Geoscience Reference
In-Depth Information
ates earthquakes that have disastrous effects on Taiwan (Lin et al., 2006a). Moreover,
typhoons that bring tremendous amounts of rainfall hit Taiwan every year from July
to October (Lin et al., 2008b). During 1996-2004, large disturbances in the following
sequence impacted central Taiwan: (1) typhoon Herb (August, 1996); (2) the Chi-Chi
earthquake (September, 1999); (3) typhoon Xangsane (November, 2000); (4) typhoon
Toraji (July, 2001); (5) typhoon Dujuan (September, 2003); and, (6) typhoon Mind-
ulle (June, 2004) (Lin et al., 2008b). In particular, after the Chi-Chi earthquake, the
expansion rate of landslide areas increased 20-fold in central Taiwan (Lin et al., 2003).
Numerous extension cracks, which accelerate landslides during downpours, were gen-
erated on hill slopes during the Chi-Chi earthquake (Lin et al., 2006b). Moreover,
during typhoon seasons, a massive amount of loose earth and stones accumulated on
the surface of slopes, increasing the risk of debris fl ows, and additional landslides (Lin
et al., 2001) that worsen the revegetation problem. Accordingly, monitoring, delineat-
ing and sampling landscape changes, spatial structure and spatial variation induced
by large physical disturbances are essential to landscape management and restoration,
and disaster management in Taiwan.
Remotely sensed data can describe surface processes, including landscape dynam-
ics, as such data provide frequent spatial estimates of key earth surface variables
(Garrigues et al., 2008b; Sellers, 1997). For example, the SPOT, LANSAT, and MO-
DIS data sets have notable advantages that account for their use in ecological applica-
tions, including a long-running historical time-series, a special resolution appropriate
to regional land-cover and land-use change investigations, and a spectral coverage
appropriate to studies of vegetation properties (Cohen and Goward, 2004; Hayes and
Cohen, 2007; Tarnavsky et al., 2008). The NDVI, a widely used vegetation index, is
typically used to quantify landscape dynamics, including vegetation cover and land-
slides changes induced by large disturbances (Garrigues et al., 2008b; Lee and Lin,
2008; Lin et al., 2008a, 2008b, 2008c). Notably, NDVI images can be determined by
simply geometric operations near-infrared and visible-red spectral data almost imme-
diately after remotely sensed data is obtained. The NDVI, which is the most common
vegetation index, has been extensively used to determine the vigor of plants as a sur-
rogate measure of canopy density (Jensen, 1996a). A high NDVI indicates a high level
of photosynthetic activity (Sellers, 1985). Moreover, signifi cant differences in NDVI
images before and after a natural disturbance can represent landscape changes, includ-
ing vegetation and landslides induced by a disturbance that changes plant-covered
land to bare lands or bare lands to plant-covered land (Lin et al., 2006c).
Spatial patterns in ecological systems are the result of an interaction among dy-
namic processes operating across abroad range of spatial and temporal scales (Lobo
et al., 1998; Urban et al., 1987; Wiens, 1989). Ecological manifestations of large
disturbances are rarely homogeneous in their spatial coverage (Millward and Kraft,
2004). Variograms are crucial to geostatistics. A variogram is a function related to the
variance to spatial separation and provides a concise description of the scale and pat-
tern of spatial variability (Curran and Atkinson, 1998). Samples of remotely sensed
data (e.g., satellite or air-borne sensor imagery) can be employed to construct vario-
grams for remotely sensed research (Curran and Atkinson, 1998). Moreover, vario-
grams have been used widely to understand the nature and causes of spatial variation
