Boreal forests constitute the world's largest terrestrial carbon pools. The main natural disturbance in these forests is wildfire, which modifies the carbon budget and atmosphere, directly and indirectly. Wildfire emissions in Russia contribute substantially to the global carbon cycle and have potentially important feedbacks to changing climate. Published estimates of carbon emissions from fires in Russian boreal forests vary greatly depending on the methods and data sets used. We examined various fire and vegetation products used to estimate wildfire emissions for Siberia.

There is a growing demand for improving the measurement of forest resources, with more frequent updating and better information on environmental variables. We explore the cost efficiency of a stratified two-stage design using area sampling to estimate the forest plantation and native forest areas in southern Chile. Analytical expressions for the approximate mean square error of combined and separate ratio estimators are derived applying Taylor linearization.

Modern ecological models often account for the influence of the surrounding environment by using landscape pattern indices (LPIs) as measures of landscape structure. Ideally, the landscape samples from which these LPIs are extracted should be centered on the locations where the response variable was measured. However, in situations where this is not possible due to a lack of adequate full-coverage landcover data, the question arises as to what degree this circumstance creates a bias in the value of the LPIs, thereby obscuring their relation with the response variable.