Design-based approaches for the spatial prediction of stand attributes under forest inventory perspectives

The use of remotely sensed data for forest inventory and monitoring of natural resources is ever increasing. Distinctively, remotely sensed data, integrated with ancillary data, can be exploited for the spatialization of biophysical attributes measured by forest inventories or management plans. Such applications are based on the relationships between the considered attributes and the information measured from remotely sensed platforms. As part of the activities of the FRESh LIFE project "Demonstrating Remote Sensing Integration in sustainable forest management", this paper compares some techniques for the quantification of forest resources that exploit the integration of inventoried methods with information layers obtained by remote sensing, with particular reference to Airborne Lidar data. This contribution focuses in particular on the results obtained in the study area located in Rincine, Tuscany. Following a Tessellation Stratified Sampling approach (TSS), which allows a spatially balanced sampling, the area has been sectioned in about 5,500 squares of 23 m in side (529 m2). Subsequently, by One Per Stratum Stratified sampling technique (OPSS), 50 squares were selected: diameter at breast height, total tree height and species of each live tree were measured. For each square, 30 indices (Lidar metrics) derived from the normalized Lidar point cloud were calculated. Four different techniques to estimate growing stock and total biomass volume based on sampling design and integration with Airborne Lidar data are here presented.