| Popis: |
To verify large-scale vegetation parameter measurements, the average value of sampling points from small-scale data is typically used. However, this method undermines the validity of the data due to the difference in scale or an inappropriate number of sampling points. A robust universal error assessment method for measuring ground vegetation parameters is, therefore, needed. Herein, we simulated vegetation scenarios and measurements by employing a normal distribution function and the Lindbergh–Levi theorem to deduce the characteristics of the error distribution. We found that the small- and large-scale error variations were similar among the theoretically deduced leaf area index (LAI) measurements. In addition, LAI was consistently normally distributed regardless of which a systematic error or an accidental error was applied. The difference between observed and theoretical errors was highest in the low-density scenario (7.6% at < 3% interval) and was lowest in the high-density scenario (5.5% at < 3% interval), while the average ratio between deviation and theoretical error of each scenario was 2.64% (low density), 2.07% (medium density), and 2.29% (high density). Furthermore, the relative difference between the theoretical and empirical errors was highest in the high-density scenario (20.0% at < 1% interval) and lowest in the low-density scenario (14.9% at < 1% interval), respectively. These data show the strength of a universal error assessment method, and we recommend that existing large-scale data of the study region are used to build a theoretical error distribution. Such prior work in conjunction with the models outlined in this article could reduce measurement costs and improve the efficiency of conducting ground measurements. |
