Sampling strategies for estimating forest cover from remote sensing-based two-stage inventories

Remote sensing-based inventories are essential in estimating forest cover in tropical and subtropical countries, where ground inventories cannot be performed periodically at a large scale owing to high costs and forest inaccessibility (e.g. REDD projects) and are mandatory for constructing historical records that can be used as forest cover baselines. Given the conditions of such inventories, the survey area is partitioned into a grid of imagery segments of pre-fixed size where the proportion of forest cover can be measured within segments using a combination of unsupervised (automated or semi-automated) classification of satellite imagery and manual (i.e. visual on-screen) enhancements. Because visual on-screen operations are time expensive procedures, manual classification can be performed only for a sample of imagery segments selected at a first stage, while forest cover within each selected segment is estimated at a second stage from a sample of pixels selected within the segment. Because forest cover data arising from unsupervised satellite imagery classification may be freely available (e.g. Landsat imagery) over the entire survey area (wall-to-wall data) and are likely to be good proxies of manually classified cover data (sample data), they can be adopted as suitable auxiliary information.