Spatial Dependence Index for Cubic, Pentaspherical and Wave Semivariogram Models

Neto, Edemar Appel; Barbosa, Ismael Canabarro; Seidel, Enio Júnior; Oliveira, Marcelo Silva de

doi:10.1590/s1982-21702018000100010

Cited by 4 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equations 3 to 8 show the expressions of the SDI index for the spherical, exponential, Gaussian (Seidel and Oliveira, 2016), cubic, pentaspherical, and wave models (Appel Neto et al, 2018), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The SDM index was proposed following the same method as Oliveira (2014, 2016) and Appel Neto et al (2018). First, the spatial correlation measure (SCM) is calculated.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have highlighted the importance of measuring the spatial variability of agricultural and soil properties using geostatistical indexes Oliveira, 2014, 2016;Appel Neto et al, 2018;Santos et al, 2018;Amaral and Della Justina, 2019;Leroux and Tisseyre, 2019). Such indexes are useful to assess the quality of the model fit on the semivariogram (Pazini et al, 2015;Oldoni and Bassoi, 2016;Büttow et al, 2017) and, consequently, to indicate whether kriging interpolation results in good quality maps (Appel Neto et al, 2018). In addition, these indexes allow the comparison of within-field variability in different research situations such as when comparing the variability of different types of soils, chemical or physical properties, different crops, among others.…”

Section: Introductionmentioning

confidence: 99%

“…A Spatial Dependence Index (SDI) was proposed by Oliveira (2014, 2016) and Appel Neto et al (2018), which is a dimensionless index (free of measurement units), inspired by integral scale J1 (Russo and Jury, 1987) and based on spatial correlation area. This SDI index takes a specific form for each semivariogram model considered, being proposed for the spherical, exponential, Gaussian Oliveira, 2014, 2016), cubic, pentaspherical, and wave models (Appel Neto et al, 2018). For its construction, the SDI index considers the following parameters of the semivariogram: contribution, sill, range, and half of the maximum distance between sampled points.…”

Section: Introductionmentioning

confidence: 99%

“…In the matter of geostatistical indexes for measuring spatial variability, there is still room to expand this knowledge. Thus, following the same idea as Oliveira (2014, 2016) and Appel Neto et al (2018) in the construction of the SDI, the aim of this study was to develop a new spatial variability index, called the Spatial Dependence Measure (SDM), inspired by integral scale J2 (Russo and Jury, 1987), and comparing it with some of the indexes already exist in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

Self Cite

View full text Add to dashboard Cite

The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also dimensionless, in the same way as the Spatial Dependence Index (SDI), also considering more parameters of the semivariogram, when compared to the Spatial Dependence Degree (SPD) and Relative Nugget Effect (NE) indexes. In a simulation data study, it is observed that the SDI and SDM indexes showed an advantage over the SPD (or NE). To exemplify the application of the SDM in the proposal for the classification of soil properties, we used estimates of geostatistical parameters presented in the two studies. The results indicate that the SDM can be a measure that, analyzed together with the SDI, can help to improve the description of the spatial variability structure. Thus, this study expands the number of geostatisitcal-based measures and increases the power of decision on the description of the degree of spatial variability of agricultural and soil attributes.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The SDM index was proposed following the same method as Oliveira (2014, 2016) and Appel Neto et al (2018). First, the spatial correlation measure (SCM) is calculated.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

Self Cite

View full text Add to dashboard Cite

show abstract

Assessment of alternative methods for analysing maximum rainfall spatial data based on generalized extreme value distribution

Ferreira,

Liska,

Beijo

2024

Discov Appl Sci

View full text Add to dashboard Cite

The present study aimed to analyze and spatially model maximum rainfall in the southern and southwestern regions of Minas Gerais using spatial statistical methods. Daily data on maximum rainfall were collected from 29 cities in the region. To obtain predictions of maximum rainfall for return periods of 2, 5, 10, 50, and 100 years, Bayesian Inference was employed, utilizing the most appropriate prior for each locality. The spatial analysis of the phenomenon based on results obtained through Bayesian Inference was conducted using interpolation methods, including Inverse Distance Weighting (IDW) and Kriging (Ordinary Kriging (OK) and Log-Normal Kriging (LK)). Different semivariogram models were used, and the most suitable one was selected based on cross-validation results for each method, which were also compared to those of IDW. Additionally, a spatial analysis was carried out using max-stable processes and spatial Generalized Extreme Value (GEV) distribution, with the models evaluated based on Takeuchi’s Information Criteria. All models were also assessed by calculating the mean prediction error for six locations that were not used in model fitting. The results indicated that the most suitable models among Kriging and IDW for return periods of 2, 5, and 10 years were Gaussian (LK), Spherical (OK), and Wave (OK), respectively. Among the max-stable models and spatial GEV, the most suitable for modeling was the Smith max-stable model. Consequently, for spatial prediction over 50- and 100-year return periods, OK (Wave) and the Smith max-stable model were employed.

show abstract