|DOI / URL||link|
|Title (Primary)||An analysis of forest biomass sampling strategies across scales|
|Author||Hetzer, J.; Huth, A.; Wiegand, T.; Dobner, H.-J.; Fischer, R.;|
|POF III (all)||T11; T53;|
|Abstract||Tropical forests play an important role in the global carbon cycle as
they store a large amount of carbon in their biomass. To estimate the
mean biomass of a forested landscape, sample plots are often used,
assuming that the biomass of these plots represents the biomass of the
In this study, we investigated the conditions under which a limited number of sample plots conform to this assumption. Therefore, the minimum number of sample sizes for predicting the mean biomass of tropical forest landscapes was determined by combining statistical methods with simulations of sampling strategies. We examined forest biomass maps of Barro Colorado Island (50 ha), Panama (50 000 km2), and South America, Africa, and Southeast Asia (3 × 106–11 × 106 km2).
The results showed that around 100 plots (1–25 ha each) are necessary for continent-wide biomass estimations if the sampled plots are randomly distributed. However, locations of current inventory plots often do not meet this requirement, for example, as their sampling design is based on spatial transects among climatic gradients. We show that these nonrandom locations lead to a much higher sampling intensity being required (up to 54 000 plots for accurate biomass estimates for South America). The number of sample plots needed can be reduced using large distances (5 km) between the plots within transects.
We also applied novel point pattern reconstruction methods to account for aggregation of inventory plots in known forest plot networks. The results implied that current plot networks can have clustered structures that reduce the accuracy of large-scale estimates of forest biomass if no further statistical approach is applied. To establish more reliable biomass predictions across South American tropical forests, we recommend more spatially randomly distributed inventory plots (minimum: 100 plots) and ensuring that the analyses of inventory plot data consider their spatial characteristics. The precision of forest attribute estimates depends on the sampling intensity and strategy.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=22966|
|Hetzer, J., Huth, A., Wiegand, T., Dobner, H.-J., Fischer, R. (2020):
An analysis of forest biomass sampling strategies across scales
Biogeosciences 17 (6), 1673 - 1683