Spatially explicit estimation of forest age by integrating remotely sensed data and inverse yield modeling techniques

Abstract

In this work we present an innovative method based on the application of inverse yield models for producing spatially explicit estimations of forest age. Firstly, a raster growing stock volume map was produced using the non-parametric k-Nearest Neighbors estimation method on the basis of IRS LISS-III remotely sensed imagery and field data collected in the framework of a local forest inventory. Secondly, species specific inverted yield equations were applied to estimate forest age as a function of growing stock volume. The method was tested in 128.000 ha of even-aged forests in central Italy (Molise region). The accuracy of the method was assessed using an independent dataset of 305 units from a local standwise forest inventory. The results demonstrated that the forest age map was accurate, with a root mean square error of 15.8 years (30% of the mean of field values), thus at least useful for supporting forest management purposes, such as the assessment of harvesting potential, and of ecosystem services. Thanks to the use of remotely sensed data and spatial modeling, the approach we propose is cost-effective and easily replicable for vast regions.

Keywords

• k-Nearest Neighbors
• Mapping
• Forest Inventory
• Growing Stock
• IRS LISS-III