Journal of Scientific Research and Reports, ISSN: 2320-0227,Vol.: 3, Issue.: 3 (01-15 February)
Assessment of a Global Land Cover Classification Allocated Across the Landscape of Georgia (USA)
Pete Bettinger1*, Krista Merry1 and Zennure Ucar1 1School of Forestry and Natural Resources, University of Georgia, Athens, GA USA 30602, USA.
Pete Bettinger1*, Krista Merry1 and Zennure Ucar1
1School of Forestry and Natural Resources, University of Georgia, Athens, GA USA 30602, USA.
(1) Luis H. Alvarez Valencia, Laboratory of Environmental Biotechnology and Microbiology, CIIBAA Instituto Tecnológico de Sonora(ITSON), Obregon Sonora, México.
(2) Alessandro Pezzoli, Water Supply & Wastewater Engineering , Turin Polytechnic and Department of Meteorology, Turin University, Italy.
(1) Muringaniza Kudakwashe Collins Ralph, Midlands State University, Zimbabwe.
(2) Robert Gilmore Pontius Jr, Clark University, USA.
(3) Mark Matsa, Midlands State University, Zimbabwe.
Complete Peer review History: http://www.sciencedomain.org/review-history/2736
Aims: To assess the agreement of a global land cover map to reference imagery when applied to a region (state) of the southern United States and to determine whether different sampling designs or the use of broader land class definitions can overcome problems associated with the inherent heterogeneity of land use in the region.
Study Design: We assessed the agreement of the Glob Cover 2009 global, medium resolution land cover assignments within the State of Georgia to USDA NAIP reference imagery. We performed the assessment using two statistically random sampling methods: pixel-based and block-based sampling. We then grouped some land classes according to possibilities of agreement relationships expressed by others, and assessed the agreement using these systems.
Place and Duration of Study: State of Georgia (USA). Imagery and reference data acquired in 2009.
Methodology: Sample: We examined 3,930 sample pixels or pixel blocks from 16 land cover classes. Each sample was allocated a land class in the GlobCover 2009 database. Each sample was interpreted as a land class through photo-interpretation of USDA NAIP imagery. An omission-commission matrix was developed from the relationship between land cover map and reference interpretation, as was an estimated population matrix. Statistics regarding agreement were developed using the latter matrix.
Results: Overall agreement for the state of Georgia was approximately 48% using both pixel- and block-based assessments. Agreement increased with the implementation of the possibilities of agreement relationships for both pixel- and block-based assessments. Three forested land cover types, representing about 78% of the Glob Cover land classes in Georgia, had agreement levels between 60 and 97% when possibilities of agreement were employed.
Conclusion: The use of the Glob Cover 2009 land cover classification may be well suited for broad, regional analysis and assessment of land cover trends. Moderate levels of classification agreement for important resources (forested areas) were estimated within the State of Georgia.
Satellite imagery; agreement; stratified random sampling; producer's accuracy; user's accuracy.
Full Article - PDF Page 457-478
DOI : 10.9734/JSRR/2014/7167Review History Comments