Improving precision of biomass estimation in field and remote sensing-based forest monitoring - considering continuous horizontal biomass distributions

• Applicant: Professor Dr. Christoph Kleinn
• Subject Area: Forestry
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 496533645

Forest biomass is among the core variables in modern forest monitoring. Precision of estimation is an important feature in forest monitoring. Research on the assessment of forest biomass focusses largely on optimizing sampling designs and enhancing statistical biomass models from field measured tree data (allometric models) and from remote sensing data (for regionalization and model-based inference). We will look at options to increase precision of estimation by improving the per plot (fixed area plots) prediction of above ground biomass (AGB): we introduce a new approach that looks at the forest biomass as a variable with a continuous horizontal distribution so that the per-plot AGB prediction is the tree biomass which is strictly within the confinements of the plot perimeter. We call this the continuous approach (CA) to plot-biomass prediction - as opposed to the conventional discrete approach (DA), in which the total biomass of the discrete number of in-trees is taken as plot biomass - regardless how much of this biomass is truly above the plot area. In the CA, parts of in-trees that slop over the plot boundary are excluded while biomass fractions of out-trees that extend into the plot need to be considered. To make this approach operational, we introduce the individual tree horizontal biomass distribution (THBD) and derive its shape from empirical data and theoretical considerations. We use fully mapped stands to compare the statistical performance of the conventional DA and our newly proposed CA, where we wish to better understand the interactions between stand characteristics, plot size, plot shape and (the unavoidable) assumptions in modelling the THBD. Based on a pilot study we expect a gain in precision of estimation; we do also evaluate the cost issue as the CA requires including additional out-trees that have crown fractions within the confinements of the plot. There are two major novel elements of basic research in this proposal that had not been worked on before: (1) the derivation of the THBD, (2) the derivation of the stand-wise horizontal distribution of tree biomass. Using the CA to field plot biomass prediction, we hypothesize to increase precision of estimation from both (1) forest inventory field sampling and (2) from monitoring approaches that integrate field observed biomass with remotely sensed data. Analyses on both are a core element of the research plan of this proposal. Further applications of the THBD - not researched here - are seen in the context of modelling the development of forest stands and the spread of forest fires.

• DFG Programme: Research Grants