Abstract
The complexity of land use and land cover (LULC) change models is often attributed to spatial heterogeneity of the phenomena they try to emulate. The associated outcome uncertainty stems from a combination of model unknowns. Contrarily to the widely shared consensus on the importance of evaluating outcome uncertainty, little attention has been given to the role a well-structured spatially explicit sensitivity analysis (SSA) of LULC models can play in corroborating model results. In this article, I propose a methodology for SSA that employs sensitivity indices (SIs), which decompose outcome uncertainty and allocate it to various combinations of inputs. Using an agent-based model of residential development, I explore the utility of the methodology in explaining the uncertainty of simulated land use change. Model sensitivity is analyzed using two approaches. The first is spatially inexplicit in that it applies SI to scalar outputs, where outcome land use maps are lumped into spatial statistics. The second approach, which is spatially explicit, employs the maps directly in SI calculations. It generates sensitivity maps that allow for identifying regions of factor influence, that is, areas where a particular input contributes most to the clusters of residential development uncertainty. I demonstrate that these two approaches are complementary, but at the same time can lead to different decisions regarding input factor prioritization.
Acknowledgments
I acknowledge the valuable comments of an anonymous reviewer and Dr. Robert Gilmore Pontius Jr, coeditor of this special issue, on the previous versions of this article.
Notes
1. 1. Represented using a scalar, for example, an average shift in ranks (Saisana et al. Citation2005).
2. 2. In extreme cases, that is, when Fdl = 0 or Fdl = 1, the total variance of development allocation at a given site is Vl(Y) = 0 and, as per equations (2) and (3), we cannot compute sensitivity indices.