Methods to summarize change among land categories across time intervals

Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error

The Scientific Foundations of Forecasting Magnetospheric Space Weather

The magnetosphere is the lens through which solar space weather phenomena are focused and directed towards the Earth. In particular, the non-linear interaction of the solar wind with the Earth's magnetic field leads to the formation of highly inhomogenous electrical currents in the ionosphere which can ultimately result in damage to and problems with the operation of power distribution networks. Since electric power is the fundamental cornerstone of modern life, the interruption of power is the primary pathway by which space weather has impact on human activity and technology. Consequently, in the context of space weather, it is the ability to predict geomagnetic activity that is of key importance. This is usually stated in terms of geomagnetic storms, but we argue that in fact it is the substorm phenomenon which contains the crucial physics, and therefore prediction of substorm occurrence, severity and duration, either within the context of a longer-lasting geomagnetic storm, but potentially also as an isolated event, is of critical importance. Here we review the physics of the magnetosphere in the frame of space weather forecasting, focusing on recent results, current understanding, and an assessment of probable future developments.Peer reviewe

Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don District, Vietnam

Geographic modelers frequently compare maps of observed land transitions to maps of simulated land transitions to relate the patterns in reference maps to the output from a simulation model. Pixel-by-pixel analysis of raster maps at a single resolution is useful for this task at a single scale, but scientists often need to consider additional scales. This article presents methods to satisfy this need by proposing a multiple-resolution method to compare land categories in three maps: a reference map of time 1, a reference map of time 2, and a simulation map of time 2. The method generates a three-dimensional table that gives the percentage of the study area for each combination of categories at the maps' native resolution and at several nested sets of coarser squares. The method differentiates allocation disagreement within coarse squares, allocation disagreement among coarse squares, quantity disagreement, and agreement. We illustrate the method with output from a run of the SAMBA agent-based model from 1990 to 2001 using 32-m resolution pixels for Cho Don District, Vietnam. Results show that half of the overall disagreement is attributable to allocation disagreement within squares that are 506 m 506 m, which is about the average size of a village. Much of the remaining disagreement is misallocation of forest and shrub between the northern and southern parts of the district, which is caused by differences between the data and the simulation concerning transitions from the crop and shrub categories

Opportunities to Improve Impact, Integration, and Evaluation of Land Change Models

Land change modeling supports analyses, assessments, and decisions concerning land management by providing a platform for both encoding mechanisms of land-change processes and making projections of future land-cover and land-use patterns. Approaches have ranged from pattern-based methods, such as machine learning models, to structural or process-based methods, such as economic or agent-based models. Selection of the appropriate modeling approach for a given scientific or decision making purpose is essential. Additionally, we argue that more needs to be done to develop and disseminate methods for evaluating land-change models (LCMs). The profession needs better data to support the use of LCMs, integration of models that operate at various scales, and combinations of models that address both positive and normative aspects of land use and land cover patterns and dynamics. © 2013 Elsevier B.V