基于加权马尔可夫模型的土地利用预测——以徐州市为例

以正在进行功能转型的徐州市区为研究对象,通过遥感影像解译将该区域内土地分为水体、林地、建设用地、农田、未利用地5种用地类型,并计算了2001年、2005年和2008年的转移矩阵概率,利用加权马尔可夫方法预测徐州市未来9年内的土地利用变化。研究结果表明:徐州市在2009—2017年内,建设用地将增加7.363km2,水体和林地略有上升,耕地面积持续减少9.61km2;采用加权马尔可夫链进行修正,能提高预测结果的准确性。

Forecast of land use based on weighted markov model: A case study of Xuzhou

This paper took Xuzhou city, which is ongoing function transformation, as the study region, divided land resources by interpreting remote sensing images into 5 types, i.e., water, forest, construction land, farmland and unused land, calculated the probability of transfer matrix in 2001, 2005 and 2008, and then predicted the changes of land use in the coming 9 years with weighted Markov model which could improve the accuracy of prediction. The results showed that from 2009 to 2017 the area of construction land will increase 7.363 km2, the areas of water and forest lands will rise slightly, but the area of farmland will continuously decrease 9.61 km2.