Geoscience Reference
In-Depth Information
The shape of variograms can be used to understand the NDVI spatial structures within an
image domain (Garrigues et al., 2006). Millward and Kraft (2004) applied variograms
to evaluate the impacts of disturbances on landscapes. In this study, experimental
variogram and modeling results indicate that large disturbances, such as the Chi-Chi
earthquake, created extremely complex heterogeneous patterns across the landscape.
Notably, a disturbance may affect some areas but not others, and disturbance sever-
ity often varies considerably within an affected area on the landscape level (Lin et
al., 2006b; Turner and Dale, 1998). Variography results illustrate that NDVI discon-
tinuities between fi elds create a mosaic spatial structure resulting primarily from
large disturbances, such as the Chi-Chi earthquake, in the study areas. Moreover, the
high-magnitude Chi-Chi earthquake created these landscape variations in space in the
Chenyulan watershed (Lin et al., 2006b). Previous studies (Chang et al., 2007; Lin et
al., 2006b, 2008b) indicated that landslides in the Chenyulan watershed were impacted
by the Chi-Chi earthquake; however, the effect of the earthquake decreased as the time
between a typhoon and the Chi-Chi earthquake increased (Chang et al., 2007). More-
over, variography results confi rm that the impacts of disturbances on the watershed
landscape pattern were cumulative, but were not always evident in space and time
in the entire landscape (Chang et al., 2007; Lin et al., 2006c). Moreover, landslides
induced by earthquakes and typhoons have distinct spatial patterns (Lin et al., 2008b).
Typhoons signifi cantly infl uence NDVI variations via the fl ow of accumulated rain-
fall and wind gradients (Lin et al., 2008d). The statistical and variogram results also
indicate that basic statistics without variograms of NDVI images may not suffi cient to
present landscape changes induced by disturbances, particularly via spatial structure,
variability, and heterogeneity analysis. Moreover, variogram modeling results also
support the above statistical results, indicating that subsequent rainstorms may cause
divergent destruction of vegetation, and then this destruction may be infl uenced by the
precipitation distribution and typhoon path (Lin et al., 2003, 2006b).
Latin Hypercube Sampling for Multiple Images
Sampling strategies are typically based on an assumed theoretical framework (Edwards
and Fortin, 2001). Sampling under such a framework involves attempting to locate
samples to capture the possible variations and fluctuations in a gradient field (Edwards
and Fortin, 2001). An efficient sampling method is therefore needed to cover the
entire range of ancillary variables (Minasny and McBratney, 2006). In this study,
experimental variograms of cLHS samples with their NDVI values were constructed
using the same lag interval to compare the spatial structures of the actual NDVI
images. Figures 5 and 6 show experimental variograms for 100, 500, 1,000, 2,000,
2,500, and 3,000 cLHS samples in 1996/11/08, 1999/03/06, 1999/10/31, 2000/11/27,
2001/11/20, 2003/12/17, and 2004/11/19, respectively. These experimental vario-
grams show that as the number of samples increased from 100 to 3,000, the ability
of experimental variograms to capture the spatial structure of actual NDVI images
increased. These variography results show that the cLHS approach can simultane-
ously select samples from multiple NDVI images to capture spatial structures of all
NDVI spatial structures.
