Consensus Report

Advancing Land Change Modeling: Opportunities and Research Requirements (2013)

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Urban development, agriculture, and energy production are just a few of the ways that human activities are continually changing and reshaping the Earth’s surface. Land-change models (LCMs) are important tools for understanding and managing present and future landscape conditions, from an individual parcel of land in a city to the vast expanses of forests around the world. A recent explosion in the number and types of land observations, model approaches, and computational infrastructure has ushered in a new generation of land change models capable of informing decision making at a greater level of detail. This National Research Council report, produced at the request of the U.S. Geological Survey and NASA, evaluates the various land-change modeling approaches and their applications, and how they might be improved to better assist science, policy, and decision makers.

Key Messages

  • New observations, improvements in modeling capability and computer infrastructure, and advances in understanding the theoretical and social context of land change have created opportunities to improve land-change models to support research and decision making on current and future land change.
  • Opportunities for advancing land-change models themselves, include: (1) improving models that are process-based in order to understand the interaction between people’s actions and land change; (2) better linking of patterns and processes at local, regional, and global scales; (3) better integration with Earth system models that connect land changes with effects on ecosystem services; and (4) development of models with optimization and design-based approaches that generate options that are both plausible and useful to society.
  • A flood of new data creates opportunities to improve the next generation of land-change models by: (1) improving the capture and processing of remotely sensed data; (2) integrating other types of data not typically included in the models, such as land-use density and land value; (3) incorporating data on land-change “actors,” such as households, firms, landowners, and policymakers; and (4) bringing together geographically referenced data from multiple agencies and geographies into one systematically linked set of observations.
  • Advances in cyberinfrastucture have created opportunities by (1) enabling the collection and analysis of large amounts of data through crowd sourcing and distributed data mining; and (2) taking advantage of enhanced computer power to improve modeling resolution.
  • Opportunities for advancing the infrastructure to support land-change modeling include the development of: (1) a consistent infrastructure for documenting and sharing models and software; (2) a data infrastructure that provides access to a common set of data resources for running and validating models; and (3) a consistent and widely adopted community modeling and governance infrastructure.
  • If appropriately planned and executed, the next generation of models can be increasingly process based, can link processes in social and natural systems from the parcel scale to regional and global scales, and make use of better methods for process validation in order to enhance both their predictive skill and their utility for policy analyses.