Quantifying uncertainty and confusion in land change analyses: A case study from central Mexico using MODIS data
Land cover classifications of coarse-resolution data can aid the identification and quantification of natural variability and anthropogenic change at regional scales, but true landscape change can be distorted by misrepresentation of map classes. The Lerma-Chapala-Santiago (LCS) is biophysically diverse and heavily modified by urbanization and agricultural expansion. Land cover maps classified with a Mahalanobis distance algorithm and possibilistic metrics of class membership were used to quantify uncertainty (potential error in class assignment) and change confusion (potential error in land change identification). While land change analysis suggests that ~33% of the landscape underwent a change in class, led by changes from or to the mosaic class (~19% of landscape), classification uncertainty values for 2001 and 2007 were 0.59 and 0.62, respectively, with highest uncertainty among bare soil classes, and an average confusion index value of 0.65, with pixels experiencing change at 0.67 and pixels experiencing persistence at 0.61 on average. These results indicate that uncertainty and potential error in land cover classifications estimates may inhibit accurate assessments of land change. Estimates of land change may be refined using these metrics to more confidently identify true landscape change and to find classes and locations that are contributing to errors in land change assessments.
GIScience and Remote Sensing
Christman, Zachary; Rogan, John; Eastman, J. Ronald; and Turner, B. L., "Quantifying uncertainty and confusion in land change analyses: A case study from central Mexico using MODIS data" (2015). Geography. 640.