Moreover, during typhoon seasons, a massive amount of loose earth and stones accumulated on the surface of slopes, increasing the risk of debris flows and additional landslides [14] that worsen the reselleck inhibitor Vegetation problem. Accordingly, monitoring, delineating 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 dynamics, as such data provide frequent spatial estimates of key earth surface variables [15, 16]. For example, the SPOT, LANSAT and MODIS data sets have notable advantages that account for their use in ecological applications, 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 [17-19]. The Normalized Difference Vegetation Index (NDVI), a widely used vegetation index, is typically used to quantify landscape dynamics, including vegetation cover and landslides changes induced by large disturbances [6, 8, 11, 16, 20]. Notably, NDVI images can be determined by simply geometric operations near-infrared and visible-red spectral data almost immediately 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 surrogate measure of canopy density [21]. A high NDVI indicates a high level of photosynthetic activity [22]. Moreover, significant differences in NDVI images before and after a natural disturbance can represent landscape changes, including vegetation and landslides induced by a disturbance that changes plant-covered land to bare lands or bare lands to plant-covered land [23].Spatial patterns in ecological systems are the result of an interaction among dynamic processes operating across abroad range of spatial and temporal scales [24-26]. Ecological manifestations of large disturbances are rarely homogeneous in their spatial coverage [4]. 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 pattern of spatial variability [27]. Samples of remotely sensed data (e.g., satellite or air-borne sensor imagery) can be employed to construct variograms for remotely sensed research [27]. Moreover, variograms have been used widely to understand the nature and causes of spatial variation within an image [28]. Modeling the variogram of NDVI images with high spatial resolution is an efficient approach for characterizing and quantifying heterogeneous spatial components (spatial variability and spatial structure) of a landscape and the spatial heterogeneity of vegetation cover at the landscape level [28, 29].