Keywords: Aggregation, Spatial structure, Variogram, NDVI, Remote sensing
Abstract Remote sensing data can be aggregated to evaluate, model and monitor in environment and ecology from local to lager scaled region. The effects of aggregated data sometimes also display on the output of models or the results of monitoring. Meanwhile, variograms provides a model of the spatial correlation of data within a statistical framework, including spatial covariance functions and a means of quantifying the commonly observed relationship. Therefore, this study performed isotropic and anisotropic experimental variograms in 12.5m, 25m, 37.5m, and 50m resolutions NDVI data from a Spot image at three different land-cover sites that were an almost pure grass, a mixed grass and shrub, and a broad forest within the yangmingshan national park in Taiwan to evaluate the impact of aggregation on the spatial structure at different land-cover sites. The results indicated that the experimental variograms of the NDVI data in the above four different resolutions displayed identical spatial structure in both isotropic and anisotropic directions at the pure grass site. At the mixed grass and shrub site the NDVI experimental variograms in the 12.5m, 25m, 37.5m, and 50m resolutions displayed a similar spatial tendency but different spatial variations. The experimental variograms of NDVI of these four resolutions at the broad-forest site displayed different spatial patterns and spatial variations in both isotropic and anisotropic directions. The experimental variograms of the 25 m and 50 m resolutions of the broad forest site displayed a similar tendency. Moreover, the 12.5 m and 37.5 m resolutions NDVI data of the broad forest site exhibit a similar pattern on their variograms.
1. Introduction
Remote sensing data can be aggregated to evaluate, model and monitor in local or global environmental and ecological study. However, the aggregations of data increase each pixel size of data and reduce the number of pixel in a sampling site. Moreover, these aggregated data are often referred to having a coarser spatial resolution (Bian and Butler, 1999). The effects of aggregated data sometimes also display on the output of models. Therefore, many scientists recently focused on these data aggregation effects to evaluate different aggregation methods.
The normalized different vegetation index (NDVI) calculated by remote sensing data can be used to evaluate monitor the spatial and temporal vegetation change. Meanwhile, the NDVI is preferred to the simple index for global vegetation monitoring (Lillesand and Kiefer, 2000). Moreover, NDVI data are useful to identify land cover categories through the seasonal variation of greenness (Loveland et al, 1991 and Cihlar et al. 1996). In biophysical remote sensing, greenness can be measured in terms of the normalized different vegetation index (NDVI) that uses radiances or reflectances from a red channel around 0.66 and a near-IR channel around 0.86µm (Lo and Faber, 2000).
A great deal of collected environmental data, high spatial continuity, indicate that points that are closer in given direction display higher correlation values than those that are separated farther (Lin and Change, 2000). The above spatial structure analysis may be affected by aggregated data. Variography is usually performed by determine the estimated variogram of the data collected in time and space. Variogram provides a model of the spatial correlation of data within a statistical framework, including spatial and temporal covariance functions (Lin and Change, 2000). Not surprisingly, these models are defined in terms of the correlation between any two data points separated by either spatial or temporal distances. Variogram has been applied in many fields such as soil pollution, air pollution, hydrology, ecology, and remote sensing. These techniques have also recently been applied to characterize the spatial variability of pollutants and environmental monitoring.
This study performed isotropic and anisotropic experimental variograms in 12.5m, 25m, 37.5m, and 50m resolutions NDVI data from a SPOT image at three 0.1407 km2 different land-cover sites that are an almost pure grass, a mixed grass and shrub, and a broad forest within the Yang Ming Shan National Park in Taiwan to evaluate the impact of aggregation on the spatial structure at different land-cover site.
2. Materials and Methods
In order to analyze the aggregation effects on different resolutions the 12.5m, 25m, 37.5m, and 50m resolutions NDVI data calculated from the aggregated SPOT images at three different 0.1407 km2 land-cover sites that are an almost pure grass, a mixed grass and shrub, and a broad forest within the Yang Ming Shan National Park in Taiwan. These level 10 SPOT images were from the center for space and remote sensing research of the national central university in Taiwan. The three selected areas are displayed in Fig 1. The aggregated NDVI maps are displayed in Fig. 2. Moreover, the experimental variograms of NDVI data were calculated within GS+ (Gamma Design, 1995).
2.1 Greenness Index
High reflectance in the near-infrared part of the spectrum, together with chlorophyll absorption in the red wavelengths, is typical, green vegetation (Gates et al. 1965, O'neill, 1996). Vegetation areas will generally yield high value for either index because of their relatively high near-IR reflectance and low visible reflectance (Lillesand and Kiefer, 2000). The NDVI expresses the difference between the incident radiation reflected by photosynthetically active pigments in green leaves, and that portion reflected on the near-infrared part of the spectrum (Jelinski and Wu, 1996). NDVI is defined as:
where NIR and RED are wavelengths in the reflective infrared (~0.65-0.90µm) and red (~0.60-0.65µm) bandwidths, respectively (Quattrochi and Luvall, 1999). Thus NDVI is bounded ratio that varies between -0.1 and 0.1, with only active growing vegetation having positive values typically between 0.1 and 0.6 (Jelinski and Wu, 1996).
