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Effects of Registration Errors Between Remotely Sensed and Ground Data on Estimators of Forest Area

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The estimation of area by land cover type is a key component of most large-scale forest inventories. Historically, these estimates were derived from a large sample of points, taken from aerial photos, followed by a smaller sample of ground points, which were used to correct errors in the classification of the aerial photo points. There has been interest in replacing aerial photography with satellite imagery. One problem with using satellite imagery is the registration errors between a pixel and a plot or a point on the ground. The estimators and modes of inference can differ substantially depending on whether the sample unit is a plot or point on the ground. Two terms, tessellated and point paradigm, are used to differentiate between the two approaches when the ground data consists of plots or points, respectively. The effects of registration errors are compared using the two paradigms for estimating the area of land by cover type. The effects of registration errors on the expected value and variance of the forest area estimator under both the tessellated and point paradigm were studied using a simulation study, where a percentage of the samples had a random one- or two-pixel registration error. The simulation study shows that registration errors increased the variance of the estimator of forest area from 4% to 434%. The estimator of forest area under both the tessellated and point paradigm exhibited no detectable bias. In the presence of registration errors, the estimated variance under the tessellated paradigm tended to overestimate the true variance with the achieved coverage rate for a nominal 80% confidence interval ranging from about 81% to 86%. For sample sizes of fewer than 100 ground points, the estimated variance under the point paradigm tended to underestimate both the 80% confidence interval and the true variance, regardless of whether there were registration errors. Further testing showed that sample sizes of between 100 and 250 ground points were needed before the estimator of the variance under the point paradigm converged to within 5% of the true variance of the estimator of forest area. Thus, we conclude that registration errors can drastically increase the variance of area estimators and the resulting confidence intervals will not achieve their nominal coverage rates. In this study, the forest area estimator under the tessellated paradigm was clearly more robust to registration errors than the area estimator under the point paradigm. FOR. SCI. 49(1):110–118.

Keywords: Aerial photography; design- and modelbased inference; environmental management; forest; forest management; forest resources; forestry; forestry research; forestry science; natural resource management; natural resources; satellite imagery; variance estimator

Document Type: Miscellaneous

Affiliations: Rocky Mountain Research Station, USDA Forest Service, 2150 A Center Drive, Suite 350 Fort Collins, Colorado, 80526, Phone: 970-295-5966; Fax: 970-295-5959

Publication date: February 1, 2003

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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