The physical expansion of urban areas lead to lasting impacts on landscapes and livelihoods. Here, we conduct a global synthesis of trends in urban land expansion, in urban population densities, and lands converted into urban from 1970 to 2010. We find that small-medium urban areas lead their larger counterparts in both rates of urban land expansion and decreases in urban population densities. Urban population densities have consistently declined only in India, China, North America, and Europe with significant exceptions across city sizes. Over 60% of the reported urban expansion was formerly agricultural land with China, Southeast Asia, and Europe in the lead. Counterfactual analysis suggests that, due to the decrease in urban population densities, an estimated 125 000 km 2 land was converted to urban land uses that could have otherwise remained in cultivation or as natural vegetation. In particular, in India and Nigeria, with much of their populations dependent on agriculture, 85% and 30% more land, respectively, was converted to urban land due to decreasing urban population densities. With increasing urbanization, proactive management of urban land expansion, especially in small and medium cities, will be critical for saving agricultural lands in periurban regions while creating equitable and affordable urban landscapes.

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Accurate mapping of water surface boundaries in rivers is an important step for monitoring water stages, estimating discharge, flood extent, and geomorphic response to changing hydrologic conditions, and assessing riverine habitat. Nonetheless, it is a challenging task in spatially and spectrally heterogeneous river environments, commonly characterized by high spatiotemporal variations in morphology, bed material, and bank cover. In this study, we investigate the influence of channel morphology and bank characteristics on the delineation of water surface boundaries in rivers using high spatial resolution passive remote sensing and a template‐matching (object‐based) algorithm, and compare its efficacy with that of Support Vector Machine (SVM) (pixel‐based) algorithm. We perform a detailed quantitative evaluation of boundary‐delineation accuracy using spatially explicit error maps in tandem with the spatial maps of geomorphic and bank classes. Results show that template matching is more successful than SVM in delineating water surface boundaries in river sections with spatially challenging geomorphic landforms (e.g. sediment bar structures, partially submerged sediment deposits) and shallow water conditions. However, overall delineation accuracy by SVM is higher than that of template matching (without iterative hierarchical learning). Vegetation and water indices, especially when combined with texture information, improve the accuracy of template matching, for example, in river sections with overhanging trees and shadows – the two most problematic conditions in water surface boundary delineation. By identifying the influence of channel morphology and bank characteristics on water surface boundary mapping, this study helps determine river sections with higher uncertainty in delineation. In turn, the most suitable methods and data sets can be selectively utilized to improve geomorphic/hydraulic characterization. The methodology developed here can also be applied to similar studies on other geomorphic landforms including floodplains, wetlands, lakes, and coastlines. Copyright © 2014 John Wiley & Sons, Ltd.

Lowland rivers regularly flood and create complex inundation patterns where energy and matter are exchanged between landscape patches over a dynamic network of surface‐water connections. Scale‐freeness of networks for phenomena in many disciplines have been studied with mixed results. Here we present the first documented example of a (roughly) scale‐free network of surface‐water connections within a river‐floodplain landscape. We accomplish this by simulating 23 inundation maps across the historical range of flows for the Mission River in Texas. We then analyze the topology of the surface‐water connections between the river and two habitat patch types. Results show that surface‐water connectivity is scale‐free for ≥64% of simulated flows (≥70% for flows with floodplain inundation). Moreover, the dynamic surface‐water connections meet five of the six conceptual criteria of scale‐free networks. Our findings indicate that river‐floodplain landscapes are self‐organizing toward scale‐free surface‐water connections among patches that optimizes energy and matter exchange.

<p>Lowland rivers regularly flood and create complex inundation patterns where energy and matter are exchanged between landscape patches over a dynamic network of surface‐water connections. Scale‐freeness of networks for phenomena in many disciplines have been studied with mixed results. Here we present the first documented example of a (roughly) scale‐free network of surface‐water connections within a river‐floodplain landscape. We accomplish this by simulating inundation maps across the historical range of flows for the Mission River in Texas. We then analyze the topology of the surface‐water connections between the river and soil and vegetation habitat patch types. Results show that surface‐water connectivity is scale‐free for ≥64% of simulated flows (≥70% for flows with floodplain inundation). Moreover, the dynamic surface‐water connections meet five of the six conceptual criteria of scale‐free networks. Our findings indicate that river‐floodplain landscapes are self‐organizing toward scale‐free surface‐water connections among patches that optimizes energy and matter exchange.</p>