AERIAL IMAGE TEXTURE INFORMATION IN THE ESTIMATION OF NORTHERN DECIDUOUS AND MIXED WOOD FOREST LEAF-AREA INDEX (LAI)

Leaf area index (LAI) currently may be derived from remotely sensed da ta with limited accuracy. This research addresses the need for increas ed accuracy in the estimation of LAI through integration of texture to the relationship between LAI and vegetation indices. The inclusion of texture, which acts as a surrogate for forest structure, to the relat ionship between LAI and the normalized difference vegetation index (ND VI) increased the accuracy of modeled LAI estimates. First-order, seco nd-order, and a newly developed semivariance moment texture are assess ed in the relationship with LAI. The ability to increase the accuracy of LAI estimates was demonstrated over a range of forest species, dens ities, closures, tolerances, and successional regimes. Initial assessm ent of LAI from spectral response over the full range of stand types d emonstrated the need for stratification by stand type prior to analysi s. Stratification of the stands based upon species types yields an imp rovement in the regression relationships. For example, deciduous hardw ood stands, spanning an LAI range from approximate to 1.5 to 7, have a moderate initial bivariate relationship between LAI and NDVI at an r( 2) of 0.42. Inclusion of additional texture statistics to the multivar iate relationship between LAI and NDVI further increases the amount of variation accounted for, to an R-2 of 0.61, which represents an incre ase in ability to estimate hardwood forest LAI from remotely sensed im agery by approximately 20% with the inclusion of texture. Mixed forest stands, which are spectrally diverse, had an insignificant initial r( 2) of 0.01 between LAI and NDVI, which improved to a significant R-2 o f 0.44 with the inclusion of semivariance moment texture. (C) Elsevier Science Inc., 1998.