The concept of a Global Triangle Model developed from AVHRR data
Jiang Li
Arizona Remote Sensing Center, University of Arizona, USA
Abstract
A global triangle model is proposed based on the characteristics of the data structure observed in spectral space. The research hypothesis is that a triangle shaped spatial arrangement of data can be observed in two dimensional spectral space as long as three conditions are met : (1) a; “Temperature” Channel and a “vegetation channel are used to produce the scatter graph, (2) the image is large enough to include a variety of landform and vegetation types, and (3) the sensors are radio metrically calibrated for vegetation, soils, water bodies, and various earth surface materials. A global triangle model is hereby derived to simulate the amount of green vegetation and the surface condition corresponding to its thermal, or ecological environment. The three corners of conditions corresponding to its thermal, or ecological environment. The three corners of the global triangle are arid terrain, full vegetation, ad deep water. Every site on the earth surface is located somewhere within the triangle according to its thermal, moisture and biomass conditions. The model could be used to help to understand how a basic eco-system is organized and operates. The triangular relationship could also be used for broad vegetation class discrimination. Temporal comparison of the triangle models at the same location may provide a mean of change detection.
Introduction
Technological advancement in remote sensing has given us tool to study the earth as a system. We can now gain comprehensive knowledge, not only of separate components, but also of the interrelation and interaction between them. This study proposes a global triangle model based on the characteristics of the data structure observed in spectral space of remotely sensed data. This model is derived to synthesize surface components and correlate earth system dynamics. The development of the global triangle model is an attempt to examine vegetation and terrain in relation to their ecological and terrain in relation to their ecological environment, and better understand the surface of the earth as an integrated system.
Vegetation and Terrain Studies in Remote Sensing
The spectral band between 0.63 and 0.69mm is known as the chlorophyll absorption band within this spectral range, the soil radiance is at a maximum and vegetation response is at a minimum. This indicates that a band within the red portion o the visible spectrum is a sensitive indicator of green vegetation. Vegetation reflectance increases rapidly and becomes significantly higher than soil reflectance in the near infrared (NIR) region between 0.75 to 1.2mm. Thus, the NIr band can also be used to distinguish vegetation from the soil background Tucker, 1978). The ratio of the NIR band to the red, first defined by Jordan (1966), yields an index that is highly sensitive to band to the red, first defined by Jordan (966), yields an index that is highly sensitive to green vegetation. Deering (1975), proposed the Normalized Difference Vegetation Index (NDVI) : (NIR – Red) / (NIR + Red.). These and other vegetation parameters including leaf are index (LAI), biomass, percentage of vegetation cover and productivity (Tucker, 1979). Another spectral
wavelength region used for vegetation and soil studies is the thermal infrared (TIR). Healthy plants are in energy equilibrium with their thermal environment. Their temperature and water status are adjusted constantly in a way best adaptable to environment changes. TIR measurements can indicate differences in vegetation type, cover, turgidity, and morphology s well as background soil moisture and other physical properties (Jackson 1977). The Canopy Temperature Variability Index (CTV) was infrared thermometer sensed canopy temperature within a field. Gardener et al. (1981) found the standard deviation of mid – day canopy temperature is an useful indicator for irrigation scheduling. The aim of studies using thermal indices is to obtain insight into the physiologic condition of plant relation to their surrounding environment, rather than quantification of vegetation biomass.
