Addressing management practices of private forests by remote sensing and open data: A tentative procedure

The qualitative and quantitative knowledge of forestry stands is a fundamental requirement for their management and exploitation planning. Traditional field measurements, based on plots, are certainly accurate, but time-consuming and expensive; moreover, it cannot provide a wall-to-wall estimate of measures over large areas. Even though they are essential for calibrate and validate models and respectively results, remote sensing could be a useful support for forest planners when trying to describe wide areas. Optical images from medium-high resolution satellite missions are widely employed due to their accessibility, affordability and readiness to use. Within this framework, in this study an approach based on open-data from the Copernicus Sentinel-2 mission and Regional low density LiDAR data was developed in order to assess private forest wood resource in the Susa Valley (Piemonte Region, NW - Italy). The proposed methodology aims to support private forest management by the local forest consortium for basal area and wood volume estimation of forest stands. Specifically, the process was based on the jointly use of Multi-layer Perceptron (MLP) artificial neural network (ANN), trained and validated with respect to ground data from 285 surveyed plot. Furtherly, to refined wood volume estimates correcting factors accounting for slope and local stand fertility were applied. Estimates of volume stands were generated separately for conifers and broadleaves. Results prove that the adoption of correcting factors improved volume estimates by the MLP and ANN about 2% (relative mean absolute error) and 13% in conifer and broadleaf stands respectively.