C. diluta density and the economic threshold.

scholarly journals Application of kriging techniques for assessing the salinity of irrigated soils: the case of El Ghrous perimeter, Biskra, Algeria

2019 ◽  
Vol 9 ◽  
Author(s):  
Mohamed Amine Abdennour ◽  
Abdelkader Douaoui ◽  
Abdelhamid Bradai ◽  
Amel Bennacer ◽  
Manuel Pulido Fernández
Keyword(s):  
Soil Salinity ◽  
Ordinary Kriging ◽  
Indicator Kriging ◽  
Spatial Trend ◽  
Risk Areas ◽  
Geostatistical Techniques ◽  
Anthropogenic Processes ◽  
Irrigated Perimeter ◽  
Made In ◽  
Irrigated Soils

In semi-arid and arid areas, soil salinity has adverse effects both on the environment and agricultural production. The main causes of this salinization come from natural or anthropogenic processes, which is certainly an environmental problem that affects more than 20% of the world's land. This study was made in order to map the spatial distribution of soil salinity of the irrigated perimeter of El Ghrous in southeastern Algeria. These maps were performed based on data collected from 190 soil samples from 0 to 15 cm deep. We used ordinary kriging (OK) to analyze the spatial variability of soil salinity, while indicator kriging (IK) was used to analyze salinity versus threshold values. The salinity map predicted by the electrical conductivity (EC) values using the ordinary kriging (OK) method showed the different classes of salinity according to Durand's classification with moderately saline 3rd order dominance, while the unsalted soil (EC &lt; 0.6 dS m<sup>-1</sup>) represents a very low percentage (1.5%). The indicator kriging (IK) was carried out by four thresholds which correspond to the salinity class limits: EC &gt; 0.6, EC &gt; 1, EC &gt; 2, EC &gt; 3, and EC &gt; 4 dS m<sup>-1</sup>, for developing probability maps to determine risk areas. This study has shown the spatial trend of soil salinity by geolocation of different classes, and to carry out risk maps using geostatistical techniques.

scholarly journals Geostatistics as a tool to reduce the sampling effort in forest inventories

2021 ◽  
pp. 174-179
Author(s):  
Myrcia Minatti ◽  
Carlos Roberto Sanquetta ◽  
Sylvio Péllico Neto ◽  
Ana Paula Dalla Corte ◽  
Vinicius Costa Cysneiros
Keyword(s):  
Forest Inventory ◽  
Ordinary Kriging ◽  
Simple Random Sampling ◽  
Exponential Model ◽  
Indicator Kriging ◽  
Sampling Effort ◽  
Thematic Maps ◽  
Visual Interpretation ◽  
Forest Inventories ◽  
Geostatistical Techniques

Geostatistics is one of the tools applied to investigate the spatial variability of forests to reduce costs and recognize the best productivity areas for planning. This study aimed to test the performance of geostatistical techniques in reducing the sampling effort in forest inventories. For this purpose, we used the height of dominant trees as a discriminator of the homogeneous strata to obtain a better representation of the productivity within the forest stands. We carried out the study in Pinus taeda L. stands in the Center-South of Paraná, Brazil, by using plots from a forest inventory allocated with the systematic process. Then, we tested three models to determine the site curves (Schumacher, Chapman-Richards 2, and 3 coefficients) with the thirty-seventh year being the reference age. To model the spatial patterns of the dominant height, we used the ordinary kriging, and, after that, we generated the thematic maps of the site classes. Similarly, we used the indicator kriging which allowed obtaining the probabilities of high, medium, and low productivity sites. The processing of the stratified sampling, with the support of the visual interpretation of the images, allowed us to define five strata according to productivity. Results showed that ordinary kriging is effective in defining the productivity classes. Along with geostatistical techniques, it produces more homogeneous strata and reduces the errors of the forest inventory. Moreover, the best-selected model was the Chapman-Richards (3 coefficients) for the site curves. The exponential model was the best model to identify the best areas of the probability of occurrence of sites with higher productivity. The efficiency of indicative kriging generated thematic maps to delimit the likely locations of the most promising sites. Overall, geostatistics proved to be efficient concerning error when compared to simple random sampling.

Total sulfur variability analysis of coal deposits using ordinary kriging estimation

2021 ◽  
Author(s):  
Yuniar Siska Novianti ◽  
Romla Noor Hakim ◽  
Nurhakim ◽  
Hafidz Noor Fikri
Keyword(s):  
Ordinary Kriging ◽  
Total Sulfur ◽  
Variability Analysis ◽  
Coal Deposits ◽  
Kriging Estimation

scholarly journals PERBANDINGAN INTERPOLASI SPASIAL DENGAN METODE ORDINARY DAN ROBUST KRIGING PADA DATA SPASIAL BERPENCILAN (Studi Kasus: Curah Hujan di Kabupaten Karangasem)

2012 ◽  
Author(s):  
NI MADE SUMA FRIDAYANI ◽  
I PUTU EKA NILA KENCANA ◽  
KOMANG GDE SUKARSA
Keyword(s):  
Spatial Data ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Control Point ◽  
Mean Absolute Deviation ◽  
Kriging Method ◽  
Absolute Deviation ◽  
Record Keeping ◽  
Kriging Estimation ◽  
Extreme Observation

Kriging as optimal spatial interpolation can produce less precise predictive value if there are outliers among the data. Outliers defined as extreme observation value of the other observation values that may be caused by faulty record keeping, improper calibration equipment or other posibbilities. Development of Ordinary Kriging method is Robust Kriging which transforms weight of clasic variogram thus become variogram that robust to outlier. The spatial data that used in this research is the spatial data that contains outliers and meet the assumptions of Ordinary Kriging. The analysis showed that the estimation value of Ordinary Kriging and Robust Kriging method is not much different in terms of Mean Absolute Deviation values which generated by both methods. An increase value of Mean Absolute Deviation on Robust Kriging estimation does not indicate that the Ordinary Kriging method is more precise than Robust Kriging method in the rainfall estimates of Amlapura control point remind that Robust Kriging does not eliminate the data of observation such as the Ordinary Kriging method. In general, Ordinary Kriging and Robust Kriging method can estimate the rainfall value of Amlapura control point quite well although it is not able to cover the changes in rainfall value that occurs due to the behavior geographic data.

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