Spatial and temporal patterns of error in land cover change analyses: Identifying and propagating uncertainty for ecological monitoring and modeling.

Improving our understanding of the uncertainty associated with a map of land-cover change is needed given the importance placed on modeling our changing landscape. My dissertation research addressed the challenges of estimating the accuracy of a map of change by improving our understanding of the spatio-temporal structure of error in multi-date classified imagery, investigating the relative strength and importance of a temporal dependence between classification errors in multi-date imagery, and exploring the interaction of classification errors within a simulated model of land-cover change. First, I quantified the spatial and temporal patterns of error in multi-date classified imagery acquired for Pittsfield Township, Michigan. Specifically, I examined the propagation of error in a post-classification change analysis. The spatial patterns of misclassification for each classified map, the temporal correlation between the errors in each classified map, and secondary variables that may have affected the pattern of error associated with the map of change were analyzed by addressing a series of research hypothesis. The results of all analyses provided a thorough description and understanding of the spatio-temporal error structure for this test township. Second, I developed a model of error propagation in land-cover change that simulated user-defined spatial and temporal patterns of error within a time-series of classified maps to assess the impact of the specified error patterns on the accuracy of the resulting map of change. Two models were developed. The first established the overall modeling framework using land-cover maps composed of two land-cover classes. The second extended the initial model by using three land-cover class maps to investigate model performance under increased landscape complexity. The results of the simulated model demonstrated that the presence of temporal interaction between the errors of individual classified maps affected the resulting accuracy of the map of change, and the magnitude of this effect was dependent on both the pattern of change and pattern of error considered. This dissertation took an important step forward in improving our understanding of the spatio-temporal structure of classification error in a change analysis. The results of this work provide the starting point for building a conceptual model of change error.Ph.D.Earth SciencesPhysical geographyRemote sensingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/127046/2/3328777.pd

Abstract Stochastic Simulation of Land-Cover Change Using Geostatistics and Generalized Additive Models

An approach to simulating land-cover change based on pairs of classified images is presented. The method conditions the simulations on three sources of information: an initial land-cover map, maps of the probabilities of each possible class transition, and a description of the spatial patterns of changes (e.g., semivariograms). The method can produce multiple simulated land-cover maps that honor each of these sources of information. The approach is demonstrated for data on forest-cover change near Traverse City, Michigan. The discussion describes extensions to the method and an approach to generating future land-cover scenarios based on socioeconomic information

Projecting regional climate and cropland changes using a linked biogeophysical‐socioeconomic modeling framework: 2. Transient dynamics

Abstract Understanding climate‐cropland interactions and their impact on future projections in West Africa motivated the recent development of a modeling framework that asynchronously couples four models for regional climate, crop growth, socioeconomics, and cropland allocation. This modeling framework can be applied to a future time slice using an equilibrium approach or to a continuous projection using a transient approach. This paper compares the differences between these two approaches, examines the transient dynamics of the system, and evaluates its impact on future projections. During the course of projection up to mid‐century, food demand is projected to increase monotonically, while the projected crop yield shows a high degree of temporal dynamics due to strong climate variability. Such temporal dynamics are not accounted for by the equilibrium approach. As a result, the transient approach projects a generally faster future expansion of cropland, with the largest differences over Benin, Burkina Faso, Ghana, Senegal, and Togo. Despite the relative large differences between the two approaches in projecting land cover changes associated with cropland expansion, the projected future climate changes are fairly similar. While the additional cropland expansion in the transient approach favors a wet signal, both the transient and equilibrium approaches project a future decrease of rainfall in the western part of West Africa and an increase in the eastern part. For quantifying climate changes, the equilibrium application of the modeling framework is likely to be sufficient; for assessing climate impact on agricultural sectors and devising mitigation and adaptation strategies, transient dynamics is important