Handling single-year big data in multi-temporal forest inventory and mapping systems

Abstract:

Data assimilation (DA) has been applied for several decades in areas such as meteorology and robotics, to predict the state of systems that evolve over time, by integrating model-based forecasting with repeated observations. Recently, DA has gained attention in forest inventory applications. For instance, study by Nyström et al. (2015) not only demonstrated the theoretical potential of employing dense time series of remotely sensed (RS) data but also identified several obstacles that must be overcome before the methodology can be practically adopted. Within the SmartForest project, we are further exploring the usefulness of DA techniques for forest inventory and mapping of forest attributes.

Recent studies have shown that DA has a potential to maintain the accuracy of plot and stand level information, obtained from accurate but expensive surveys, such as airborne laser scanning (ALS), by making use of inexpensive optical satellite data and DA throughout several subsequent years. However, with ever-increasing amounts of RS data, it is important to evaluate not only how to make assessments and growth updates through DA, but also how to best utilize huge amounts of RS data from within single years. For example, the European Space Agency’s Sentinel-2 satellites currently provide new data across boreal forests every second week.

In a study initiated within the Norwegian SmartForest programme, we evaluate whether building separate models for each RS dataset and applying composite estimation or merging all data into a single model through principles of partial least squares regression and random forest non-parametric regression, yields the best results in terms of prediction accuracy.

Our investigation was conducted within the Våler municipality of Norway and focused on growing stock volume as our primary target variable. The RS data were acquired in 2022 and included ALS point clouds, digital aerial photogrammetric point clouds, and Sentinel-2 spectral data. Alongside comparing prediction accuracies, we conducted a qualitative assessment to discern the practical advantages and disadvantage of each method in integrating them into a multi-temporal data DA system.

Reference:

Nyström, M., Lindgren, N., Wallerman, J., Grafström, A., Muszta, A., Nyström, K., Bohlin, J., Willén, E., Fransson, J.E., Ehlers, S. and Olsson, H., 2015. Data assimilation in forest inventory: first empirical results. Forests, 6(12), pp.4540-4557.