This book describes various land-change model (LCM) approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models.

LCMs are a key means for understanding how humans are reshaping the Earth's surface in the past and present, for forecasting future landscape conditions, and for developing policies to manage our use of resources and the environment at scales ranging from an individual parcel of land in a city to vast expanses of forests around the world.
This issue provides a summary and evaluation of several modeling approaches, and their theoretical and empirical underpinnings, relative to complex land-change dynamics and processes, and identifies several opportunities for further advancing the science, data, and cyberinfrastructure involved in the LCM enterprise.

Contents
SUMMARY
1. THE STATE OF LAND CHANGE MODELING
The Significance of Land Systems and Land Change Models
Key Concepts
Needs for Science and Practice
Model Uncertainty
Measurement and Characterization of Land Change
Structure of the Report
2. LAND CHANGE MODELING APPROACHES
Machine Learning and Statistical
Cellular
Sector-Based and Spatially Disaggregated Economic
Agent-Based
Hybrid Approaches
A Comparison of Land Change Modeling Approaches
3. IMPROVING LAND CHANGE MODELING
Opportunities for Advances in Land Change Models
Opportunities in Land Observation Strategies
Opportunities in Cyberinfrastructure
Opportunities for Infrastructure to Support Land Change Modeling
Model Evaluation
REFERENCES
APPENDIXES
A: List of Contributors
B: Online Questionnaire
C: Committee and Staff Biographies

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