Land cover Mapping by Combining Satellite Estimated NDVI and Surface Temperature
V. Saravanapavan, T. Saravanapavan and R. Shibasaki
Institute of Industrial Science, University of Tokyo
7-22-1 Roppongi, Minato-Ku, Tokyo 106, Japan
Tel: (81)-3-3402-6231 Fax: (81)-3-3479-2762
E-mail:vasu@shunji.iis.u-tokyo.ac.jp
Abstract
Accurate land cover mapping, which provides information on the distribution of vegetation on earth surface , is essential for monitoring and maintaining the natural resources of the earth. Usage of satellite remote sensing is becoming a potential tool as it describes the spatial and temporal variation prominently. The conventional method o0f classification by employing Vegetation Indices has some uncertainties, especially in discriminating the active agricultural land form forest in the southeast Asia. Since satellite observed surface temperature measurements are the additive compositions of TIR from background soils and overlying vegetation canopy and the NDVI measurements is and estimate of the vegetation cover, we employ the combination of surface temperature and NDVI in land cover mapping. Our results result reveal appreciable improvement in classifying land cover by Surface temperature and NDVI combination and we produce classified map of southeast Asia.
Introduction
Fast development occurring in Southeast Asia causes higher demand for agricultural development and urbanization and food. Therefore, monitoring which can be achieved by land cover mapping, and maintaining of natural resources are becoming an essential issue. Time series of land cover map for southeast Asia are essential for land cover change and its change analysis especially to determine the change in natural resources. Land cover mapping can be carried out by using ground measured information. Since the ground measured information are time, money and labor consuming and suitable for small, areas remote sensing has the potential to map large area of land cover .
Vegetation index data derived from the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer is the main feature for land cover change in broad scale (Lambin 1994 a). Eventhough the NDVI is used for identifying and separating vegetation type there are some limitation to the NDVI s a discriminant of vegetation type, particularly for high biomass. Its is likely that if physiognimically different but phenologicaly same vegetation type are considered together. Therefore, by employing NDVI only it sis difficult to differentiate some active agricultural vegetation form forest. Since surface temperature provides the information on back ground soil and vegetation and NDVI measures the information of vegetation, the combination provides more information on land cover. Lambin (1996) used the ratio of surface temperature and NDVI in order to separate the weed land form forest in Africa.
Background
A good understanding of surface temperature and NDVI is essential when land cover is classified by using these two parameters. A negative relation is observed between vegetation indices and surface temperature ( Goward et al, 1985, Price 1990). Te basis for this relationship lies in the unique spectral reflectance- emittance properties of plant relative to soil. Thus vegetated surface show low surface temperature than soil . surface temperature is the function of evapotranspiration. Green biomass increment is often associated with the reduction in surface resistance to evapotranspiration and greater transpiration and a larger latent heat transfer. The presence of transpiring vegetation increases the share of latent heat flux with regard to total available energy.
The loses due to evapotranspiration by vegetation depends on stomatal resistance, canopy height and structure, rooting depth of plants. Moreover surface resistance is influenced by amount and type of vegetation an soil characteristics 9 Rosenberg et al, 1983). Therefore different types of vegetation differ in evapotranspiration. Lambin (1996) used surface temperature and NDVI ratio for land cover classification in Africa since NDVI and surface temperature are having correlation regarding biome.
Data and methodology
The National Oceanic and Atmospheric Administration (NOAA) Advanced Very High resolution Radiometer (AVHRR) data was used in this study. NDVI ( Normalized Difference Vegetation Index ) data an channel 4 and channel 5 data were collected form EROS Data Center for the study area covering southeast Asia form April 1992 to March 1993. surface temperature was obtained. NDVI, surface temperature was calculated based on Price formula 9198
3, 1984) and monthly composite data were obtained . NDVI, surface temperature and the ration of surface temperature were used separately as a measure for the classification . unsupervised classification was performed since there was insufficient information on the study area. Land use map of Thailand, 1990 was used for the validation.
