The Spatial Pattern Characteristics of Soil Nutrients at the Field Scale

Yang, Yixin; Zhu, Jianhua; Tong, Xueqin; Wang, Dianchang

doi:10.1007/978-1-4419-0209-2_14

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…Moreover, the accuracy of the predictions has been measured through the Mean Error (ME), the Mean Absolute Error (MAE), as well as the Root Mean Square Error (RMSE) (Wang et al 2007;Yang et al 2009), computed as follows:…”

Section: A Comparative Assessment Of the Prediction Resultsmentioning

confidence: 99%

“…Moreover, the accuracy of the predictions has been measured through the Mean Error ( ME ), the Mean Absolute Error ( MAE ), as well as the Root Mean Square Error ( RMSE ) (Wang et al ; Yang et al ), computed as follows:

M E = \frac{1}{n} \sum_{α = 1}^{n} [z (u_{α}) - \overset{z}{true} (u_{α})],

M A E = \frac{1}{n} \sum_{α = 1}^{n} [| z (u_{α}) - \overset{z}{true} (u_{α}) |],

R M S E = \sqrt{\frac{1}{n} \sum_{α = 1}^{n} [z (u_{α}) - \overset{z}{true} (u_{α})]^{2}},

where

z (u_{α})

represents the measured value at the sampled location

u_{α}, \overset{z}{true} (u_{α})

corresponds to the estimated value and n is the number of sampled data.…”

Section: Ock Versus Rkmentioning

confidence: 99%

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Radon Predictions withGeographical Information SystemCovariates: From Spatial Sampling to Modeling

Iaco

Maggio

Palma

2016

Geographical Analysis

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Radon (Rn) is a potentially toxic gas in soil which may affect human health. Assessing Rn levels in soil gas usually requires enormous efforts in terms of time and costs, since the sampling protocol is very complex. In most cases, the variable under study is sparsely sampled over the domain and this could affect the reliability of the spatial predictions. For this reason, it is useful to incorporate, into the estimation procedure, some auxiliary variables, correlated with the in soil gas Rn concentrations (primary variable) and more densely available over the domain. On the basis of this latter aspect, it is even better if the covariates are derived from a geographical information system (GIS). In this article, the Rn sampling protocol used during a measurement campaign planned over a risk area is described and the process of deriving GIS covariates considered as secondary information for predicting the primary variable is clarified. Then, multivariate modeling and prediction of the Rn concentrations over the domain of interest are discussed and a comparative study regarding the performance of the prediction procedures is presented. Rn prone areas are also analyzed with respect to urban and school density. All these aspects can clearly support decisions on environmental and human safeguard.

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Section: A Comparative Assessment Of the Prediction Resultsmentioning

confidence: 99%

M E = \frac{1}{n} \sum_{α = 1}^{n} [z (u_{α}) - \overset{z}{true} (u_{α})],

M A E = \frac{1}{n} \sum_{α = 1}^{n} [| z (u_{α}) - \overset{z}{true} (u_{α}) |],

R M S E = \sqrt{\frac{1}{n} \sum_{α = 1}^{n} [z (u_{α}) - \overset{z}{true} (u_{α})]^{2}},

where

z (u_{α})

represents the measured value at the sampled location

u_{α}, \overset{z}{true} (u_{α})

corresponds to the estimated value and n is the number of sampled data.…”

Section: Ock Versus Rkmentioning

confidence: 99%

Radon Predictions withGeographical Information SystemCovariates: From Spatial Sampling to Modeling

Iaco

Maggio

Palma

2016

Geographical Analysis

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show abstract

“…The accuracy of the predictions has been measured through the Root Mean Square Error (RMSE) index [35,38], defined as follows:…”

Section: A Comparison Among the Prediction Resultsmentioning

confidence: 99%

Radon Risk Analysis Through Geostatistical Tools Implemented in a WebGIS

Distefano¹,

Iaco²,

Palma³

et al. 2015

Current Air Quality Issues

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“…The indices used for cross-validation were: mean standardized error (ME), root mean square error (RMSE) and mean relative error (MRE) (Yang et al, 2009; Bhunia, Shit & Pourghasemi, 2019): with Z ( x i ) the measured value of SOC of each sampling point, Z′ ( x i ) the predicted value derived from the interpolation methods, N the number of validation points and Δ the range between the maximum and minimum data obtained.…”

Section: Methodsmentioning

confidence: 99%

Land use is the main driver of soil organic carbon spatial distribution in a high mountain ecosystem

Fusaro

Sarria-Guzmán

Chávez-Romero

et al. 2019

PeerJ

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BackgroundTerrestrial ecosystems play a significant role in carbon (C) storage. Human activities, such as urbanization, infrastructure, and land use change, can reduce significantly the C stored in the soil. The aim of this research was to measure the spatial variability of soil organic C (SOC) in the national park La Malinche (NPLM) in the central highlands of Mexico as an example of highland ecosystems and to determine the impact of land use change on the SOC stocks through deterministic and geostatistical geographic information system (GIS) based methods.MethodsThe soil was collected from different landscapes, that is, pine, fir, oak and mixed forests, natural grassland, moor and arable land, and organic C content determined. Different GIS-based deterministic (inverse distance weighting, local polynomial interpolation and radial basis function) and geostatistical interpolation techniques (ordinary kriging, cokriging and empirical Bayes kriging) were used to map the SOC stocks and other environmental variables of the top soil layer.ResultsAll interpolation GIS-based methods described the spatial distribution of SOC of the NPLM satisfactorily. The total SOC stock of the NPLM was 2.45 Tg C with 85.3% in the forest (1.26 Tg C in the A horizon and 0.83 Tg C in the O horizon), 11.4% in the arable soil (0.23 Tg in the A horizon and only 0.05 Tg C in the O horizon) and 3.3% in the high moor (0.07 Tg C in the A horizon and <0.01 Tg C in the O horizon). The estimated total SOC stock in a preserved part of the forest in NPLM was 4.98 Tg C in 1938 and has nearly halved since then. Continuing this trend of converting all the remaining forest to arable land will decrease the total SOC stock to 0.52 Tg C.DiscussionDifferent factors explain the large variations in SOC stocks found in this study but the change in land use (conversion of forests into agricultural lands) was the major reason for the reduction of the SOC stocks in the high mountain ecosystem of the NPLM. Large amounts of C, however, could be stored potentially in this ecosystem if the area was used more sustainable. The information derived from this study could be used to recommend strategies to reverse the SOC loss in NPLM and other high-altitude temperate forests and sequester larger quantities of C. This research can serve as a reference for the analysis of SOC distribution in similar mountain ecosystems in central part of Mexico and in other parts of the world.

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The Spatial Pattern Characteristics of Soil Nutrients at the Field Scale

Cited by 14 publications

References 4 publications

Radon Predictions withGeographical Information SystemCovariates: From Spatial Sampling to Modeling

Radon Predictions withGeographical Information SystemCovariates: From Spatial Sampling to Modeling

Radon Risk Analysis Through Geostatistical Tools Implemented in a WebGIS

Land use is the main driver of soil organic carbon spatial distribution in a high mountain ecosystem

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