In this contribution, we assessed the biomass and carbon stock of a post-fire area covered by a young oak coppice of Quercus pyrenaica Willd. associated with shrubs, mainly of Cistus laurifolius L. This area was burned during the fire event of Chequilla (Guadalajara, Spain) in 2012. Sentinel-2 imagery was used together with our own forest inventories in 2020 and machine learning methods to assess the total biomass of the area. The inventory includes plots of total dry weight ranging between 6 and 14 Mg·ha−1 with individuals up to 8 years old. Nonlinear, nonparametric Gaussian process regression methods were applied to link reflectance values from Sentinel-2 imagery with total shrub biomass. With a reduced inventory of only 32 plots covering 136 ha, the total biomass could be assessed with a root-mean-square error of 1.36 Mg·ha−1 and a bias of −0.04 Mg·ha−1, getting a relative error between 9.8% and 20.4% for the gathered biomass. This is a rather good estimation considering the little effort and time invested; thus, the suggested methodology is very suitable for forest monitoring and management.

Remote sensing and advanced statistical models have been used to assess actual biomass and to analyze the biomass growth in a mature forest of Pinus halepensis Mill. Biomass maps using Landsat-5 in 2011 and Landsat-8 imagery in 2017 and non-parametric models were achieved. Afterwards, a fast and affordable methodology has been developed to monitor the evolution depending on forest management or its abandonment. Significative statistical evidences have been found between two types of management, demonstrating that managed areas have bigger growth potential. This methodology spares the efforts of exhaustive inventories and encourage forest managers to maximize their forest allowances.