ESTIMATING URBAN LEAF AREA USING FIELD MEASUREMENTS AND SATELLITE REMOTE SENSING DATA
Ryan R. Jensen1 and Perry J. Hardin
Abstract: Accurate estimation of urban leaf area is important in understanding the urban forest's role in heat island mitigation, pollution removal, and carbon sequestration. Remotely sensed satellite data provide an alternative method to inexpensively and nondestructively estimate this important urban biophysical variable. Ceptometer measurements of leaf area index (LAI) at 143 urban sites in Terre Haute, Indiana, U.S., were modeled as a function of reflected radiance flux sensed by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Multiple regression models of LAI were compared to estimates produced by feed-forward back-propagation artificial neural networks. The most accurate estimation was produced by the neural network utilizing the ASTER green band and the ratio of the ASTER red and near-infrared bands. In this case, the simple correlation between the observed and predicted LAI values was moderately high (R = 0.71). The standard error of the LAI estimate was 1.35. In every case, the predictive accuracy of the neural network models exceeded the multiple regression models. Examination of the parameters in the successful models indicates that the estimation of urban LAI in Terre Haute is physically predicated on the relative proportions of leaf chlorophyll, leaf spongy mesophyll, and indurate matter (e.g., concrete, asphalt, soil) constituting the individual picture elements of the satellite image.
Keywords: Leaf area; remote sensing; ceptometer; leaf area index.