| This research utilized multipolarized and multiple incidence angle radar data to study the relationships between radar backscatter and forest cover types and stand characteristics. L-band multipolarized (i.e. HH, VV, VH, and HV) multiple incidence angle aircraft SAR (Synthetic Aperture Radar) data were used in conjunction with computer-aided analysis techniques to differentiate forest and land cover types, and also were analyzed in relation to various forest stand parameters (i.e. age, height, DBH, basal area, stand volume, number of trees per acre, and stand biomass). Then, regression models using the radar backscatter to predict the forest stand parameters were developed.; The results showed that multipolarized and multiple incidence angle SAR data can differentiate between several forest and land cover types very effectively. Overall classification accuracies of 95% were obtained when the SAR data were processed through a low pass 7 x 7 mean spatial filter and a perpoint classifier was applied. The results indicate that there is a strong statistically significant relationship between HV-polarized radar backscatter and several forest stand parameters (e.g. the correlation coefficients comparing radar backscatter with age, DBH, basal area, height, cords/acre, trees/acre, and stand biomass were 0.93, 0.94, 0.90, 0.83, 0.91. {dollar}-{dollar}0.81, and 0.97 respectively). The results of this research also indicate that forest stand parameters can be estimated from radar backscatter using regression models with a reasonable degree of accuracy. Therefore, simulation may allow efficient estimates of the Total Biomass (T{dollar}sb{lcub}rm B{rcub}{dollar}) on a regional scale when regression models are combined with an appropriate inventory design. A comparison between these results obtained with multipolarized aircraft data and HH-polarized SIR-B (Shuttle Imaging Radar-B) data obtained five weeks later over the same study site indicated that if the SIR-B data had been collected using any of the polarizations having a vertical component (i.e., VV, HV, or VH), the SIR-B data would probably have had even better relationships with the forest stand parameters than had been found. |
