山东沿海地区土地利用和景观格局变化

为分析和预测山东省沿海区域土地利用和景观格局变化,该文将景观格局指数作为评价土地利用变化模拟模型的基本指标;基于RS和GIS技术分析2000-2010年土地利用和景观格局的变化特征,并尝试CA-Markov模型预测土地利用变化,发现其在景观格局预测方面的不足,因而探索和提出Spatial-Markov模型,该模型不仅适合于土地利用变化模拟,也适合于景观格局过程分析。具体包括:1)基于2000、2005和2010年的Landsat影像进行土地利用分类,分析10a间土地利用和景观格局的变化特征,表明:耕地面积不断减少,城镇和农村居民点用地不断扩张而占用大量耕地,草地等又不断开垦为耕地;区域景观格局破碎化趋势显著,人为干扰加剧,各种景观类型的分布向均匀化发展;2)基于Logistic-CA-Markov模型,以11个变量、2000和2005年土地利用分类图为基础,模拟的2010年土地利用图与观测值相比较,虽然得到的Kappa系数较高(0.8530),但难以支持对景观格局特征的预测和分析;3)提出Spatial-Markov模型,基于2000和2005年土地利用分类图模拟2010年土地利用,模拟结果的Kappa系数高达0.8872,且景观格局指数也与观测值非常接近,因此,选择该模型预测2015和2020年的土地利用和景观格局;4)预测结果表明,2010-2020年间耕地面积将继续减少,城镇、农村居民点将继续保持快速增长的态势;景观尺度除了分形维数,其他指数保持2000-2010年间的变化趋势,而在类型尺度,除水域和未利用地外,各种景观类型多个景观指数将总体保持原有的变化趋势。该研究可为山东沿海区域土地利用规划提供参考,并为土地利用预测研究提供了一种新的方法。

Land use and landscape pattern changes in coastal areas of Shandong province, China

In this paper, the Spatial-Markov model, which was based on the theory of Markov process and spatial analysis techniques, was proposed to simulate land use change and landscape dynamics. By the Spatial-Markov model, the study area could be divided into numerous lattices and land use change in each lattices was simulated separately by the Markov process model. The outputs of the model include a set of ratio scale images and a nominal scale image. The whole process of the model was fulfilled by compiling programs with AML in ArcGIS 9.3. The coastal area of Shandong province was selected as the case study area. Land use maps were extracted based on Landsat TM/ETM+ images captured in 2000, 2005, and 2010 respectively. Firstly, characteristics of land use change and landscape dynamics were analyzed. It showed that, from 2000 to 2010, urban area and rural settlement expanded dramatically by massively occupying farmland, which, in turn, drove grassland reclaimed to farmland. At the landscape level, the landscape fragmentation increased, and both the diversity and evenness of the landscape increased. Secondly, using land use maps in 2000 and 2005, the Spatial-Markov model was developed to simulate the land use map in 2010 at a spatial scale of 500m. At the same time, the CA-Markov model was selected for model comparison, in specific, eleven driving factors were selected and the Logistic regression method was used to create the transitional maps for CA. Both Kappa coefficient and landscape indices were introduced to evaluate and compare the two models. It showed that the Spatial-Markov model not only achieved much higher Kappa coefficient, but also much better landscape indices than the CA-Markov model. Therefore, the Spatial-Markov model was applied to predict land use change and landscape dynamics in the next decade. Moreover, the prediction result shows that, from 2010 to 2020, areas of urban area and rural settlement will go on increasing, while areas of farmland will continue to decline. At the landscape level, all the landscape indices will follow their historical trend except for fractal dimension. As to the landscape indices at the class level, all landscape types will follow the same trend as before except for water and unused land.