基于混合像元分解方法的康保县植被覆盖度估测
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摘要
以康保县为研究区,使用Landsat8 OLI数据,结合134个野外样地调查数据,通过像元二分法、完全约束最小二乘法和随机森林三种方法来进行混合像元的分解,探讨混合像元分解方法对提取地表植被覆盖度的可行性。结果显示:基于随机森林的混合像元分解方法结果最优,R2为0. 664,RMSE为0. 127;三种方法都能较为精确地估测出康保县的植被覆盖情况,且与实测数据较为拟合,因此采用混合像元分解的方法进行植被覆盖度估测是完全可行的。
This paper focused on the vegetation coverage,using the Landsat8 OLI data for Kangbao County,Hebei Province.In this paper,134 sample plots were systematically selected in Kangbao County and vegetation coverage data were collected.In addition,the pixel binary method,the completely constrained least square method and the random forest method were conducted to spectral unmixing analysis to explore the feasibility of extracting vegetation coverage by spectral unmixing analysis.The results showed that:the spectral unmixing analysis method based on random forest model was the best,with R2 coefficient 0.664 and RMSE coefficient0.127.The three methods could all accurately estimate the vegetation coverage in Kangbao County and the estimations were close to the actual data.Therefore,it was feasible to estimate the vegetation coverage by the method of the spectral unmixing analysis.
This paper focused on the vegetation coverage,using the Landsat8 OLI data for Kangbao County,Hebei Province.In this paper,134 sample plots were systematically selected in Kangbao County and vegetation coverage data were collected.In addition,the pixel binary method,the completely constrained least square method and the random forest method were conducted to spectral unmixing analysis to explore the feasibility of extracting vegetation coverage by spectral unmixing analysis.The results showed that:the spectral unmixing analysis method based on random forest model was the best,with R2 coefficient 0.664 and RMSE coefficient0.127.The three methods could all accurately estimate the vegetation coverage in Kangbao County and the estimations were close to the actual data.Therefore,it was feasible to estimate the vegetation coverage by the method of the spectral unmixing analysis.
引文
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[25]王光镇,王静璞,邹学勇,等.基于像元三分模型的锡林郭勒草原光合植被和非光合植被覆盖度估算[J].生态学报,2017,37(17):5722-5731.
[26]姬翠翠,贾永红,李晓松,等.线性/非线性光谱混合模型估算白刺灌丛植被覆盖度[J].遥感学报,2016,20(6):1402-1412.
[27]Kuusinen N,Tomppo E,Berninger F.Linear unmixing of MODISalbedo composites to infer subpixel land cover type albedos[J].International Journal of Applied Earth Observation&Geoinformation,2013,23(23):324-333.
[28]黄艳妮,查良松,陈健.基于线性光谱混合模型的混合像元分解研究---以合肥市为例[J].安徽师范大学学报:自然科学版,2012,35(3):258-263.
[29]李涛,李晓松,李飞.基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算[J].生态学报,2015,35(11):3643-3652.
[30]朱怀朝.带约束的最小二乘求解算法及其在高光谱遥感混合像元分解中的应用[D].成都:成都理工大学,2012.
[31]Cao C,Chen W,Li G,et al.The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis[J].Canadian Journal of Remote Sensing,2012,37(4):348-358.
[32]Heinz DC,Chang CI.Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001.39(3):529-545.
[33]雷震.随机森林及其在遥感影像处理中应用研究[D].上海:上海交通大学,2012.
[34]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57,74.
[35]马晓东.基于加权决策树的随机森林模型优化[D].武汉:华中师范大学,2017.
[2]张云霞,李晓兵,陈云浩.草地植被盖度的多尺度遥感与实地测量方法综述[J].地球科学进展,2003,18(1):85-93.
[3]汪明霞,王卫东.植被覆盖度的提取方法研究综述[J].黄河水利职业技术学院学报,2013,25(2):23-26+31.
[4]秦伟,朱清科,张学霞,李文华,方斌.植被覆盖度及其测算方法研究进展[J].西北农林科技大学学报:自然科学版,2006,34(9):163-170.
[5]罗朝沁.MODIS影像森林类型快速提取关键技术研究[D].长沙:中南林业科技大学,2016.
[6]Jiapaer G,Chen X,Bao A.A comparison of methods for estimating fractional vegetation cover in arid regions[J].Agricultural&Forest Meteorology,2011,151(12):1698-1710.
[7]Xing ZR,Feng YG,Yang GJ,et al.Method of Estimating Vegetation Coverage Based on Remote Sensing[J].Remote Sensing Technology&Application,2009,24(6):849-854.
[8]孙华,鞠洪波,张怀清,林辉,凌成星.三种回归分析方法在Hyperion影像LAI反演中的比较[J].生态学报,2012,32(24):7781-7790.
[9]包刚,包玉海,覃志豪,等.高光谱植被覆盖度遥感估算研究[J].自然资源学报,2013,28(7):1243-1254.
[10]Wang A,Zhao Z.Research Advances on the Method of Estimating Vegetation Coverage Based on Remote Sensing[J].Journal of Green Science&Technology,2015,55(3):10-14.
[11]Lukina EV,Stone ML,Raun WR.Estimating vegetation coverage in wheat using digital images[J].Journal of Plant Nutrition,1999,22(2):341-350.
[12]陈晋,马磊,陈学泓,饶玉晗.混合像元分解技术及其进展[J].遥感学报,2016,20(5):1102-1109.
[13]Xiao J,Moody A.A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico,USA[J].Remote Sensing of Environment,2005,98(2-3):237-250.
[14]胡茂桂,王劲峰.遥感影像混合像元分解及超分辨率重建研究进展[J].地理科学进展,2010,29(06):747-756.
[15]马中刚.康保县荒漠化地区植被覆盖度遥感反演研究[D].长沙:中南林业科技大学,2016.
[16]徐晓雨,孙华,王广兴,等.基于GF-1与Landsat-8的康保县叶面积指数遥感反演研究[J].中南林业科技大学学报,2018,38(1):43-48.
[17]李苗苗.植被覆盖度的遥感估算方法研究[D].北京:中国科学院研究生院:遥感应用研究所,2003.
[18]Guerschman JP,Hill MJ,Renzullo LJ,et al.Estimating fractional cover of photosynthetic vegetation,non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors[J].Remote Sensing of Environment,2009,113(5):928-945.
[19]Yue YM,Wang KL,Zhang B,et al.Remote sensing of fractional cover of vegetation and exposed bedrock for karst rocky desertification assessment[J].Procedia Environmental Sciences,2012,13(10):847-853.
[20]Obata K,Miura T,Yoshioka H.Analysis of the Scaling Effects in the Area-Averaged Fraction of Vegetation Cover Retrieved Using an NDVI-Isoline-Based Linear Mixture Model[J].Remote Sensing,2012,4(7):2156-2180.
[21]Ding Y,Zheng X.Comparison of fractional vegetation cover estimations using dimidiate pixel models and look-up table inversions of the PROSAIL model from Landsat 8 OLI data[J].Journal of Applied Remote Sensing,2016,10(3):036022.
[22]Taniguchi K,Miura M,Obata K,et al.Analysis of the scaling effect present in the relative differences between NDVIs obtained from multiple sensors,based on the soil isoline equation[C]//International Geoscience and Remote Sensing Symposium.IEEE,2017:3401-3404.
[23]Yang J,Weisberg PJ,Bristow NA.Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semiarid woodlands:Comparison of vegetation indices and spectral mixture analysis[J].Remote Sensing of Environment,2012,119(8):62-71.
[24]Jaebeom K,Jang K,Kang S.Comparison of fractional vegetation cover derived from digital camera and MODIS NDVI in Mongolia[C]//AGU Fall Meeting.AGU Fall Meeting Abstracts,2014.
[25]王光镇,王静璞,邹学勇,等.基于像元三分模型的锡林郭勒草原光合植被和非光合植被覆盖度估算[J].生态学报,2017,37(17):5722-5731.
[26]姬翠翠,贾永红,李晓松,等.线性/非线性光谱混合模型估算白刺灌丛植被覆盖度[J].遥感学报,2016,20(6):1402-1412.
[27]Kuusinen N,Tomppo E,Berninger F.Linear unmixing of MODISalbedo composites to infer subpixel land cover type albedos[J].International Journal of Applied Earth Observation&Geoinformation,2013,23(23):324-333.
[28]黄艳妮,查良松,陈健.基于线性光谱混合模型的混合像元分解研究---以合肥市为例[J].安徽师范大学学报:自然科学版,2012,35(3):258-263.
[29]李涛,李晓松,李飞.基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算[J].生态学报,2015,35(11):3643-3652.
[30]朱怀朝.带约束的最小二乘求解算法及其在高光谱遥感混合像元分解中的应用[D].成都:成都理工大学,2012.
[31]Cao C,Chen W,Li G,et al.The retrieval of shrub fractional cover based on a geometric-optical model in combination with linear spectral mixture analysis[J].Canadian Journal of Remote Sensing,2012,37(4):348-358.
[32]Heinz DC,Chang CI.Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001.39(3):529-545.
[33]雷震.随机森林及其在遥感影像处理中应用研究[D].上海:上海交通大学,2012.
[34]张晓羽,李凤日,甄贞,等.基于随机森林模型的陆地卫星-8遥感影像森林植被分类[J].东北林业大学学报,2016,44(6):53-57,74.
[35]马晓东.基于加权决策树的随机森林模型优化[D].武汉:华中师范大学,2017.
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