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基于MODIS影像的土地覆被分类研究——以京津冀地区为例

DOI: 10.11820/dlkxjz.2014.11.012, PP. 1556-1565

Keywords: CART决策树,MODIS影像,分类,分类特征组合,京津冀地区,土地覆被,谐波分析

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Abstract:

在全球变化研究中,如何快速、准确获取土地覆被信息对该项研究有着至关重要的作用.随着遥感科学的不断发展和应用领域的深入,研究者可以利用遥感影像进行土地覆被分类研究,并且具有准确、快速、自动化等优点.本文利用MODIS数据具有的多光谱、多时相特点,以京津冀地区为例,选取2013年全年16-day的MOD13Q1/EVI时间序列数据、2013年5月份一期的MOD09Q1(1、2波段)和MOD09A1(3-7波段)产品,并运用时间序列谐波分析法对全年MOD13Q1/EVI时间序列数据进行去云、去噪的平滑重建处理,使其数据更能反映物候周期性变化规律.选择谐波分析后的全年MOD13Q1/EVI时间序列数据、MODIS数据的1-7波段地表反射率和NDWI(归一化差异水体指数)、MNDWI(改进归一化差异水体指数)和NDSI(土壤亮度指数),构建了3种特征变量组合方案的CART决策树,分别进行京津冀地区的土地覆被分类研究.结果表明方案一(全年EVI的23个时相)、方案二(方案一+MOD09的1-7波段地表反射率)和方案三(方案二+MNDWI+NDSI+NDWI)的总体分类精度分别达到86.70%、89.98%、91.34%,Kappa系数分别为84.94%、88.66%、90.20%.研究表明,仅利用MODIS遥感影像自身多种分类特征和决策树方法对宏观土地覆被分类就可达到较高精度,显示了本文分类方法在实践中的可行性及MODIS数据在区域尺度土地覆被分类研究方面的优势与潜力.

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