Adapting a global stratified random sample for regional estimation of forest cover change derived from satellite imagery

Abstract

A desirable feature of a global sampling design for estimating forest cover change based on satellite imagery is the ability to adapt the design to obtain precise regional estimates, where a region may be a country, state, province, or conservation area. A sampling design stratified by an auxiliary variable correlated with forest cover change has this adaptability. A global stratified random sample can be augmented by additional sample units within a region selected by the same stratified protocol and the resulting sample constitutes a stratified random sample of the region. Stratified sampling allows increasing the sample size in a region by a few to many additional sample units. The additional sample units can be effectively allocated to strata to reduce the standard errors of the regional estimates, even though these strata were not initially constructed for the objective of regional estimation. A complete coverage map of deforestation within the Brazilian Legal Amazon (BLA) is used as a population to evaluate precision of regional estimates obtained by augmenting a global stratified random sample. The standard errors of the regional estimates for the BLA and states within the BLA obtained from the augmented stratified design were generally smaller than those attained by simple random sampling and systematic sampling.

Mark Broich

Postdoctoral Research Fellow

Played a central research role on the Geospatial Analysis for Environmental Change team and had a key role in building the GAEC lab.