Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery

Abstract: Remote sensing techniques have been seen as valuable and low-cost tools for frequent forest inventory purposes. However, estimation errors of relevant forest structure variables remain too high for operational use of high spatial resolution satellite imagery, such as Landsat TM/ETM and SPOT HRV, in temperate forests. Very high spatial resolution images that have been acquired by new commercial satellites, such as IKONOS-2 or QuickBird, are expected to reduce estimation errors to a level that is acceptable by foresters. This study assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables—age, top height, circumference, stand density and basal area—in even-aged common spruce stands. They were estimated on the basis of texture features that were derived from the grey-level co-occurrence matrix (GLCM). The coefficients of determination, R 2, of the best models ranged from 0.76 to 0.82 for top height, circumference, stand density and age variables. Basal area was found to be weakly correlated to texture variables (R 2 =0.35). Relative prediction errors of four out of the five studied forest variables were comparable to the usual sampling inventory errors (top height: 10%; circumference: 15%; basal area: 16%; age: 18%), but the stand density estimation error (29%) remained too high for use in forest planning. The sensitivity analysis to the GLCM parameters showed that the most important parameters were the texture feature, the displacement and the window size. The orientation parameter had minimal effects on the R 2 values, even if it influenced the values of the texture features. [Copyright &y& Elsevier]