Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability

Dingfan Xing, Stephen V. Stehman, Giles M. Foody and Bruce W. Pengra
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Dingfan Xing: Department of Sustainable Resources Management, SUNY ESF, 1 Forestry Drive, Syracuse, NY 13210, USA
Stephen V. Stehman: Department of Sustainable Resources Management, SUNY ESF, 1 Forestry Drive, Syracuse, NY 13210, USA
Giles M. Foody: School of Geography, University of Nottingham, Room C7 Sir Clive Granger, University Park, Nottingham NG7 2RD, UK
Bruce W. Pengra: KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA

Land, 2021, vol. 10, issue 1, 1-17

Abstract: Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.

Keywords: land cover monitoring; sampling; Landsat; LCMAP; remote sensing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:10:y:2021:i:1:p:35-:d:474246

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