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Spatial Accuracy Assessment in Natural Resources And This file was created by scanning the printed publication. Errors identified by the software have been corrected; however, some errors may remain. Attribute and Positional Accuracy Assessment of the Murray Darling Basin Project, Australia Fitzgerald, R.W.', Ritman, K.T. & Lewis, A. Abstract: The Murray Darling Basin comprises a land area of 1,058,000 km2 covering a substantial portion of SE Australia and encompassing Australia's largest river system. A basin wide woody vegetation dataset has been assembled from LANDSAT TM imagery supplemented with aerial photography at a nominal scale of 1: 100,000. The attribute accuracy assessment method is based on a multi-stage systematic sample design. A rectangular grid is placed over the entire Murray Darling Basin dataset and at each primary sample point, a secondary grid is created formed from a square contiguous set of aerial photos. A half tone, grey scale transparency covering approximately 10km2 is generated from satellite digital imagery (LANDSAT TM) for each primary grid point at a contact scale matching the available air photos. Overprinted on this transparency are the secondary grid points as patch size sampling frames. The air photos are then visually co-registered underneath the transparency with Landcover features from the LANDSAT TM image. Attribute data (presence or absence of woody vegetation) is collected directly from the air photos for each secondary grid point at 4 different patch sizes. Positional accuracy is assessed by recording 40 or more ground control points from the 1: 100,000 map sheet containing the primary grid point. These Eastings and Northings are compared to their position in the LANDSAT TM image. The outcome of the accuracy assessment is a Basin wide attribute and positional accuracy statement and spatial variability contour maps of attribute and positional accuracy as GIs overlays. INTRODUCTION This paper discusses the attribute and positional accuracy assessment I Infoplus, Po Box 125, Queanbeyan, NSW 2620, Australia. Fax: -+dl (0)6 299 1331, E-mail: bfitzger@pcug, org.au methodology developed for the Murray Darling Basin project2(MDBP). The Murray Darling Basin (MDB) covers an area exceeding 440 x 1: 100,000 map sheets with a total area of 1,058,000 krn2. The diverse range of vegetation and land forms creates challenges in terms of methodology and logistics. The initial focus of the accuracy assessment method include three stratification hypothesise. The first is that the local geographic variations in the classification accuracy is a function of vegetation type, terrain and substrate. Second, the smaller the patch of woody vegetation, the lower the attribute accuracy. Thirdly, the overall accuracy percentage is proportional to the percentage of vegetation cover. If the vegetation cover is patchy, then the ratio between the length of a boundary around a patch polygon and it's enclosed area is of interest. This ratio was examined by Crapper et.al. (1 986). The positional and accuracy assessment methodology will be: a. Statistically sound, practical, inexpensive to implement, easily understood by project staff and portable to MDBP's GIs; b. Produce scalable attribute and positional accuracy assessments of the woody1 non woody vegetation dataset as; I. Attribute accuracy assessment statistics (error matrix, user and producer accuracies and Kappa statistic) at different .. spatial scales; 11. Spatial variability maps of attribute and positional accuracy for the entire MDBP. A brief examination of the MDB woodylnon-woody GIs layer provided an insight into data quality and processing standards. The details of these standards including lineage are outlined in the Draft specification (Ritman, 1995). The Victorian & South Australian groups have examined attribute accuracy assessment methodologies. The work of Czaplewski et.al. (1 992) proved to be the most substantial and well documented attribute accuracy assessment methodology available. They used aerial photo interpretation as their pseudo ground truth and a systematic sample. Tadrowski et.al. (1990) in South Australia investigated supervised classification aided by manual photo interpretation and a stratified simple random sample for attribute accuracy assessment. The production of the digital woody vegetation dataset at a nominal Basin-wide scale of 1:100,000 was achieved by a two stage process (Ritman, 1995). Woody vegetation is defined as any perennial vegetation having a height exceeding 2m and a density greater than 20% crown cover (McDonald et.al., 1990). The first stage was the digital classification from LANDSAT TM imagery of a template of only woody vegetation. This method is based on that of Gilbee & Goodson (1992) and comprises an unsupervised 100 IS0 class cluster analysis of 2 This paper is a product of a consulting project titled "AtfributeAccuracy AssessmentJor Project M305", DLWC, September 1995, Murray Darling Basin Project M305. 318 LANDSAT TM imagery, followed by manual aggregation based on field input, aerial photos and ancillary data. In addition a filter (an ARCINFO AML process) designed to remove patches of cells diagonally and orthogonally connected, created by Dr. K. Ritman, was passed over the resulting woody vegetation layer to remove unconnected vegetation patches less than 0.25 ha.. The resultant woody dataset is in raster format with 25 x 25m pixels. The next stage was either a manual or digital classification of vegetation structural elements such as genus, density class and growth form. Only the woody vegetation layer is subject to the attribute and positional accuracy methodology described in this paper. A BRIEF REVIEW OF THE LITERATURE AND PREVIOUS STUDIES. Attribute classification accuracy is usually assessed by constructing a contingency table of a classified map versus ground truth or reference data (Congalton, 1991; Veregin ,1989). The resulting error or confusion matrix C is a k x k matrix where k is the number of discreet classes in the classification scheme. In the case of the MDB woodylnon-woody dataset, k = 2. The most commonly used index of attribute classification accuracy is the overall accuracy percent (OA%). Confidence limits for the OA% can be constructed easily from either the binomial distribution or the normal approximation to the binomial distribution. Van Genderen et.al. (1 978) logically extends the use of the binomial confidence limits to estimate sample sizes given expected classification accuracy and confidence levels. The OA% is a simple index of classification accuracy which has its limitations. The OA% can't differentiate between errors of omission and commission. Also it can not reliably be used to compare the performance of different error matrices with different sample sizes as well as not being able to account for correct classification by chance alone. One of the best methods developed to overcome these limitations is the Kappa statistic discussed extensively by Congalton (1 99 1) and Fitzgerald & Lees (1994a). It statistically quantifies the level of agreement and has been shown to give a less biased estimate of classification accuracy than the OA% (Rosenfield & Fitzpatrick- Lins, 1986). The effects of sampling schemes on classification accuracies especially when viewed across the spectral, spatial, environmental, taxonomical and temporal domains can induce substantial bias into classification. Congalton (1 988, 1991) compares the relative effects of five sampling schemes including random and stratified random sampling on classification accuracy. Franklin & Hiernaux (1 991) discuss the effect of sampling schemes on woody vegetation classification while Fitzgerald & Lees (1994b) discuss scale and its relationship to floristic structure. High accuracy surveying standards exist for assessing the accuracy of topographic maps in all three dimensions. The state and national mapping agencies are responsible for the surveying standards and integrity of the map base. The implicit assumption made in this study is that this map base is generally correcf Positional accuracy quantifies the accuracy of feature locations after various image processing and GIs transformations have been applied. A number of tests are available to assess positional accuracy including deductive estimates, internal evidence checks, comparisons to source documents and reference to independent sources of higher accuracy (Veregin, 1989). The latter is the most desirable and the one used in this study. The independent source is the AUSLIG 1: 1 00,000 map base series. Spatial variability maps of both attribute and positional accuracy will be produced for the MDB from this accuracy assessment methodology. The 12 x 18 primary systematic grid (described below) will contain the derived data values of OA% and Kappa (attribute accuracy) and 2D-RMS and CEP (positional accuracy). These gridded values will then be interpolated to a surface from which a contour map (isometric lines) of attribute and positional accuracy will be produced. The accuracy of this interpolation is dependant on the number and spatial distribution of the observed sample values. Systematic sampling (aligned or unaligned) is the best of all the sampling techniques tested to minimise the effects of spatial distribution on contouring (Veregin, 1989). SAMPLE DESIGN The constraints on the sample design for the attribute and positional accuracy assessment for the MDBP were as follows: a. The extent of the MDB (1 ,058,000km2) precludes field checking as the major source of ground truth. Aerial photo interpretation
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