A compatible system for estimation of individual tree volume was developed for Scots pine (Pinus sylvestris L.) in northwestern Spain. The system comprises a merchantable volume equation, a total volume equation, and a taper function. The use of the volume equation allows rapid estimation of tree volume, and stand volume by summing individual tree volumes, which is equal to the volume obtained by integrating the taper equation. The volume equation is very easy to use and is therefore preferred when classification of the products by merchantable sizes is not required. Data from 228 destructively sampled trees were used for model development. Fourteen compatible volume equations were evaluated, 13 of these equations were taken from the available literature, and the other was developed in the present study. A modified second-order continuous autoregressive error structure was used to correct the autocorrelation of the hierarchical data used. The model developed by Fang et al. (Z. Fang, B.E. Borders, and R.L. Bailey. 2000. For. Sci. 46: 1–12) best described the data. There model is therefore recommended for the estimation of diameter at a specific height, merchantable volume, and total volume of Scots pine stems in the area of study.

Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth’s surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R² = 0.71 and RMSE = 33.5 Mg·ha⁻¹; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.