Next Article in Journal
Earthworm Diversity, Forest Conversion and Agroforestry in Quang Nam Province, Vietnam
Previous Article in Journal
At the Crossroads of European Landscape Changes: Major Processes of Landscape Change in Czechia since the Middle of the 19th Century and Their Driving Forces
Article

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

1
Department of Sustainable Resources Management, SUNY ESF, 1 Forestry Drive, Syracuse, NY 13210, USA
2
School of Geography, University of Nottingham, Room C7 Sir Clive Granger, University Park, Nottingham NG7 2RD, UK
3
KBR, Contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD 57198, USA
*
Author to whom correspondence should be addressed.
Land 2021, 10(1), 35; https://doi.org/10.3390/land10010035
Received: 30 November 2020 / Revised: 23 December 2020 / Accepted: 30 December 2020 / Published: 4 January 2021
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. View Full-Text
Keywords: land cover monitoring; sampling; Landsat; LCMAP; remote sensing land cover monitoring; sampling; Landsat; LCMAP; remote sensing
Show Figures

Figure 1

MDPI and ACS Style

Xing, D.; Stehman, S.V.; Foody, G.M.; Pengra, B.W. Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability. Land 2021, 10, 35. https://doi.org/10.3390/land10010035

AMA Style

Xing D, Stehman SV, Foody GM, Pengra BW. Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability. Land. 2021; 10(1):35. https://doi.org/10.3390/land10010035

Chicago/Turabian Style

Xing, Dingfan, Stephen V. Stehman, Giles M. Foody, and Bruce W. Pengra 2021. "Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability" Land 10, no. 1: 35. https://doi.org/10.3390/land10010035

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop