The United Nations Collaborative Program on Reduced Emissions from Deforestation and Forest Degradation in Developing Countries (UN REDD) was launched with the aim of contributing to the development of capacity for reducing emissions from loss of forest carbon in developing countries. It is understood that REDD mechanisms must be supported by forest assessment programs that can monitor the carbon stocks by carbon pools and human activities. Reporting at a national level will be required but many countries are likely to benefit from more local monitoring programs within the countries as well, gauging the effects of national policies and local financial mechanisms aimed at reaching goals for emission control for the nation as a whole. Field-based forest sample surveys are typically used as support for national reporting purposes. However, monitoring within the countries will require huge investments in field surveys to provide reliable change estimates with high spatial and temporal resolution. Airborne scanning LiDAR has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass. Thus, in a recent study we aim to demonstrate how “wall-to-wall” LiDAR data can be used for change estimation. Estimators for areal changes of categories representing human activities such as “deforestation”, “degradation” and “untouched” were presented. Corresponding estimators for variance were also provided. Furthermore, it was shown how net change in biomass for the defined activity categories and for the entire area of interest can be estimated from a field sample survey with and without support of LiDAR remote sensing data and how the uncertainty can be quantified by corresponding variance estimates. In a case study in a small boreal forest area in southeastern Norway (852.6 ha) a probability sample of 176 field sample plots distributed according to a stratified systematic design was measured twice over an 11 year period. Corresponding multi-temporal scanning LiDAR data were also available. A multinomial logistic regression model was used to predict change category for every LiDAR grid cell in the area, and areal changes were estimated from the pure field sample and with the support of the LiDAR data applying model-assisted estimators. The standard errors of the areal change estimates were reduced by 43–75% by adding LiDAR data to the estimation. The change categories were used as post-strata in a subsequent estimation of net change in biomass. The standard errors of the biomass change estimates for the respective change categories were reduced by 18–84% compared to the pure field survey when using LiDAR data as auxiliary information in a model-assisted estimation procedure, which translates to a need for 1.5–38.7 times as many field plots when relying only on the field data. For the entire area of interest, the standard error of the overall net change in biomass was reduced by 57% compared to the uncertainty reported from the pure field survey.