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融合数据在草地生物量估算中的应用

DOI: 10.6046/gtzyyg.2013.04.24, PP. 147-154

Keywords: 数据融合,NDVI,锡林浩特,生物量估算模型

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Abstract:

为实现草地生物量的实时高精度监测,将时空适应性反射率融合模型(spatialandtemporaladaptivereflectancefusionmodel,STARFM)融合后的数据引入到草地生物量估算模型中,以提高该模型的精度。以内蒙古锡林浩特市为研究区,首先采用STARFM融合MODIS和LandsatTM数据,同时对比分析反射率和NDVI输入数据的融合效果,认为直接融合NDVI数据得到的高分辨率NDVI产品的精度更高;然后,基于融合后高精度的NDVI数据与实测生物量建立多种生物量估算模型;通过统计比较得到最优生物量估算模型——指数模型;最后,基于融合后NDVI与原始MODISNDVI数据分别作为自变量建立指数模型,以验证融合数据提高生物量估算模型精度的能力。研究表明,基于融合后NDVI的生物量估算模型决定系数R2由0.761提高到0.832,均方根误差由32.521g/m2降低到28.653g/m2,证明融合NDVI数据提高了生物量估算模型的精度。

References

[1]  朴世龙,方精云,贺金生,等.中国草地植被生物量及其空间分布格局[J].植物生态学报,2004,28(4):491-498. Piao S L,Fang J Y,He J S,et al.Spatial distribution of grassland biomass in China[J].Acta Phytoecologica Sinica,2004,28(4):491-498.
[2]  Zhu X L,Chen J,Gao F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote Sensing of Environment,2010,114(11):2610-2623.doi:10.1016/j.rse.2010.05.032.
[3]  Walker J J,de Beurs K M,Wynne R H,et al.Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology[J].Remote Sensing of Environment,2012,117:381-393.doi:10.1016/j.rse.2011.10.014.
[4]  杨英莲,邱新法,殷青军.基于MODIS增强型植被指数的青海省牧草产量估产研究[J].气象,2007,33(6):102-107. Yang Y L,Qiu X F,Yin Q J.Study on monitoring system of Qinghai grassland output based MODIS EVI data[J].Meteorological Monthly,2007,33(6):102-107.
[5]  李素英,李晓兵,莺歌,等.基于植被指数的典型草原区生物量模型:以内蒙古锡林浩特市为例[J].植物生态学报,2007,31(1):23-31. Li S Y,Li X B,Ying G,et al.Vegetation indexes biomass models for typical semi_arid steppe:A case study for Xilinhot in northern China[J].Journal of Plant Ecology,2007,31(1):23-31.
[6]  Xie Y C,Sha Z Y,Yu M,et al.A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China[J].Ecological Modelling,2009,220(15):1810-1818.
[7]  李建龙,蒋平.遥感技术在大面积天然草地估产和预报中的应用探讨[J].武汉测绘科技大学学报,1998,23(2):153-158. Li J L,Jiang P.The study on the remote sensing technology in estimating and forecasting grassland field applications[J].Journal of Wuhan Technical University of Surveying and Mapping,1998,23(2):153-158.
[8]  查勇,Gao J,倪绍祥.国际草地资源遥感研究新进展[J].地理科学进展,2003,22(6):607-617. Zha Y,Gao J,Ni S X.Most recent progress of international research on remote sensing of grassland resources[J].Progress in Geography,2003,22(6):607-617.
[9]  程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008,20(1):13-18. Cheng H F,Zhang W B,Chen F.Advances in researches on application of remote sensing method to estimating vegetation coverage[J].Remote Sensing for Land and Resources,2008,20(1):13-18.
[10]  Todd S W,Hoffer R M,Milchunas D G.Biomass estimation on grazed and ungrazed rangelands using spectral indices[J].International Journal of Remote Sensing,1998,19(3):427-438.
[11]  张连义,宝路如,尔敦扎玛,等.锡林郭勒盟草地植被生物量遥感监测模型的研究[J].中国草地学报,2008,30(1):6-14. Zhang L Y,Bao L R,Erdun Z M,et al.Research on remote sensing models for monitoring grassland vegetation biomass in Xilinguole[J].Chinese Journal of Grassland,2008,30(1):6-14.
[12]  Boelman N T,Stieglitz M,Rueth H M,et al.Response of NDVI,biomass,and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra[J].Oecologia,2003,135(3):414-421.doi:10.1007/s00442-003-1198-3.
[13]  Asner G P.Cloud cover in Landsat of observations of the Brazilian Amazon[J].International Journal of Remote Sensing,2001,22(18):3855-3862.
[14]  Jorgensen P V.Determination of cloud coverage over Denmark using Landsat MSS/TM and NOAA-AVHRR[J].International Journal of Remote Sensing,2000,21(17):3363-3368.doi:10.1080/014311600750019976.
[15]  Ju J C,Roy D P.The availability of cloud-free Landsat ETM plus data over the conterminous United States and globally[J].Remote Sensing of Environment,2008,112(3):1196-1211.
[16]  Price J C.How unique are spectral signatures?[J].Remote Sensing of Environment,1994,49(3):181-186.
[17]  Gao F,Masek J G,Schwaller M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218.
[18]  Hilker T,Wulder M A,Coops N C,et al.Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model[J].Remote Sensing of Environment,2009,113(9):1988-1999.
[19]  Devendra S.Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data[J].International Journal of Applied Earth Observation and Geoinformation,2011,13(1):59-69.
[20]  Watts J D,Powell S L,Lawrence R L,et al.Improved classification of conservation tillage adoption using high temporal and synthetic satellite imagery[J].Remote Sensing of Environment,2011,115(1):66-75.

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