Capatility of Alos-Palsar-2 Radar Quad Polarization Data for Estimation of Some Structural Quantitative Characteristics of Planted Forest, Arabdagh region, Iran

Estimating forest attributes is essential for understanding the condition and function of the forest to be applied in forest planning and management. The purpose of this study was to estimate the structural attributes of conifer-dominated plantations using radio detection and ranging (RADAR) polarimetric data and nonparametric algorithms in the Arabdagh region of Golestan province. Field-based structural attributes were collected from 319 circular plots with 400 m2 areas designed within a random cluster method. Within each plot, diameter at breast height (for all trees) and height (for some trees) were measured. The precise position of plots ere also recorded. Then stand volume, basal area, and the number of stem per ha were calculated. The required preprocessing and processing were conducted on raw RADAR data, followed by the extraction of plot-based values from the derived indices. Model training was done on 75% of plots using random forest, support vector machine, and K nearest neighbor algorithms. Results were validated with the remaining 25% of the plots. The results showed the lowest Root Mean Square Error and Bias for Random Forest algorithm for basal area 50.62% and -1.7%, respectively. Moreover, the support vector machine model achieved 58.82% and -7.94% for volume as well as 52.07% and -5.1% for no. of trees per hectare. As a whole, this study showed that the full polarization PALSAR-2 data has a moderate ability to estimate the quantitative structural attributes due to the high amplitude of changes in the quantitative forest characteristics.