Spatiotemporal Variation of Driving Forces for Settlement Expansion in Different Types of Counties

Multi-Dimensional Feature Recognition and Policy Implications of Rural Human–Land Relationships in China

Rural decline has become an indisputable fact and a global issue. As a developing country, China is simultaneously facing unprecedented rapid urbanization and severe rural decline. The coordinated development of its rural human–land relationship is therefore of great significance for ensuring the country’s food security and achieving both rural revitalization and sustainable development. Yet, the related research on this complex subject has mostly focused on a single element: rural settlements. Since studies of the rural human–land relationship tend to only discuss the coordinated change in rural populations vis-à-vis rural settlement area, their degree of spatial matching and intensive utilization level of rural settlements has been largely overlooked. To rectify this imbalance, using data on rural populations and rural settlement area in counties of Shandong Province in 2009 and 2018, this paper applied the methods of per capita rural settlement area, the Theil index, and Tapio’s decoupling model to quantitatively identify the rural human–land relationship along three dimensions: intensive utilization level, spatial matching degree, and change coordination degree. The results revealed that the per capita rural settlement area in Shandong Province was as high as 212.18 m2/person in 2018, which exceeded the standard to varying degrees in all cities, having an overall geographical pattern of being high in the north and low in the south. The Theil index for all cities was small, which indicates that the spatial matching between rural population and rural settlements is high. To sum up, there are small differences in the utilization of rural settlements among cities, and their extensive utilization of rural settlements is a common phenomenon. In addition, the relationship between the changes in the rural population size and rural settlement area corresponded to a discordant state, in the form of strong negative decoupling, expansive negative decoupling, and expansive coupling; however, among them, the strong negative decoupling type was the dominant type. It is worth noting that all of these three types will exacerbate the extensive utilization of rural settlements. Accordingly, this paper proposes policies and measures, such as the paid withdrawal of rural homesteads, an expanded scope of homestead transfer, cross-regional “increasing versus decreasing balance”, classified promotion of rural revitalization, and improved village planning.

An Elevation-Based Stratification Model for Simulating Land Use Change

Land use significantly influences the planet’s land surface and associated biogeochemical processes. With fierce conflict between various land uses, it is important to project the land system process to support decision-making. Lack of insight into scale differences of land use change (LUC) increased uncertainties in previous studies. To quantify the differences in LUCs within an elevation gradient, in this study, a novel model, the stratified land use change simulation model (SLUCS), was developed by using an elevation-based stratification strategy. This model consists of four modules. First, an elevation-based stratification module to develop a quantitative method for generating stratifications using elevation and land-use characteristics. Second, a non-spatial land-use demand module to forecast the overall land use area and make zoning constraints to simulate LUCs. Third, a stratified suitability estimation module that uses the stratified logistic regression method to reveal the regional relationship of the driving factors with LUCs at different stratifications. Finally, a spatial allocation of the land-use module, which projects a spatially explicit LUC. The SLUCS model was applied and tested in the Guizhou and Guangxi Karst Mountainous Region. Results validated the effectiveness of the model, and further demonstrated an improved spatial consistency with the reference, a higher accuracy assessment, and a better simulation performance in conversion areas than the traditional method. Three scenarios from 2015 to 2030 with different land-use priorities were designed and projected. Each scenario presented the same LUC trends, but with different magnitudes, including the rapid expansion of built-up land, the restoration of forest and water, and the loss of farmland and grassland. Priority of the socioeconomic development and ecological protection of the scenarios forecasted a sharper increase in the built-up land and in forests than the historical extrapolation scenario. The SLUCS model visually projected the LUC trajectory and competition between land uses, which suggests specific tradeoffs among management strategies to support sustainable land uses.

Exploring the Factors Driving Changes in Farmland within the Tumen/Tuman River Basin

Understanding farmland changes and their mechanisms is important for food security and sustainable development. This study assesses the farmland changes and their drivers within the Tumen River of China and the Tuman River within the Democratic People’s Republic of Korea (DPR Korea) from 1991 to 2016 (1991–2000, 2000–2010, and 2010–2016). Farmland surfaces in Tumen/Tuman River Basin (TRB) for each of the years were mapped from satellite imagery using an object-based image segmentation and a support vector machine (SVM) approach. A logistic regression was applied to discern the mechanisms underlying farmland changes. Results indicate that cultivated surfaces changes within the two regions were characterized by large differences during the three time periods. The decreases of cultivated surface of −15.55 km2 (i.e., 0.55% of total cultivated surface area in 2000) and −23.61 km2 (i.e., 0.83% of total cultivated surface area in 2016) occurred in China between 1991 and 2000 and between 2010 and 2016, respectively; while an increase of 30.98 km2 (i.e., 1.09% of total cultivated surface area in 2010) was seen between 2000 and 2010. Cultivated surfaces increased within DPR Korea side over the three time periods; a marked increase, in particular, was seen between 1991 and 2000 by 443.93 km2 (i.e., 23.43% of total cultivated surface area in 2000), while farmland increased by 140.87 km2 (i.e., 6.92% of total cultivated surface area in 2010) and 180.86 km2 (i.e., 1.78% of total cultivated surface area in 2016), respectively, between 2000 and 2010 and between 2010 and 2016. We also found that expansions and contractions in farmland within both regions of the TRB were mainly influenced by topographic, soil, climatic, and distance factors, which had different importance degrees. Among these significant forces, the temperatures in the two regions were paramount positive factors on farmland changes during 1991–2016 and slope in China and precipitation in DPR Korea were the paramount negative factors affecting farmland changes, respectively. Additionally, except for between 2000 and 2010 in DPR Korea TRB region, most of the factors significantly influencing the farmland changes revealed the same positive or negative effects in different periods, because of mountainous topography. This study allows enhancing understanding of the mechanisms underlying farmland changes in the TRB.

Does the Exhaustion of Resources Drive Land Use Changes? Evidence from the Influence of Coal Resources-Exhaustion on Coal Resources–Based Industry Land Use Changes

Analyzing the spatial-temporal changes of resources–based industrial land is essential to the transformation and development of resources–exhausted cities. In this paper, we studied coal resources–based industrial land use changes and their driving factors in a typical coal resources–exhausted city, Anyuan District, Pingxiang city. The changes between coal resources–based industrial land and other land-use types were analyzed. The logistic regression models were applied to identify the main driving factors and quantify their contributions to coal resources–based industrial land-use changes during the two periods of 2003–2008 and 2008–2013. The results show that coal resources–based industrial land declined by 34.37% during the period 2008–2013 as coal resources were being exhausted. Altitude, distance to roads, distance to town, population density change, fixed-asset investment per area change, and GDP per capita change drove coal resources–based industrial land-use changes. However, the patterns of the driving effects differed, and even the same factors had different influences on coal resources–based industrial land-use changes during the two periods. The changes in the driving factors can be seen as responses to socioeconomic transformation and development in the city, which is experiencing the exhaustion of coal resources. As a result of the comprehensive effects of these driving factors, coal resources–based industrial land use has changed in complex ways.

Temporal Dynamics of the Driving Factors of Urban Landscape Change of Addis Ababa During the Past Three Decades

Mapping and quantifying urban landscape dynamics and the underlying driving factors are crucial for devising appropriate policies, especially in cities of developing countries where the change is rapid. This study analyzed three decades (1984-2014) of land use land cover change of Addis Ababa using Landsat imagery and examined the underlying factors and their temporal dynamics through expert interview using Analytic Hierarchy Process (AHP). Classification results revealed that urban area increased by 50%, while agricultural land and forest decreased by 34 and 16%, respectively. The driving factors operated differently during the pre and post-1991 period. The year 1991 was chosen because it marked government change in the country resulting in policy change. Policy had the highest influence during the pre-1991 period. Land use change in this period was associated with the housing sector as policies and institutional setups were permissive to this sector. Population growth and in-migration were also important factors. Economic factors played significant role in the post-1991 period. The fact that urban land has a market value, the growth of private investment, and the speculated property market were among the economic factors. Policy reforms since 2003 were also influential to the change. Others such as accessibility, demography, and neighborhood factors were a response to economic factors. All the above-mentioned factors had vital role in shaping the urban pattern of the city. These findings can help planners and policymakers to better understand the dynamic relationship of urban land use and the driving factors to better manage the city.