The Precision of C Stock Estimation in the Ludhikola Watershed Using Model-Based and Design-Based Approaches View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-12

AUTHORS

T. S. Chinembiri, M. C. Bronsveld, D. G. Rossiter, T. Dube

ABSTRACT

In this study, two sampling protocols using a model-based and a design-based framework were juxtaposed to evaluate their precision in the estimation of C stock in the Ludikhola watershed, Nepal. The model-based approach exploits the spatial dependencies in the sampled variable and may therefore be attractive over the design-based approach as it reduces the substantial costs of survey and effort required in the latter. Scales of spatial variability for C stock which resulted in a grid resolution of 10,000 m2 were determined using a reconnaissance variogram. Akaike information criterion was used for the selection of the best linear model of feature space for use in kriging with external drift (KED). Among the five tested covariates, distance, elevation, and aspect were statistically significant, with the best model of feature space accounting for 87.7% variability of C stock. An ANOVA established significance differences in mean C stocks (P = 0.00017). KED using the best model of feature space was found to be more precise, (9.89 ± 0.17) sqrt mg C/ha, than a pure-based approach of ordinary kriging and the design-based approach, (9.91 ± 0.8) sqrt mg C/ha. The confidence bounds of the two estimators showed that their confidence intervals will overlap 99.7% of the time, with both confidence intervals falling within the 95% confidence bounds of each other. There is less uncertainty around the mean C stock estimated using the model-based approach than the mean C stock estimated using the design-based approach. The model-based approach is a prospective option for the REDD framework. More... »

PAGES

297-309

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11053-013-9216-6

DOI

http://dx.doi.org/10.1007/s11053-013-9216-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028715304


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