基于时空适应反射率融合模型的林区遥感应用研究
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摘要
高空间、时间分辨率遥感数据在林业遥感变化监测方面具有重要的作用,然而,对于特定传感器获取的遥感影像在空间和时间分辨率上存在着不可调和的矛盾。本文针对神农架林区多云雾高时空分辨率数据缺乏的现状,提出了一套区域尺度高时空分辨率植被覆盖度数据构建方法。首先,利用LandSat8数据进行预处理,得到高分辨率的NDVI数据,并将MODIS NDVI数据进行重投影、重采样等预处理;其次,利用STARFM模型进行高分辨NDVI预测,利用评价因子选择最佳算法参数,并利用二分模型计算植被覆盖度;再次,以LandSat8获取的真实数据与预测数据进行精度评价;最后,选取黑龙江小兴安岭西北部林区生长季数据进行验证试验。结果表明:利用该方法可以在神农架林区获得预测日期的较好的NDVI及植被覆盖度,精度分别为90.8%、82.60%。此外,通过验证试验,可以获得同年生长季小兴安岭林场较好的NDVI以及植被覆盖度,精度分别达到92.86%、88.65%。
High spatial and temporal resolution remote sensing data plays an important role in forestry remote sensing change monitoring,However,there is an irreconcilable contradiction between spatial and temporal resolution of remote sensing images acquired by aparticular sensor.Focusing on the cloudy and foggy region lacked of remote sensing data with highspatial and temporal resolution in Shennongjia forest district,a method of constructing vegetation fractional coverage with high spatial and temporal resolution,on region scale has been proposed,in this paper.Firstly,obtained the high resolution NDVI data by preprocessing LandSat8 data,and preprocessed MODIS NDVI data,including re projection,Resampling,etc;Secondly,Used the STARFM model to predict the high resolution NDVI,then selected the best algorithm parameters by evaluation factors,and calculated the vegetation fractional coverage based on Binary Pixel Model;After that,evaluated the accuracy between the real data and forecast data.Last but not least,selected the growth season data of Northwest Xingan ridge in Heilongjiang to validated the experiment.The results show that:by using this method,the better NDVI and vegetation fractional coverage can be obtained in Shennongjia forest district,with the accuracies of 90.8%and 82.60%respectively.In addition,through the verification test,we can get better NDVI and vegetation fractional coverage of the small Lesser Khingan Range in the same growing season,and the precision reaches 92.86%,88.65%.
High spatial and temporal resolution remote sensing data plays an important role in forestry remote sensing change monitoring,However,there is an irreconcilable contradiction between spatial and temporal resolution of remote sensing images acquired by aparticular sensor.Focusing on the cloudy and foggy region lacked of remote sensing data with highspatial and temporal resolution in Shennongjia forest district,a method of constructing vegetation fractional coverage with high spatial and temporal resolution,on region scale has been proposed,in this paper.Firstly,obtained the high resolution NDVI data by preprocessing LandSat8 data,and preprocessed MODIS NDVI data,including re projection,Resampling,etc;Secondly,Used the STARFM model to predict the high resolution NDVI,then selected the best algorithm parameters by evaluation factors,and calculated the vegetation fractional coverage based on Binary Pixel Model;After that,evaluated the accuracy between the real data and forecast data.Last but not least,selected the growth season data of Northwest Xingan ridge in Heilongjiang to validated the experiment.The results show that:by using this method,the better NDVI and vegetation fractional coverage can be obtained in Shennongjia forest district,with the accuracies of 90.8%and 82.60%respectively.In addition,through the verification test,we can get better NDVI and vegetation fractional coverage of the small Lesser Khingan Range in the same growing season,and the precision reaches 92.86%,88.65%.
引文
[1]陈阳,范建容,张云,等.多云雾地区高时空分辨率植被覆盖度构建方法研究[J].遥感技术与应用,2016,31(3):518-529.
[2]赵永光,黄波,汪超亮.基于超分辨率重建的多时相MODIS与Landsat反射率融合方法[J].遥感学报,2013,17(3):590-608.
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[4]谢登峰,张锦水,孙佩军,等.结合像元分解和STAR-FM模型的遥感数据融合[J].遥感学报,2016,20(1):62-72.
[5]李玉东,黄永喜.三种TM与MODIS数据融合方法在山区的适用性研究[J].测绘与空间地理信息,2013(9):124-127.
[6]Gao F,Masek J,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience&Remote Sensing,2006,44(8):2207-2218.
[7]邬明权,牛铮,王长耀.多源遥感数据时空融合模型应用分析[J].地球信息科学学报,2014,16(5):776-783.
[2]赵永光,黄波,汪超亮.基于超分辨率重建的多时相MODIS与Landsat反射率融合方法[J].遥感学报,2013,17(3):590-608.
[3]赵艳丽,李大成,贾琇明,等.STARFM算法生成湿地类型TM反射率数据的应用评价[J].计算机应用与软件,2016,33(3):267-270.
[4]谢登峰,张锦水,孙佩军,等.结合像元分解和STAR-FM模型的遥感数据融合[J].遥感学报,2016,20(1):62-72.
[5]李玉东,黄永喜.三种TM与MODIS数据融合方法在山区的适用性研究[J].测绘与空间地理信息,2013(9):124-127.
[6]Gao F,Masek J,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience&Remote Sensing,2006,44(8):2207-2218.
[7]邬明权,牛铮,王长耀.多源遥感数据时空融合模型应用分析[J].地球信息科学学报,2014,16(5):776-783.