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

Hung, I-Kuai

Kroll, James

Tribby, Hillary

Unger, Daniel

Williams, Hans Michael

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Stephen F. Austin State UniversitySFA ScholarWorksFaculty Presentations Spatial Science10-2005Multi-Source Image ClassificationHillary TribbyJames KrollArthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, jkroll@sfasu.eduDaniel UngerArthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, unger@sfasu.eduI-Kuai HungArthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, hungi@sfasu.eduHans Michael WilliamsArthur Temple College of Forestry and Agriculture Division of Stephen F. Austin State University, hwilliams@sfasu.eduFollow this and additional works at: http://scholarworks.sfasu.edu/spatialsci_facultypresPart of the Forest Management CommonsTell us how this article helped you.This Conference Proceeding is brought to you for free and open access by the Spatial Science at SFA ScholarWorks. It has been accepted for inclusion inFaculty Presentations by an authorized administrator of SFA ScholarWorks. For more information, please contact cdsscholarworks@sfasu.edu.Recommended CitationTribby, Hillary; Kroll, James; Unger, Daniel; Hung, I-Kuai; and Williams, Hans Michael, "Multi-Source Image Classification" (2005).Faculty Presentations. Paper 19.http://scholarworks.sfasu.edu/spatialsci_facultypres/19MULTI-SOURCE IMAGE CLASSIFICATIONHillary Tribby, James Kroll, IDaniel Unger, I-Kuai Hung, Hans WilliamslCorresponding Author: Daniel Unger (unger@sfasu.edu)Arthur Temple College of Forestry and AgricultureStephen F. Austin State UniversityNacogdoches, Texas 75962ABSTRACTSince multi-source image classifications have the ability to exceed single sourceprocesses, such as traditional unsupervised classification methods, this paper will present theintegration of four types ofdata: Lidar, elevation, multispectral and thermal. Using multi-sourcedata and maximum likelihood classification methodology, as well as all possible permutations ofdata types, this paper will discuss ways to increase accuracy assessments of forested areas in eastTexas and find the best combination of data sources.Key Words: Land Cover, Accuracy, Lidar, Terrain, Canopy ElevationINTRODUCTIONLand cover classification is a method to demarcate the landscape for the purpose ofdetermining the dominant cover and monitoring the changes in land cover over time. Numerousschemes have been developed for use with land cover and land use classifications, but early insatellite remote sensing Anderson and his colleagues (1976) developed and published a rubric bywhich to define dominant cover.Although important information can be extracted from a single satellite image, theinclusion of multiple sources and types of data may be best for analysis. This project assessedthe utility of combining terrain and canopy elevation values derived from lidar (light detectionand ranging) with traditional multispectral and thermal satellite imagery to increase the accuracyof land cover classifications.OBJECTIVESSince remote sensing technologies are constantly changing incorporating data into anygiven remote sensing project has become more complex. This project evaluated andrecommends which data sources should be integrated into image classifications in order toproduce the most accurate land cover map. These results will aid decision-makers indetermining what data will be included and provide the most accurate information possible. Ourprimary objective was to evaluate the accuracy of single and multi-source image land coverclassifications based on the hypotheses that: (1) the addition oflidar derived components willincrease land cover map accuracy; and, (2) the addition of thermal data will increase land covermap accuracy.METHODSUsing the inherent maximum likelihood (ML) algorithm in ENVI@ (Environment forVisualizing Images), the Principal Components of a Landsat ETM+ scene were classifiedaccording to the Anderson Land Use Land Cover System (Table 1; Figure 1). Since lidar datawere collected as vector data, it must be transformed into a continuous format. Moving windowsthat capture the highest elevation point for a given area were used to generate a surface elevationTable 1. Eigenvalues associated with each of the Landsat ETM+ Principal Component bands.Band Eigenvalue Percent Variability1 877.5889 89.782 71.7970 7.343 20.6104 2.114 4.2251 0.435 1.8339 0.196 1.4560 0.15Landsat ETM+ ImageryImage AcquisitionRadiometric CorrectionGeometric CorrectionTraining Site SelectionThermalORPrincipal Component AnalysisORMaximum LikelihoodClassificationAccuracy Assessment and ZStatisticPairwise Comparison of Maps 1,2,3,4, and 5Including multiple sources of data for integration into a classificationrequires data normalization and stacking.Figure 1. Schematic of image classification methodology and accuracy assessment.model (Popescu, Wynne & Nelson 2002). The digital terrain model was created using thesecond lidar return (Woolard & Colby, 2002; Lee & Shan, 2003; Popescu, 2004). The secondreturn represented the laser signal hits that penetrated the canopy and returned from the ground.Using the Geostatistical Analyst extension in ArcGIS 9.0®, a digital terrain model was generatedusing Radial Basis Function. Numerous interpolation techniques were used and the RBF-Multiquadric had the lowest Root Mean Square Error. The terrain model was subtracted fromthe surface model to generate the Canopy Height Model.The K statistic is a measure of agreement between the reference data and the classifiedland cover map and is calculated using the variables in the error matrix:Where k is the number of rows, Xii is the number of observations in row i and column t, Xi+ andX+i are the marginal totals of row i and column t, and 11 is the number of observations (Congalton,1991). To determine the Z statistic the variance must be calculated using the Delta method:whereandOnce variance has been calculated the Z statistic can be calculated:The Z statistic informs the researcher whether or not the agreement between the reference dataand the land cover map was greater than random chance. Since the Z statistic is standardized andnormally distributed, with the null hypothesis that K is equal to zero and the alternativehypothesis that K is not equal to zero, ifZ 2: ZaJ2 then the null hypothesis is rejected. Z scoreswith a value greater than one standard deviation from the mean or 1.960 is deemed significant.The Z statistic was also used to detennine significant differences between two map accuracies:By calculating the K, variance, and Z statistics for two error matrices, significant differences canbe identified (Congalton & Green, 1999).RESULTSOfthe five land cover maps, all map Z scores were deemed significant, indicating that theclassifications were better than random chance (Table 2). Differences between the maps werenot deemed to be significantly different, indicating that the addition of multiple sources of datadid not effect the supervised classification ofLandsat ETM+ data (Table 3).Although the incorporation of multiple sources did not significantly effect the accuracyassessment, the inclusion of multiple data sources did produced the highest kappa statistic.Future studies should assess the accuracy of higher spatial resolution image classifications thatinclude similar data.Table 2. Land cover map results showing significant Z scores.Land Cover Map OverallAccuracy %KappaCoefficientVariance Z ScorePrincipal Component Analysis 65.63 0.5816 0.001292 16.184(PCA)PCA and Canopy Height Model 64.23 0.5664 0.003632 9.400(CHM)PCA and Digital Terrain Model 69.30 0.6283 0.003112 11.262(DTM)PCA and Thermal Data 67.61 0.6050 0.001074 18.456PCA, CHM, DTM, and Thermal 70.14 0.6384 0.004320 9.713, 'Table 3. Kappa analysis results describing the pairwise comparisons of error matrices.Map2 Z ScorePCA PCAandCHM 0.2167PCA PCAandDTM 0.7025PCA PCA and Thermal 0.4793PCA PCA, CHM, DTM, and Thermal 0.7577PCAandCHM PCAandDTM 0.7529PCAandCHM PCA and Thermal 0.5615PCAandCHM PCA, CHM, DTM, and Thermal 0.8070PCAandDTM PCA and Thermal 0.3602PCAandDTM PCA, CHM, DTM, and Thermal 0.1176PCA and Thermal PCA, CHM, DTM, and Thermal 0.4554· .REFERENCESAnderson,1. R., Hardy, E. E., Roach, 1. T. & Witmer, R. E. (1976). A land use and land coverclassification system for use with remote sensor data (Geological survey professionalpaper 964). Washington, DC: U.S. Government Printing Office.Congalton, R G. (1991). A review of assessing the accuracy of classifications of remotelysensed data. Rem. Sens. Env., 37,35-46.Congalton, RG. & Green, K. (1999). Assessing the accuracy ofremotely sensed data:Principles andpractices. Boca Raton, FL: Lewis Pubs.Lee, D. S. & Shan, J. (2003). Combining lidar elevation data and IKONOS multispectralimagery for coastal classification mapping. Marine Geodesy, 26, 117-127.Popescu, S. C., Wynne, R H. & Nelson, R F. (2002). Estimating plot-level tree heights withlidar: local filtering with a canopy-height based variable window size. Compo &Electron. Ag., 37, 71-95.Popescu, S. C. (2004). Seeing the trees in the forest: Using small-footprint airborne lidarmeasurements. PhD. dissertation, Virginia Polytechnic Institute and State University,Blacksburg, VA. 155 p.Woolard, J. W. & Colby, 1. D. (2002). Spatial characterization, resolution, and volumetricchange of coastal dunes using airborne LIDAR: Cape Hatteras, North Carolina.Geomorphology, 48, 269-287.

Multi-Source Image Classification

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