Outputs from these analyses are particularly useful for identifying human activity or management of the landscape (e.g., agricultural plots) and hold the potential to interact more directly with models of human decision making.

6. Multiple LiDAR remote sensing platforms, from spaceborne to airborne systems, provide information in the third dimension. For example, the Geoscience Laser Altimeter System on ICESat collected data that can be used to map vegetation canopy parameters including tree height, biomass volume, and stand density. Sensors of this sort further expand the land variables that can be sensed and included in LCMs, either as inputs or outputs.

Whereas for many years, the primary land imaging sensors were Landsat and the Advanced Very High Resolution Radiometer, the LCM community now has at its disposal a large range of additional sensors and types of information for LCM inputs. As discussed in Chapter 3, however, the community is only beginning to harness the synergies possible from algorithm advances in both the LCM and remote sensing communities.

Other Data for Land Change Models

In addition to data from remote sensing, data from a wide variety of other sources are central to development of the ability in LCMs to both characterize aspects of the land system that cannot be directly observed with Earth observation technology (e.g., property ownership) and incorporate information about the social, historical, ecological, and other drivers of land change (e.g., population growth, economic activity).

A common challenge that arises with using these data for LCMs is that, with the exception of customized surveys, none were designed explicitly for use in LCMs but rather for different audiences and different purposes. For example, census data are collected over various administrative units and usually at decadal intervals. In addition, they can usually be used only at some aggregate level (e.g., census blocks), not at their finest spatial resolution. Other data may be recorded or collected more intermittently. Land values, for example, may be updated only when a property changes ownership. Four measurement and characterization issues common to all data for LCMs are briefly reviewed below and opportunities for future advancement are outlined in Chapter 3.

Format of variables

The subjective decision to format variables in LCMs as continuous or discrete influences the type of questions that can be answered and the type of analyses that can be employed (Southworth et al., 2004). With discrete variables, each spatial unit is represented by a single categorical value, and these data can detect wholesale changes in either the land use or cover (e.g., agriculture to urban) or the input drivers (e.g., change in land ownership). Continuous variables allow

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