The Use of High-resolution Satellite Imagery in Forest Inventory: a Case of Hans Kanyinga-namibia

The present study investigated double sampling with regression estimators as a quest for efficiency and effectiveness in forest inventory in Namibian woodlands. Auxiliary data used were obtained from Standard QuickBird satellite scenes (phase 1) for Hans Kanyinga Community Forest from October and November 2004 covering an area of 12,107 hectares, amplified with terrestric data (phase 2) of 2002. The relationships between auxiliary and terrestric variables are described and prediction models were constructed. According to the results of the stepwise procedure with the Mallow’s Cp statistic as the selection criteria, photogrammetric stand density and a combination of the photogrammetric crown area with photogrammetric stand density were the best candidates for predicting the stand volume. The resulting volume model explains 56% of the variation. Photogrammetric stand density was found to be highly correlated to the terrestric stand density with the resulting model explaining 81% of the variation. Photogrammetric crown diameter was found to be correlated with the diameter at breast height measured from the plots which were assessed for spatial tree positions, which enabled the derivation of the diameter distribution. The diameter distribution model explains 43% of the variation. In addition, the actual tree positions were determined using the GPS and surveying techniques (polar positions) involving distance and bearings. GPS tree positions showed a considerable shift of up to 8.67 m. However, only the distance measurements of tress from the plot centre using the infield surveying methods were more reliable. Nevertheless, the influences of the tree positional errors are not of high concern for temporary based sample plots which are normally used in Namibian forest inventories. A reduction in inventory cost was found to be 24% i.e. N$25.79 to N$19.67 per hectare. The results of this study are valid for Kavango region or any other region with similar set of physical and climatic conditions, but caution must be exercised in implementing these results elsewhere under different physical and environmental conditions.