Abstract
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 surrounding forest. 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.
Highlights
For a better understanding of the global carbon cycle, reliable estimations of aboveground biomass (AGB) in vegetation have become increasingly important (Broich et al, 2009; Malhi et al, 2006; Marvin et al, 2014), especially for tropical forests, as they store more carbon in biomass than any other terrestrial ecosystem (Pan et al, 2011)
We present a novel simulation approach for determining the number of plots necessary across scales, answering the following questions: (i) how many sample plots are necessary for forest biomass estimations in South America, and what is the role of the sampling strategy? (ii) What is the influence of scale on the sampling design?
We focus on three forest biomass datasets for the South American tropical region covering different scales (Fig. 1)
Summary
For a better understanding of the global carbon cycle, reliable estimations of aboveground biomass (AGB) in vegetation have become increasingly important (Broich et al, 2009; Malhi et al, 2006; Marvin et al, 2014), especially for tropical forests, as they store more carbon in biomass than any other terrestrial ecosystem (Pan et al, 2011). Current biomass mapping approaches are based on forest field inventory plots (e.g., Chave et al, 2003; Lewis et al, 2004; Malhi et al, 2006; Mitchard et al, 2014) or remote sensing measurements (e.g., Asner et al, 2013; Avitabile et al, 2016; Baccini et al, 2012; Saatchi et al, 2015) and involve statistical approaches (e.g., Malhi et al, 2006) or vegetation modeling (e.g., Rödig et al, 2017). Remote-sensing-derived maps have a typical spatial resolution of 100–1000 m and capture the biomass of large landscapes or even entire continents (Asner et al, 2013; Avitabile et al, 2016; Baccini et al, 2012; Saatchi et al, 2011).
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