| Description: |
A report that 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 land-change model (LCM) enterprise. |
| Synopsis: |
People are constantly changing the land surface through construction, agriculture, energy production, and other activities. Changes both in how land is used by people (land use) and in the vegetation, rock, buildings, and other physical material that cover the Earth's surface (land cover) can be described and future land change can be projected using land-change models (LCMs). 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. Advancing Land Change Modeling: Opportunities and Research Requirements describes various LCM approaches, suggests guidance for their appropriate application, and makes recommendations to improve the integration of observation strategies into the models.This report 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. Because of the numerous models available, the report focuses on describing the categories of approaches used along with selected examples, rather than providing a review of specific models. Additionally, because all modeling approaches have relative strengths and weaknesses, the report compares these relative to different purposes. Advancing Land Change Modeling's recommendations for assessment of future data and research needs will enable model outputs to better assist the science, policy, and decisionsupport communities. |