This study performed an assessment of the representational accuracy of the forest class of the GlobeLand30 (GL30) global land cover data sets for the country of Mexico using a robust geographically distributed forest inventory survey of the forests in Mexico. The representational accuracy assessment was carried out for both the 2000 and 2010 GL30 data sets. The detailed attribute data associated with the validation set demonstrates how GL30 classifies specific forest types and how canopy coverage and number for trees per site influence the likelihood of GL30 identifying the sites correctly as forests. The results indicate that producers accuracies range from 72.3% to 97.3%. The tropical forests (89.1%) were better represented by the GL30 forest class than the temperate forest (73.9%). The most poorly represented classes from the temperate (oak: 72.3%) and tropical (low dry deciduous jungle: 74.9%) groups were deciduous. Receiver Operator Curve and Area Under the Curve analyses show that canopy coverage of a site is a better predictor of GL30, correctly identifying the site as forest for temperate forest, and that the number of the trees per site is a better predictor of GL30 correctly identifying a site as forest for tropical forests. The results also indicate a distinct spatial variability in the location of the sample sites that are misidentified as forests by GL30. The results of this thesis will help researchers and professionals better understand the representational accuracy of the GL30 data sets for the forests in Mexico.
|Commitee:||Anthamatten, Peter, Maclaurin, Galen, Torres-Rojo, Juan|
|School:||University of Colorado at Denver|
|School Location:||United States -- Colorado|
|Source:||MAI 56/05M(E), Masters Abstracts International|
|Subjects:||Geography, Geographic information science|
|Keywords:||Forest, GlobeLand30, Mexico, Validation|
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