We developed a GIS and neural network-based land use/land cover change model for backcasting land use change and applied it to the Muskegon River watershed, a typical upper midwestern watershed in the USA. We developed 12 variants of the model, based on different structural assumptions, to simulate urban, forest, agriculture, and shrubland transitions. We compared the model variants against 12,598 land use interpreted locations from 235 aerial photographs acquired from the study region between the late 1930s through to the early 1970s. The model variants produced around 41-70% accuracy (integrating both omission and commission errors) in simulating the spatial locations of the dominant land use category, forests and agriculture, and lower accuracy for the shrub and urban land use categories. We describe the assumptions made in developing the model and discuss the implications of the assumptions to model goodness-of-fit analysis and to forecasting land use. The Windows executable version of the model and data sets are available for download from http://ltm.agriculture.purdue.edu/default_back.htm.
Bibliographical noteFunding Information:
We acknowledge funding from the NSF Water Cycle Program (grant #WCR 0233648), the NASA Land Cover/Use Change and Hydrology Program, the Great Lakes Fisheries Trust, Environmental Protection Agency’s Multi-stressor Response Program, and the Wege Foundation. We also thank Ashlee Kilpatrick and Amélie Davis for developing the historical land use database from interpreted aerial photographs. Suggestions of the two anonymous reviewers greatly improved the quality of the manuscript. Sarah Harvey and Kimberly Robinson provided editorial inputs on an earlier draft. Avantika Regmi helped in cross checking the FM spatial accuracy.
- Backcast model
- Land use change modeling
- Neural networks