APPLICATION OF HIGH-RESOLUTION THERMAL INFRARED REMOTE-SENSING AND GIS TO ASSESS THE URBAN HEAT-ISLAND EFFECT

Day and night airborne thermal infrared image data at 5 m spatial reso lution acquired with the 15-channel (0 . 45 mu m-12 . 2 mu m) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signat ures of urban land cover types between day and night. Thermal channel number 13 (9 . 60 mu m-10 . 2 mu m) data with the best noise-equivalen t temperature change (NE Delta T) of 0 . 25 degrees C after atmospheri c corrections and temperature calibration were selected for use in thi s analysis. This research also examined the relation between land cove r irradiance and vegetation amount, using the Normalized Difference Ve getation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0 . 60-0 . 63 mu m) and reflected infra red (channel number 6: 0 . 76-0 . 90 mu m) ATLAS data. Based on the me an radiance values, standard deviations, and NDVI extracted from 351 p airs of polygons of day and night channel number 13 images for the cit y of Huntsville, a spatial model of warming and cooling characteristic s of commercial, residential, agricultural, vegetation, and water feat ures was developed using a GIS approach. There is a strong negative co rrelation between NDVI and irradiance of residential, agricultural and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential u ses associated with varying degrees of tree cover showed great contras ts with commercial and services land cover types in the centre of the city, and favours the development of urban heat islands. The high-reso lution thermal infrared images match the complexity of the urban envir onment, and are capable of characterizing accurately the urban land co ver types for the spatial modeling of the urban heat island effect usi ng a GIS approach.