期刊
LAND
卷 10, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/land10010035
关键词
land cover monitoring; sampling; Landsat; LCMAP; remote sensing
资金
- U.S. Geological Survey's Land Resources Mission Area under USGS Contract [G15PC00012]
- U.S. Forest Service (IRDB-LCMSNWFP) Landscape Change Monitoring System
- USGS [G17AC00237]
The study compares agreement between area estimates obtained from simple averaging (SA) and latent class modeling (LCM). Results indicate that the LCM area estimates for wetland and three change types show significant differences compared to SA, with LCM typically estimating higher percentages for these rare classes.
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.
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