Abstract
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.
Highlights
E STABLISHING the connection between genotype and phenotype is currently one of the most significant chal-Manuscript received July 8, 2019; revised September 26, 2019; accepted November 18, 2019
Using (5), we found that the measured average leaf area index (LAI) was consistently normally distributed regardless of which an systematic error (SE) or accidental error was applied
We have demonstrated the reliability and applicability of error assessment in LAI ground observations where any deviation of error distribution could be due to either the number of sampling points or the process of averaging variation across different scales
Summary
E STABLISHING the connection between genotype and phenotype is currently one of the most significant chal-Manuscript received July 8, 2019; revised September 26, 2019; accepted November 18, 2019. Color versions of one or more of the figures in this article are available online at http://ieeexplore.ieee.org. This article has supplementary downloadable material available at http://ieeexplore.ieee.org, provided by the authors. Survey costs and land accessibility limit the extent to which ground observations can be measured, so the data are normally extrapolated based on the parameters calculated for small areas [17]–[19]. For these reasons, whole regions are rarely or never measured in their entirety, which means that the sampling errors always exist
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Instrumentation and Measurement
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
