EurekaMag.com logo
+ Site Statistics
References:
47,893,527
Abstracts:
28,296,643
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on Google+Follow on Google+
Follow on LinkedInFollow on LinkedIn

+ Translate

Assessing the spatial uncertainty in soil nitrogen mapping through stochastic simulations with categorical land use information






Ecological Informatics 16: 1-9

Assessing the spatial uncertainty in soil nitrogen mapping through stochastic simulations with categorical land use information

This study explores the capability of an extended sequential Gaussian simulation algorithm with incorporation of categorical land use information (SGS-CI) for simulating spatial variability of soil total nitrogen (TN) contents and assessing associated spatial uncertainty. 402 sampled data in soil TN contents in a county scale region and the categorical land use map data of the study area were used to perform sequential simulations for comparing the SGS-CI algorithm and the conventional SGS algorithm, and 135 validation samples were used to assess the improvement of SGS-CI over SGS in prediction accuracy and uncertainty reduction. showed that the validation data were more strongly correlated with the optimal prediction (i.e., E-type estimates) data of SGS-CI than with those of SGS, and the mean error and the root mean square error of the optimal prediction using SGS-CI were smaller than those using SGS. SGS-CI also performed slightly better than SGS in uncertainty modeling in terms of accuracy plots and goodness statistic G. In addition, because demands for soil total nitrogen by different crops are usually different in agricultural practice, we showed that SGS-CI could be used to assess spatial uncertainty of deficiency or abundance degrees of soil TN based on demands of different crops in different land use types. Therefore, SGS-CI may provide an effective method for improving prediction accuracy and reducing uncertainty in soil TN prediction.


Accession: 036849295

DOI: 10.1016/j.ecoinf.2013.04.001



Related references

G.Buttafuoco, M.Conforti, P.P.C.Aucelli, G.Robustelli, F.Scarciglia, 2012: Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation. Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated...

Lin,Y.P., 2008: Simulating spatial distributions, variability and uncertainty of soil arsenic by geostatistical simulations in geographic information systems. This study quantifies and delineates the spatial distributions, variability and uncertainties of soil arsenic (As) in the northern part of Changhua County in central Taiwan by using kriging, sequential Gaussian simulation (SGS) and simulated annea...

Montanari, A.; Brath, A., 2004: A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Rainfall-runoff models have received a great deal of attention by researchers in the last decades. However, the analysis of their reliability and uncertainty has not been treated as thoroughly. In the present study, a technique for assessing the u...

Bourennane, H.; King, D.; Couturier, A.; Nicoullaud, B.; Mary, B.; Richard, G., 2007: Uncertainty assessment of soil water content spatial patterns using geostatistical simulations: An empirical comparison of a simulation accounting for single attribute and a simulation accounting for secondary information. This study compares sequential Gaussian simulation (sGs), and collocated cokriging simulation (CCS) algorithms with respect to their success in modeling prediction uncertainty, and their accuracy in making point predictions of water content (w) i...

Caridad-Cancela, R.V.dal-Vazquez, E.V.eira, S.; Abreu, C.; Paz-Gonzalez, A., 2005: Assessing the spatial uncertainty of mapping trace elements in cultivated fields. Many of the cultivated soils in Galicia (NW Spain) consist of grassland areas and, subsequently, cattle density is also considerable. As a result, dispersion of heavy metals into the rural environment associated with manure and slurry applications...

Lin, W-Chih.; Lin, Y-Pin.; Wang, Y-Chieh.; Chang, T-Kuo.; Chiang, L-Chi., 2015: Assessing and mapping spatial associations among oral cancer mortality rates, concentrations of heavy metals in soil, and land use types based on multiple scale data. In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate da...

Castrignano, A.; Buttafuoco, G.; Canu, A.; Zucca, C.; Madrau, S., 2008: Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation. Soil erosion varies greatly over space and is commonly estimated using the revised universal soil loss equation (RUSLE). Neglecting information about estimation uncertainty, however, may lead to improper decision-making. One geostatistical approa...

Luoto, M.; Marmion, M.; Hjort, J., 2010: Assessing spatial uncertainty in predictive geomorphological mapping; a multi-modelling approach. Maps of earth surface processes and the potential distribution of landforms make an important contribution to theoretical and applied geomorphology. Because decision making often depends on information based on spatial models, there is a great nee...

Bierkens, M.F.P.; Burrough, P.A., 1993: The indicator approach to categorical soil data: II. Application to mapping and land use suitability analysis. This second paper in the series presents a case study on the mapping of water table classes using indicator kriging. In particular, the behavior and performance of the method is investigated. The method performs well when validated on an independe...

Wang, G.; Gertner, G.; Anderson, A.B.; Howard, H.; Gebhart, D.; Althoff, D.; Davis, T.; Woodford, P., 2007: Spatial variability and temporal dynamics analysis of soil erosion due to military land use activities: Uncertainty and implications for land management. Human activities, such as military off-road vehicular traffic, disturb ground and vegetation cover of landscapes and increase potential rainfall related runoff and soil erosion. On military lands, soil erosion is of major concern in order to sust...