Accuracy Assessment of Land Cover Maps Derived from Multiple Data Sources

Maximum Likelihood (ML) and Artificial Neural Network (ANN) supervised classification methods were used to demarcate land cover types within IKONOS and Landsat ETM+ imagery. Three additional data sources were integrated into the classification process: Canopy Height Model (CHM), Digital Terrain Model (DTM) and Thermal data. Both the CHM and DTM were derived from multiple return small footprint LIDAR. Forty maps were created and assessed for overall map accuracy, user\u27s accuracy, producer\u27s accuracy, kappa statistic and Z statistic using classification schemes from U.S.G.S. 1976 levels 1 and 2 and T.G.l.C. 1999 levels 2 and 4. Results for overall accuracy of land cover maps derived from multiple sources ranged from 13.67 to 57.56 percent for U.S.G.S. level 2 and T.G.l.C. level 4 across ML and ANN classifications. Results for overall map accuracy ranged from 26.00 to 72.33 percent for U.S.G.S. level 1 and T.G.I.C. level 2 across ML and ANN classifications. Land cover maps, derived using ML classification methodology, were consistently more accurate than land cover maps derived using an ANN classification algorithm

Multi-Source Image Classification

Since multi-source image classifications have the ability to exceed single source processes, such as traditional unsupervised classification methods, this paper will present the integration of four types of data: Lidar, elevation, multispectral and thermal. Using multi-source data and maximum likelihood classification methodology, as well as all possible permutations of data types, this paper will discuss ways to increase accuracy assessments of forested areas in east Texas and find the best combination of data sources