Comparison of non-destructive LAI determination methods and optimization of sampling schemes in an open Populus euphratica ecosystem

Populus euphratica (P. euphratica) grows in the water-limited Tarim River Basin in spatially heterogeneous open ecosystems; thus, efforts to quantify the leaf area index (LAI) with optical instruments developed for homogeneous closed canopies have a high probability of failure. In this study, we explored methods for designing an acceptable sampling scheme to quantify the tree LAI for open P. euphratica canopies in arid areas. Field data were collected from three 30 m × 30 m plots and one 100 m × 100 m plot. We compared three indirect methods, i.e. i) allometry, ii) LAI-2000 canopy analyser, iii) Tracing Radiation and Architecture of Canopies (TRAC), and a new semi-direct method combining leaf density and crown volume (SDDV) method for quantifying the isolated tree and canopy LAI of a P. euphratica forest. We also analysed the effects of random and grid sampling designs on the accuracy of the LAI estimates obtained with the LAI-2000. The results showed that the allometric method is applicable to isolated trees with regular shapes; however, because the LAI of P. euphratica was calculated from an allometric equation based on the basal area (at 1.3 m), the allometric equation is prone to failure if the basal area is beyond a specific range. Because there are no significant differences in the plot size between the allometric and the SDDV method predictions, the proposed SDDV method can be used as an alternative for field measurements. The combination of LAI-2000 and TRAC is found to be more reliable than TRAC only, and the field view of the LAI-2000 sensor and the clumping index are important factors for sparse vegetation LAI retrieval. The results from sampling optimization showed that for the LAI-2000 instrument, the best sampling method is grid sampling, and the sampling interval should not be less than 20 m. For random sampling scheme, the number of sampling points in a 100 m × 100 m plot should be greater than 86 with a coefficients of variation of 15% and an allowable error (AE) of 0.15 m² m^?2, respectively.