Abstract:Understanding the relationship between the spatiotemporal expansion of rural settlement land and the variation of rural population is the foundation of rational and specific planning for sustainable development. Based on the integration of Landsat TM, ETM+, and OLI images and demographic data, using mathematical models, landscape indexes, and a decoupling model, the spatiotemporal changes of the rural settlement land area and its decoupling relationship with the rural registered population were analyzed for the middle basin of the Heihe River in China. During the period 1986-2014, the following changes occurred: (1) The study area experienced increases of 124.94%, 55.16%, and 1.56% in rural settlement land area, number of patches, and rural registered population, respectively; (2) Edge-expansion, dispersion, and urban encroachment were the dominant patterns of dynamic changes in the studied rural settlement land. Among these, edge-expansion was the most prevalent development pattern; it contributed more than half of the total increase in the number of patches and the total area growth; (3) The annual growth rate of the rural registered population increased from 0.7% in 1986-2002 to´0.5% in [2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014]. By that time the rural settlement land area had undergone a gentle increase from 3.4% to 3.6%. Generally, the rural registered population and rural settlement land has experienced a shift from weakly decoupled in 1986-2009 to strongly decoupled in 2009-2014; (4) From 1986 to 2014, rural urbanization and modernization were the main causes that led to the decline in the rural registered population; however, economic growth promoted the expansion of rural settlement land during this same period. We believe that with the rapid development of urbanization, the decoupling relationship between the rural settlement land area and the reduction in the rural registered population cannot be completely reversed in the short term. It is recommended that the government should enhance the role of planning rural settlement land during the process of urbanization.

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

Urban bare lots are persistent phenomena in urban landscapes in the course of urbanization. In the present study, we examined the spatio-temporal distribution of urban bare lots in low-slope hilly areas, and to assess the major pathways by which they are generated and later re-transformed for exploitation. We extracted land use and land cover (LULC) change information and analyzed spatio-temporal distribution characteristics of urban bare lots using Landsat TM/OLI series remote sensing images. Subsequently, we proposed an index system for their evaluation and classification, and identified five types of urban bare lots. Urban bare lot quantity and distribution are closely correlated with human activity intensity. Stakeholders should consider the multiple effects of location, topography, landscape index, transportation, service facilities, and urban planning in urban bare lot classification activities for renovation and re-transformation.