Estimation of tree canopy cover in evergreen oak woodlands using remote sensing

The montado/dehesa landscapes of the Iberian Peninsula are savannah-type open woodlands dominated by evergreen oak species (Quercus suber L. and Q. ilex ssp. rotundifolia). Scattered trees stand over an undergrowth of shrubs or herbaceous plants. To partition leaf area index between trees and the herbaceous/shrubby understorey requires good estimates of tree canopy cover and is of key importance to understand the ecology and the changes in land cover. The two vegetation components differ in phenology as well as in radiation and rainfall interception, water and CO2 fluxes. The main goal of this study was to estimate tree canopy cover in a montado/dehesa region of southern Portugal (Alentejo) using remote sensed data. For this purpose we developed empirical models combining measurements obtained through the analysis of aerial photos and reflectance from Landsat Thematic Mapper (TM) individual channels, vegetation indices, and the components of the Kauth-Thomas (K-T) transformation. A set of 142 plots was designed, both in the aerial photos and in the satellite data. Several simple and multiple linear regression models were adjusted and validated. A subset of 75% of the data (n = 106) was used for model fitting, and the remainder (n = 36) was used for model assessment. The best linear equation includes Landsat TM channels 3,4, 5 and 7 (r(2) = 0.74), but the Normalised Difference Vegetation Index (NDVI), the components of the K-T transformation, and the Atmospherically Resistant Vegetation Index (ARVI) also performed well (r(2) = 0.72, 0.70, and 0.69, respectively). The statistics of prediction residuals and tests of model validation indicates that these were also the models with better predictive capability. These results show that detection of low/medium tree canopy cover in this type of land cover (i.e. evergreen oak woodlands) can be accomplished with the help of high and medium spatial resolution satellite imagery. (c) 2005 Elsevier B.V. All rights reserved.