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Remote sensors can be used as a robust and effective means of monitoring isolated or inaccessible forest sites. In the present study, the multivariate adaptive regression splines (MARS) technique was successfully applied to remotely sensed data collected by the Landsat-8 satellite to estimate mean diameter at breast height (R2 = 0.73), mean crown cover (R2 = 0.55), mean volume (R2 = 0.57) and total volume per plot (R2 = 0.41) in the forest monitoring sites. However, the spectral data yielded poor estimates of tree number per plot (R2 = 0.22), the mean height (R2 = 0.25) and the mean diameter at base (R2 = 0.38). Seven spectral bands (band 1 to band 7), six vegetation indexes and other derived parameters (NDVI, SAVI, LAI, FPAR. ALB and ASR) and eight terrain variables derived from the digital elevation model (elevation, slope, aspect, plan curvature, profile curvature, transformed aspect, terrain shape index and wetness index) were used as predictors in the fitted models. To prevent over-parameterization only some of the predictor variables considered were included in each model. The results indicate the MARS technique is potentially suitable for estimating dasometric variables from using spectral data obtained by the Landsat-8 OLI sensor.

Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico