The Influence of Geographic Sampling Methods on Vegetation Map Accuracy Evaluation in a Swampy Environment

The effectiveness of five sampling methods was evaluated empirically to determine the thematic accuracy of six-class vegetation maps of two study areas in the Okefenokee Swamp produced b y computer-assisted supervised classification of Landsat TM data. An exhaustive survey of the two study areas permitted estimates of overall, user's, and producer's accuracies to be made by each sampling method. The spatial pattern of classification errors was also examined with the aid of spatial autocorrelation analysis. By comparing Kappa coefficients of agreement among error matrices generated by the different sampling methods and by applying Chi-square tests, the stratified random sampling method was found to perform best for thematic accuracy evaluation in a swampy environment where vegetation exhibited a mixture of simple and complex spatial patterns. The higher spatial resolution of the image data employed to produce the vegetation map tended to cause stronger positive spatial autocorrelation in the resultant map error pattern. Introduction In remote sensing applications, the thematic accuracy of the land-uselland-cover maps generated from aerial photographs or satellite images is usually evaluated with the aid of a geographic sampling procedure. A number of points, traverses (lines), or quadrats (areas) are selected by means of a geographic sampling method from the land-uselland-cover maps. Ground truthing of these points, traverses, or quadrats is then conducted to produce an error matrix, from which the overall accuracy, producer's accuracy, and user's accuracy can be calculated (Story and Congalton, 1986). There are different types of geographic sampling methods or designs for land-uselland-cover map accuracy evaluation. The commonly used ones for thematic map accuracy evaluation include simplc random, stratified random, systematic random, stratified systematic unaligned, and random clusters (Berry and Baker, 1968; Clark and Hosking, 1986; Griffith and Amrhein, 1991). Previous research by Congalton (1988a) using three types of land covers with varying degrees of spatial complexity (agriculture, range, and forest) generated by Landsat MSS images suggested that spatial complexity of a given environment would affect the choice of an appropriate sampling method. He found that simple random sampling was adequate in all situations, while stratified random sampling was also appropriate, especially when small areas were represented in the sample. However, the results of a stratified systematic unaligned sampling and systematic sampling varC.P. Lo is with the Department of Geography, University of Georgia, Athens, GA 30602 (chpanglo@uga.cc.uga.edu). L.J. Watson is with 3D Imaging, LLC, 29111 Newnam Road, Easton, MD 21601. ied depending on the spatial complexity of the image, and should be used with extreme caution. Because of the "systematic" nature of these two sampling methods, periodicity in remotely sensed data would also adversely affect their performance. It was further suggested that this type of analysis should be extended to higher spatial resolution Thematic Mapper (TM) data or SPOT data to see whether the same conclusions applied. In a subsequent research conducted by Stehman (1992) using simulated periodic patterns and actual land-uselland-cover derived from TM images, it was confirmed that systematic sampling methods were generally more precise and efficient than simple random sampling. When strong periodicity in the spatial pattern of misclassifications was suspected, systematic sampling and stratified systematic unaligned sampling should be employed only if the unfavorable sampling interval could be avoided. This research is an attempt to extend the empirical evaluation of the influence of sampling methods on thematic accuracy to higher spatial resolution TM data based on the vegetation pattern of the Okefenokee Swamp, which consists of forested upland, forested wetland, scrub-shrub wetland, emergent wetland, and aquatic bed (Hamilton, 1982). The swampy environment provides varying degrees of spatial complexity, which exhibits some of the characteristics of the forested, rangeland, and agricultural environments employed in Congalton's (1988a) study, for the evaluation of the influence of geographic sampling methods on thematic mapping accuracy. Characteristics of Geographic Sampling Methods The geographic sampling methods employed in this research are simple random, stratified random, systematic random, stratified systematic unaligned, and random cluster, each of which possesses distinctive properties. In simple random sampling, each point is chosen independently and randomly. Point locations are defined by coordinate positions. The x and y coordinates of these points are derived using either a random numbers table or a random numbers generator. Simple random sampling is usually conducted "without re~lacement. " meanine that each s a m ~ l e point can only be clhosen onck, and evGy point has anlequal chance of being chosen. In remotely sensed data, pixels are so numerous that simple random sampling may be justifiable (Griffith and Amrhein, 1991). Photogrammetric Engineering & Remote Sensing, Vol. 64, No. 12, December 1998, pp. 1189-1200. 0099-1112/98/6412-1189$3.00/0 O 1998 American Society for Photogrammetry and Remote Sensing PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING