Post-classification approaches to estimating change in forest area using remotely sensed auxiliary data

Abstract

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of classifications. In particular, when rates of change are small and equal probability sampling is used, observations of change may be scarce. For these situations, post-classification approaches may be the only viable alternative. The study focused on model-assisted and model-based approaches to inference for post-classification estimation of gross afforestation, gross deforestation, and net deforestation using Landsat imagery as auxiliary data. Emphasis was placed on estimation of variances to support construction of statistical confidence intervals for estimates. Both analytical and bootstrap approaches to variance estimation were used. For a study area in Minnesota, USA, estimates of net deforestation were not statistically significantly different from zero.