Scenario-based simulation of land use in Yingtan (Jiangxi Province, China) using an integrated genetic algorithm-cellular automata-Markov model.

Yingtan is a rapidly urbanizing city in Jiangxi Province, South China. During rapid urbanization, construction land is expanded at the expense of cropland and forest. Although economic benefits are gained, ecological and environmental damage is irreversible. In this study, a methodological framework for land use simulation using an integrated genetic algorithm-cellular automata-Markov model is proposed to assess the relationship between economic development and cropland protection in Yingtan. This framework considers both the economic and ecological benefits of different land use types. Three land use scenarios are evaluated to seek recommendations for land use practice. The results show that the areas with high suitability for cropland and construction are mainly concentrated in urban fringes. Under the green development scenario, the area of new construction land can meet the land demand for population growth and economic development proposed for 2025 based on population forecasting and government interviews. The expansion for construction land is decreased by ~ 35 km2 while the cropland area is increased by ~ 20 km2 compared with those under natural and controlled development scenarios. Additionally, ecological losses are lowest under the green development scenario. In conclusion, the green development scenario is conducive to both cropland and ecological protection, which is of relevance for future spatial planning in Yingtan.

[Landscape pattern analysis and optimum design of park green space in Nanchang City, China based on GIS.] / 基于GISçš„å—æ˜Œå¸‚å ¬å›­ç»¿åœ°æ™¯è§‚æ ¼å±€åˆ†æžä¸Žä¼˜åŒ–è®¾è®¡.

Based on the current map data of park green space in the main urban area of Nanchang, the spatial database of park green space was set up with GIS technique, with the corresponding landscape indices being calculated by FRAGSTATS, the software of landscape pattern. Based on analyzing current landscape pattern of green space in Nanchang, the optimization strategy and scheme were proposed and the optimized landscape pattern was evaluated. The results indicated that the spatial distribution of patches in current park green space was uneven and area discrepancy was large, which is especially true in densely populated areas with less patch number of park green space and obviously low available area for disaster shelter. By substantially increasing the quantity and area of patches, improving the inter-patch connectivity, and increasing landscape fragmentation index appropriately, the "point-line-plane" pattern of park green space system in Nanchang would be optimized and the spatial distribution would be more rational, which could effectively enhance its role in biodiversity conservation, disaster prevention, and risk avoidance. The optimized indices of patch diversity, evenness and aggregation would be significantly increased, the dominance index would be reduced, and the landscape diversity would be more abundant.

[Spatial Variability of C-to-N Ratio of Farmland Soil in Jiangxi Province].

Spatial variability of soil carbon-to-nitrogen ratio (C/N) at the provincial scale was analyzed using ordinary kriging methods. The effects of the factors influencing C/N were quantified by regression analysis based on 16,582 points of surface soil samples (0-20 cm) collected during the project of soil-test-based formulated fertilization in Jiangxi Province in 2012. The results showed that soil C/N ranged from 2.98 to 52.67, with an average of 11.72. The coefficient of variation was 25.17%, suggesting moderate variability. The nugget-to-sill ratio was 88.44%, meaning that the stochastic factors played a more important role in the spatial variability of soil C/N between the structural and stochastic factors. The spatial distribution of soil C/N was relatively smooth and the high-value areas were mainly distributed in Pengze County-Jiujiang City, Shangli County-Pingxiang City, and Lean County-Fuzhou City. The terrain factors, farmland-use type, parent material, soil type, and the level of nitrogen fertilizer had significant impacts on the spatial variability of soil C/N (P<0.05), but the degree of influence was different for each factor. Soil C/N indicated a significant positive Pearson's correlation with elevation and the slope of slope (P<0.05). The terrain factors explained 0.3% of the spatial variability of soil C/N and the farmland-use could explain 1.4%. The explanatory power of soil groups, subgroups, and soil family were 2.7%, 3.6%, and 5.5% respectively. The level of nitrogen fertilizer could explain 33.4% of the spatial variability of soil C/N, which showed that the amount of nitrogen fertilizer was the main factor that controls the spatial distribution of soil C/N.