Spatial analysis of shoreline recession and accretion
An optimal sampling design is an important consideration
when collecting large volumes of data: Too few data lead
to inconclusive results and oversampling is inefficient
and often costly. Due to the wide geographical impact
of coastal processes along the shore, shoreline, responses
(erosion or accretion) do not significantly vary from
transect to transect at some sample spacings. Thus, shorelines
rates of change exhibit a "nearest neighbor"
effect or spatial autocorrelation. This phenomenon inhibits
the utility of conventional statistical models for sample
In this paper we capitalize on this spatial autocorrelation
by using the Theory of Regionalized Variables or geostatistics.
Geostatistics enables global estimates of along-the0shoreline
rates of change, point estimates at unsampled locations,
and provides nomograms for optimal sample designs based
on the standard errors of this estimate.
Results of the spatial analyses show that using local
esimation variances, obtained by ordinary kriging, is
an effective means of determining optimal sample size.
The 50 m spacing of transects along Hatteras Island, North
Carolina, provides an excellent estimate ofrates between
transects. This dense sampling scheme is, however, redundant
due to the high degree of autocorrelation. Last, our confidence
in estimation of rate-of-change values in the spatial
domain (due to spatial continuity) far exceeds our confidence
in rate values calculated at a sample location in the
Dolan, R., Fenster, M.S., and Holme, S
Journal of Coastal Research, v. 8, no. 2, p. 263-285