A sampling design strategy to reduce survey costs in forest monitoring

Abstract

High-quality data and long-term time series are the basis of any research activity dealing with natural resources analysis. Adequate sampling designs are fundamental to allow a robust statistical analysis to be representative of a relevant set of target variables. In this work, the sampling strategy of ICP-Forests Level II European network has been proposed to define more efficient and cost-effective procedures under the hypothesis that the average value of single-tree growth (increment) is a proxy of forest health. ICP plots have a fixed spatial structure consisting of a square of 50×50m framed into 25 squared sub-plots. To estimate basal area (G) and increase over time (ΔG), two different sub-sampling methods have been implemented based on a measure of (i) the dominant layer only (i.e. a subset of the highest trees in the plot), and (ii) a random sample of squared sub-plots. While the vertical sampling procedure was performed using a progressive threshold, the horizontal sampling followed a bootstrapping procedure with random extraction without replacement. The mean absolute relative error (MARE) was used to evaluate quality of the two sub-sampling methods. Results highlighted a low predictive power with both methodologies, preventing the possibility to reduce the sampling efforts when estimating ΔG directly. In this context, the vertical sampling was strictly related to species-specific ecology, spatial structure and forest age, being influenced by vertical distribution of trees. The use of horizontal sampling for direct ΔG estimation led to systematically high errors. However, the use of horizontal sampling for total G estimation and indirect estimation of ΔG may reveal as a more effective procedure for a coherent representation of horizontal distribution of trees. Estimate ΔG as the difference between G values at time t and t+Δt finally allows for a sensible reduction of costs with a controlled estimation error. An adequate level of MARE should be decided a-priori to select the number of sub-squares to be randomly sampled.