Charles Darwin University

CDU eSpace
Institutional Repository

CDU Staff and Student only

An assessment of map validation techniques : a case study using a vegetation map of Litchfield National Park, Northern Territory, Australia

Singh, Hardev (2016). An assessment of map validation techniques : a case study using a vegetation map of Litchfield National Park, Northern Territory, Australia. MEM Thesis, Charles Darwin University.

Document type: Thesis
Citation counts: Google Scholar Search Google Scholar
Attached Files (Some files may be inaccessible until you login with your CDU eSpace credentials)
Name Description MIMEType Size Downloads
Download this reading MEM_59616_Singh_H.pdf PDF scanned and generated by CDU application/pdf 1.94MB 189
Reading the attached file works best in Firefox, Chrome and IE 9 or later.

Author Singh, Hardev
Title An assessment of map validation techniques : a case study using a vegetation map of Litchfield National Park, Northern Territory, Australia
Institution Charles Darwin University
Publication Date 2016-07
Thesis Type MEM
Supervisor Edwards, Andrew
Trueman, Mandy
0502 - Environmental Science and Management
050205 - Environmental Management
Abstract Litchfield National Park required a vegetation habitat map to undertake informed conservation management. The Northern Territory Government Department of Land Resource Management were commissioned to derive this map in a short timeframe that did not enable validation of the results. Good vegetation mapping procedure requires validation to inform the users of the map’s inherent errors. This can be done with field reference data by statistical analysis using a standard error matrix and Kappa analysis. The user’s and producer’s accuracy derived through the error matrix enables the users of the map to assess the individual vegetation class accuracy. However, the field sampling and data recording methods, although widely used, are not fully standardised.

Therefore, this research undertook an accuracy assessment of the Litchfield vegetation map to test the appropriateness of various field sampling techniques for reference data collection. This included the comparison of stratified versus stratified-random sampling and the comparison of vegetation classification on-site versus post-field photo interpretation. Two complete field datasets of 12 different vegetation classes were collected through air survey. All site classifications were calibrated based on the field photos, observations, imagery and landscape context. Subsequently, these datasets were used to create error matrices and conduct statistical analysis that assessed the accuracy of the map and the sampling and classification strategies.

The overall accuracy of the Litchfield vegetation map was “moderate” (~60%). The vegetation types of lowland woodland, alluvial grassland, riparian and sandstone woodland were the most accurately mapped. The sampling methods comparison (Z statistic) showed that there was no significant difference in the error matrices generated from each sampling strategy, so it can be concluded that both methods were effective for collecting the reference data. When the Litchfield vegetation map was reclassified into 4 simple classes based on vegetation structure or management units, the overall accuracy of the mapping was much higher (72 – 80%), however still included misclassifications that would result in erroneous management. The field photo data recording type was more accurate (87%) compared to the on-site observation method (79%) for generating reference data for map validation.

Both reference data collection strategies, random stratified and systematic stratified, were equally suitable for providing data for the map accuracy assessment. This means that the easier, less costly approach can be used, in this case the easiest and cheapest strategy was random transect - stratified interval sampling.
Keyword Accuracy assessment
Litchfield vegetation map
Random/stratified sampling
Error matrix
Kappa analysis
Open access True

© copyright

Every reasonable effort has been made to ensure that permission has been obtained for items included in CDU eSpace. If you believe that your rights have been infringed by this repository, please contact

Version Filter Type
Access Statistics: 186 Abstract Views, 189 File Downloads  -  Detailed Statistics
Created: Tue, 23 Aug 2016, 11:29:21 CST by Jessie Ng