Geoscience Reference
In-Depth Information
CONCLUSIONS
This study presents a novel and effective approach that integrates cLHS, variograms,
kriging, and SGS in remotely sensed images for efficient monitoring, sampling and
mapping of the impacts of chronologically ordered large disturbances on spatial char-
acteristics of landscape changes to spatial structure, variability and heterogeneity. The
NDVI images, which can be generated almost immediately after the remotely sensed
data are acquired, were used as the inferential index because landscape changes in-
duced by a large disturbance are easily recognized by changes in NDVI images. Var-
iography of multiple NDVI images before and after a large disturbance is an essential
method for characterizing and quantifying the spatial variability, structure and hetero-
geneity of landscapes induced by a disturbance. The variography results illustrated
that cumulative impacts of disturbances on spatial variability existed and depended
on disturbance magnitudes and paths, but were not always evident in spatiotempo-
ral variability of landscapes in the study areas. Moreover, the cLHS approach is an
effective sampling approach for multiple true NDVI images from their multivariate
distributions to replicate the statistical distribution and spatial structures of the NDVI
images. The sufficient number of NDVI samples using cLHS can be used to monitor
and sample changes in landscapes induced by large physical disturbances. Kriging and
SGS combined with the sufficient number of cLHS samples can be used to estimate
and simulate NDVI images to generate maps that capture the spatial pattern and vari-
ability of actual NDVI images of disturbed landscapes. Kriging with sufficient number
of NDVI cLHS samples produces NDVI maps with the best local estimates to identify
patterns of NDVI images of disturbed landscapes. SGS with sufficient cLHS samples
generate multiple realizations and an average of the realizations of NDVI and captures
the spatial variability and heterogeneity of disturbed landscapes.
KEYWORDS
Chi-Chi earthquake
Latin hypercube sampling
Normalized difference vegetation index
Sequential Gaussian simulation
Variogram
ACKNOWLEDGMENTS
The authors thank the Soil and Water Conservation Bureau of Taiwan for providing
field data and financially supporting this research under Contract No. SWCB-92-026-
08. The authors also would like to thanks Mr. Deng for treatments of remote sensing
data.
 
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