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evaluation of a measurement method for forest vegetation in a large-scale ecological survey (1997)

STAPANIAN, MARTIN A. ; CLINE, STEVEN P. ; CASSELL, DAVID L.

Springer

Environmental monitoring and assessment 45 (1997), S. 237-257

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ISSN: 1573-2959

Keywords: cover ; ecological monitoring ; measurement variation ; quality assurance ; vascular plant diversity

Source: Springer Online Journal Archives 1860-2000

Topics: Energy, Environment Protection, Nuclear Power Engineering

Notes: Abstract We evaluate a field method for determining species richness andcanopy cover of vascular plants for the Forest Health MonitoringProgram (FHM), an ecological survey of U.S. forests. Measurementsare taken within 12 1-m2 quadrats on 1/15 ha plots in FHM.Species richness and cover are determined for four height classes(strata) within each quadrat and aggregated by stratum over the entireplot. We estimated (1) the agreement between experienced trainers andinexperienced technicians who collected the data on this survey(accuracy) and (2) the agreement among the technicians (precision) forresults on species richness and cover from 3 test plots at 3 timeintervals. The methods appear to be highly precise, although somediscrepancies with the values obtained by the trainers were found.Trainers found significantly more species in the ground stratum (0–0.6 m) and measured significantly more cover in the uppermost stratum(〉4.9 m). The proportion of variation due to measurement error andtemporal variability was less than 13% for species richness (all strata)and cover (all but one stratum). This indicates that the method issuitable for monitoring changes in species richness and canopy coverfor a large-scale synoptic monitoring project such as FHM.

Type of Medium: Electronic Resource

URL: http://dx.doi.org/10.1023/A:1005747031287

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Finding suspected causes of measurement error in multivariate environmental data (1993)

Stapanian, Martin A. ; Garner, Forest C. ; Fitzgerald, Kirk E. ; [et al.]

New York, NY : Wiley-Blackwell

Journal of Chemometrics 7 (1993), S. 165-176

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ISSN: 0886-9383

Keywords: Measurement error ; Outliers ; Environmental ; Quality control ; Multivariate kurtosis ; Generalized distance ; Chemistry ; Analytical Chemistry and Spectroscopy

Source: Wiley InterScience Backfile Collection 1832-2000

Topics: Chemistry and Pharmacology

Notes: Environmental data are usually multivariate, with the variables conforming to some correlation structure. Occasionally, measurements which do not conform in structure or magnitude may occur in one or more variables. It is important (1) to characterize these discordancies in terms of the disturbed variables and the direction and magnitude of the anomalous error and (2) to associate each discordant observation with a specific cause of measurement error in order to prevent further mismeasurement. We describe a procedure for identifying suspected causes of discordant observations in otherwise multinormal data sets. Variables are assigned to groups, each of which is associated with a specific cause of measurement error. Discordant observations are identified with the generalized distance test or the multivariate kurtosis test. Suspected causes of measurement error are identified by repeating the tests with one of the groups of variables omitted in each analysis. The procedures are evaluated with simulated data sets having a correlation structure similar to that of a large environmental data set.

Additional Material: 4 Ill.