Model-assisted estimation of change in forest biomass over an 11 year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide “activity data”

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