Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey
Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey
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DOI：
10.1007/s10661-018-6877-y
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发表时间：
2018-07
影响因子：
3
通讯作者：
Elaheh Zadbagher;K. Becek;S. Berberoglu
中科院分区：
文献类型：
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作者：
Elaheh Zadbagher;K. Becek;S. Berberoglu
土地利用和土地覆盖(LULC)的变化影响几个自然环境因素，包括土壤侵蚀、水文平衡、生物多样性和气候，最终影响社会福祉。因此，土地利用变化是土地管理的一个重要方面。用于分析LULC变化的一种方法是数学建模方法。在这项研究中，细胞自动机和马尔可夫链(CA-MC)模型被用来预测土耳其塞伊汗盆地到2036年可能发生的LULC变化。使用基于目标的分类方法对1995、2006和2016年获取的卫星多光谱图像进行分类，并将其用作CA-MC模型的输入数据。随后，采用分类后比较法确定待模拟模型的参数。利用马尔可夫链分析和多准则评价法(MCE)分别生成转移概率矩阵和土地适宜性图。模型用Kappa指数进行了验证，总体水平达到77%。最后，根据过渡规则和过渡区域矩阵绘制了2036年土地利用/土地利用变化变化图。2036年土地利用/土地利用变化预测显示，与2016年的土地利用/土地利用/土地利用变化趋势相比，建成区类别增加了50%，空地类别减少了7%。2036年，农业用地也可能增加约8%。预计灌木面积将增加约4%，森林面积将减少5%。
Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based classification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted.