A Monte Carlo appraisal of tree abundance and stand basal area estimation in forest inventories based on terrestrial laser scanning

• Published in: Canadian journal of forest research Vol. 49; no. 1; pp. 41 - 52
• Main Authors: Corona, P, Di Biase, R.M, Fattorini, L, D’Amati, M
• Format: Journal Article
• Language: English
• Published: Ottawa NRC Research Press 2019; Canadian Science Publishing NRC Research Press
• Subjects: Bias; Computer simulation; Density; distance sampling; détection par scanner laser terrestre (SLT); Estimators; forest inventory; Forests; hybrid inference; inférence hybride; Inventories; Inventory data; Observations; Optical radar; plot sampling; probability; Rayon; Sampling; simulation study; stand basal area; Technology application; Terrestrial ecosystems; Timber inventory; TLS-based detection; Trees; Vegetation mapping; échantillonnage de placettes; échantillonnage à distance; étude par simulation
• ISSN: 0045-5067; 1208-6037; 1208-6037
• DOI: 10.1139/cjfr-2017-0462

Non-detection of trees is an important issue when using single-scan TLS in forest inventories. A hybrid inference approach is adopted. Quoting from distance sampling, a detection function is assumed, so that the inclusion probability of each tree included within each plot can be determined. A simulation study is performed to compare the TLS-based estimators corrected and uncorrected for non-detection with the Horvitz–Thompson estimator based on conventional plot sampling, in which all the trees within plots are recorded. Results show that single-scan TLS provides more efficient estimators with respect to those provided by the conventional plot sampling in the case of low-density forests when no distance sampling correction is performed. In low-density forests, uncorrected estimators lead to a small bias (1%–6%), increasing with plot size. Therefore, care must be taken in enlarging the plot radius too much. The bias increases in forests with clustered spatial structures and in dense forests, where the bias levels (30%–50%) deteriorate the performance of uncorrected estimators. Even if the bias-corrected estimators prove to be effective in reducing the bias (below 15%), these reductions are not sufficient to outperform conventional plot sampling. Therefore, there is no convenience in using TLS-based estimation in high-density forests.