The use of remotely sensed data in sampling designs for forest monitoring

Technology development has led to a large availability of increasingly precise remotely sensed data ready-to-use, but several countries’ forest monitoring programs are still based on the traditional systematic sampling design of National Forest Inventories (NFIs). It is well known that, in order to improve surveys estimates, auxiliary data can be used both in the design phase and in the estimation phase. Recent literature has presented some proposals of using remote sensing (RS) data to improve NFIs but all are limited to specific countries or areas. Our aim is to investigate how RS data can be exploited to produce global forest estimates in a more cost-efficiently way. We assess the use of a global Landsat-based cloud/noise free Best Available Pixel (BAP) composite image in the design phase in order to produce reliable estimates of the biomass and soil carbon density.