26 Jensen and Hardin: Estimating Urban Leaf Area can be used to answer important geographic questions by describing temporal and spatial LAI dynamics at landscape to regional scales (e.g., Jensen 2002). Of equal importance, this method- ology can help land managers, conservationists, and urban foresters formulate urban environ- mental policy that is empirically supported by inexpensive remotely sensed biophysical data. LITERATURE CITED Aber, J.D., and J.M. Melillo. 1991. Terrestrial Ecosystems. Saunders College Publishing, Chicago, IL. Barclay, H.J. 1998. Conversion of total leaf area to projected leaf area in lodgepole pine and Douglas-fir. Tree Physiol. 18:185–193. Benediktsson, J.A., P.H. Swain, and O.K. Ersoy. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geosci. Remote Sensing 28:540–551. Bischof, H., W. Schneider, and A.J. Pinz. 1992. Multispectral classification of Landsat images using neural networks. IEEE Trans. Geosci. Remote Sensing 30:482–490. Chason, J.W., D.D. Baldocchi, and M.A. Huston. 1991. A comparison of direct and indirect methods for estimating forest canopy leaf area. Agric. For. Meteorol. 57:107–128. Chen, J.M., and T.A. Black. 1992. Defining leaf-area index for non-flat leaves. Plant Cell Environ. 15:421–429. Chen, J.M., P.M. Rich, S.T. Gower, J.M. Norman, and S. Plummer. 1997. Leaf area index of boreal forests: Theory, techniques, and measurements. J. Geophys. Res. Atmos. 102:29429–29443. Ellsworth, D.S., and P.B. Reich. 1993. Canopy structure and vertical patterns of photosynthesis and related leaf traits in a deciduous forest. Oceologia 96:169–178. Fassnacht, K.S., S.T. Gower, M.D. MacKenzie, E.V. Nordheim, and T.M. Lillesand. 1997. Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper. Remote Sensing Environ. 61:229–245. Green, E.P., P.J. Mumby, A.J. Edwards, C.D. Clark, and A.C. Ellis. 1997. Estimating leaf area index of mangroves from satellite data. Aquatic Bot. 58:11–19. Gopal, S., and C.E. Woodcock. 1996. Remote sensing of forest change using artificial neural networks. IEEE Trans. Geosci. Remote Sensing. 34:398–404. Hardin, P.J. 2000. Neural networks versus nonparametric neighbor-based classifiers for semisupervised classification of Landsat Thematic Mapper imagery. Optical Eng. 39:1898–1908. ©2005 International Society of Arboriculture Figure 2. Estimated LAI map of the Terre Haute area computed using the artificial neural network and three ASTER bands. Lighter areas represent higher LAI values. Note the city center in the middle left of the image and Terre Haute International Airport in the middle right of the image. Haykin, S. 1994. Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ. Jensen, J.R. 2000. Remote Sensing of the Environment: An Earth Resources Perspective. Prentice Hall, Upper Saddle River, NJ. Jensen, R.R. 2002. Spatial and temporal leaf area index dynamics in a north central Florida, USA preserve. Geocarto Int. 17(4):45–52. Jensen, R.R., and M.W. Binford. 2004. Measurement and comparison of leaf area index estimators derived from satellite remote sensing techniques. Int. J. Remote Sensing 25:4251–4265. Jensen, R.R., J.R. Boulton, and B.T. Harper. 2003. The relationship between urban leaf area and household energy usage in Terre Haute, Indiana, U.S. J. Arboric. 29(4):226–230. Jensen, J.R., F. Qui, and M. Ji. 2000. Predictive modeling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. Int. J. Remote Sensing. 20:2805–2822. Larcher, W. 1975. Physiological Plant Ecology. Springer- Verlag, New York, NY. Lymburner, L., P.J. Beggs, and C.R. Jacobson. 2000. Estimation of canopy-average surface-specific leaf area using Landsat TM data. Photogram. Eng. Remote Sensing. 66:183–191.
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