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doi:10.1016/j.ecolmodel.2004.05.010 
Copyright © 2004 Elsevier B.V. All rights reserved.
Useful techniques of validation for spatially explicit land-change models
Received 25 June 2003;
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Abstract
This paper offers techniques of validation that land-use and -cover change (LUCC) modelers should find useful because the methods give information that is useful to improve LUCC models and to set the agenda for future LUCC research. Specifically, the validation technique: (a) budgets sources of agreement and disagreement between the prediction map and the reference map, (b) compares the predictive model to a Null model that predicts pure persistence, (c) compares the predictive model to a Random model that predicts change evenly across the landscape, and (d) evaluates the goodness-of-fit at multiple-resolutions to see how scale influences the assessment. This paper introduces a new criterion called the Null Resolution, which is the spatial resolution at which the predictive model is as accurate as the Null model.
For illustration, these techniques are applied to assess an LUCC model called Geomod, which predicts land change in the 22 towns of the Ipswich River Watershed in northeastern Massachusetts, USA. For this application, the Null Resolution is approximately 1 km. At resolutions finer than 1 km, the Null model performs better than Geomod, which performs better than the Random model. At resolutions coarser than 1 km, both Geomod and the Random models perform better than the Null model, but Geomod and the Random models are nearly indistinguishable beyond the 1 km resolution.
Author Keywords: LUCC; Model; Null; Scale; Prediction; Resolution; Validation
Article Outline
- 1. Introduction
- 1.1. State of land-change modeling
- 1.2. Calibration versus validation
- 1.3. Criteria for validation
- 1.4. Ipswich River Watershed example
- 2. Methods
- 2.1. Data
- 2.2. The LUCC model
- 2.3. Three-way map comparison
- 2.4. Error budget
- 2.5. Multiple resolution comparison
- 2.6. Null Resolution
- 3. Results
- 4. Discussion
- 4.1. Interpretation of scale
- 4.2. The statistical criterion
- 4.3. The bias of masking
- 4.4. Room for improvement
- 4.5. Comparison with other models
- 5. Conclusions
- Acknowledgements
- References
