MSc thesis topic: Impact of the spatial configuration of forest inventory plots for integration with large-area biomass maps

At the same time, some studies already address the benefits of using such maps, as an auxiliary source of information to national forest inventories (NFIs), for increasing the precision of (sub)national aboveground biomass (AGB) estimates through the application of model-assisted estimators (Næsset et al., 2020; Málaga et al., in. prep). Still, some harmonization challenges arise from the integration of remote sensing-based products with ground-based information. One of which being the very different spatial support of NFI’s plots (most NFI’s plots are smaller than 0.5 ha) compared to large-are biomass maps (~ 1 ha), which may result in large uncertainties in map-to-plot intercomparisons (Næsset et al., 2015; Réjou-Méchain et al., 2019). Furthermore, the best configuration of (sub) plots within larger blocks (Tomppo et al., 2014; Yim et al., 2015) is a yet unsolved problem.

In this research, you will use a geo-statistical approach to simulate the within-pixel AGB distribution of large-area biomass maps to assess different plot spatial configurations.

Marked point process have been used in the past to model forest attributes (i.e. growth stock volume) at the stand level and understanding its within stand distribution (Penttinen and Stoyan, 2000; Gavrikov and Stoyan, 1995). These studies are based on tree-level information; tree location information is related to the point processes whereas tree characteristics (i.e. tree diameter at breast height, tree height, tree AGB) are associated to the “marks”. To the best of our knowledge, there are no studies assessing plot spatial configuration to support map-to-plot intercomparisons.

Relevance to research/projects

This study contributes to the work on how biomass maps can enhance (sub)national AGB estimates for land use, land use change and forestry (LULUCF) carbon monitoring and reporting purposes

Objectives

• Simulate the tree AGB distribution within a large-area map pixel/polygon using a geo-statistical approach (i.e. marked point process)
• Assess different plot configurations within the AGB map pixels/polygons
• Estimate model-assisted AGB for different plot configurations within the pixel/polygons

Literature

• Gavrikov, V., Stoyan, D., 1995. The use of marked point processes in ecological and environmental forest studies. Environ Ecol Stat 2, 331–344.
• Næsset, E., Bollandsås, O.M., Gobakken, T., Solberg, S., McRoberts, R.E., 2015. The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data. Remote Sensing of Environment 168, 252–264.
• Næsset, E., McRoberts, R.E., Pekkarinen, A., Saatchi, S., Santoro, M., Trier, Ø.D., Zahabu, E., Gobakken, T., 2020. Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania. International Journal of Applied Earth Observation and Geoinformation 89, 102109.
• Penttinen, A., Stoyan, D., 2000. Recent applications of point process methods in forestry statistics. Statistical Science 15, 61–78.
• Réjou-Méchain, M., Barbier, N., Couteron, P., Ploton, P., Vincent, G., Herold, M., Mermoz, S., Saatchi, S., Chave, J., de Boissieu, F., Féret, J.-B., Takoudjou, S.M., Pélissier, R., 2019. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them. Surv Geophys 40, 881–911.
• Santoro, M., 2020. CCI Biomass Product User Guide v2.
• Tomppo, E., Malimbwi, R., Katila, M., Mäkisara, K., Henttonen, H.M., Chamuya, N., Zahabu, E., Otieno, J., 2014. A sampling design for a large area forest inventory: case Tanzania. Can. J. For. Res. 44, 931–948.
• Yim, J.-S., Shin, M.-Y., Son, Y., Kleinn, C., 2015. Cluster plot optimization for a large area forest resource inventory in Korea. Forest Science and Technology 11, 139–146.

Requirements

• Knowledge of geostatistics, e.g. acquired in the course GRS30306.

Theme(s): Modelling & visualisation; Integrated Land Monitoring