基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究
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摘要
近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。
In recent years,wetland vegetation degradation has always been a global concern.It is particularly important to invert the vegetation coverage and study its temporal and spatial distribution characteristics.In order to solve the problem of mixed pixels in vegetation inversion,this paper proposes an object-oriented multi-end element spectrum hybrid analysis method.Taking Zhalong Wetland Reserve as the research object,middle-high resolution Landsat imagery is the data source,and the characteristics of spatio-temporal changes of wetland vegetation are studied from the perspective of time scale and vegetation cover level change.The characteristics of spatio-temporal changes of wetland vegetation were studied from the perspective of temporal scale and vegetation coverage level change.The results show that the object-oriented multiterminal hybrid model effectively reduces the computational complexity and the variation of the end-cells of mixed pixels,and the correlation between the inversion value and the test value is high,and the root-meansquare error is small,which is superior to the traditional multi-terminal The hybrid model method improves the accuracy of vegetation cover inversion.The vegetation coverage of Zhalong Wetland has been deteriorating for many years.The average rate of change from 2001 to 2017 is relatively faster than that of1985～2000.It has important theoretical significance for improving the prediction accuracy of vegetation transfer under global climate change scenarios.
In recent years,wetland vegetation degradation has always been a global concern.It is particularly important to invert the vegetation coverage and study its temporal and spatial distribution characteristics.In order to solve the problem of mixed pixels in vegetation inversion,this paper proposes an object-oriented multi-end element spectrum hybrid analysis method.Taking Zhalong Wetland Reserve as the research object,middle-high resolution Landsat imagery is the data source,and the characteristics of spatio-temporal changes of wetland vegetation are studied from the perspective of time scale and vegetation cover level change.The characteristics of spatio-temporal changes of wetland vegetation were studied from the perspective of temporal scale and vegetation coverage level change.The results show that the object-oriented multiterminal hybrid model effectively reduces the computational complexity and the variation of the end-cells of mixed pixels,and the correlation between the inversion value and the test value is high,and the root-meansquare error is small,which is superior to the traditional multi-terminal The hybrid model method improves the accuracy of vegetation cover inversion.The vegetation coverage of Zhalong Wetland has been deteriorating for many years.The average rate of change from 2001 to 2017 is relatively faster than that of1985～2000.It has important theoretical significance for improving the prediction accuracy of vegetation transfer under global climate change scenarios.
引文
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[15]Zhao Chunhui,Cui Shiling,Liu Wu.Multi-endmember Hierarchical Mixture Analysis Algorithm for Spectra[J].Journal of Optoelectronics·Laser,2014,25(9):1830-1836.[赵春晖,崔士玲,刘务.基于分层的多端元光谱解混算法[J].光电子·激光,2014,25(9):1830-1836.]
[16]Li Xiaoxue,An Ru,Wu Hong,et al.Extraction of the Vegetation Fraction based on an Improved Multiple Endmember Spectral Mixture Analysis for the Central and Eastern Area of Source Region of Yangtze,Yellow and Lantsang Rivers[J].Remote Sensing Technology and Application,2011,26(3):383-391.[李晓雪,安如,吴红,等.基于一种改进的多端元混合像元分解方法的三江源中东部地区植被盖度信息的提取[J].遥感技术与应用,2011,26(3):383-391.]
[17]Huang J,Jr R G P,Li Q,et al.Use of Intensity Analysis to Link Patterns with Processes of Land Change from 1986to2007in A Coastal Watershed of Southeast China[J].Applied Geography,2012,34:371-384.
[18]Cadonna A,Kottas A,Prado R.Bayesian Mixture Modeling for Spectral Density Estimation[J].Statistics&Probability Letters,2017,125:189-195.
[19]Lin Shangrong,Li Jing,Liu Qinhuo.Overview on Estimation Accuracy of Gross Primary Productivity with Remote Sensing Methods[J].Journal of Remote Sensing,2018,22(2):234-254.[林尚荣,李静,柳钦火.陆地总初级生产力遥感估算精度分析[J].遥感学报,2018,22(2):234-254.]
[20]Shao Weigeng,Han Qin,Liu Xinyu,et al.Ecological Response of Reeds to Fire in Zhalong Wetland[J].Protection Forest Science and Technology,2012,(3):58-60.[邵伟庚,韩勤,刘新宇,等.扎龙湿地芦苇对火烧的生态响应[J].防护林科技,2012,(3):58-60.]
[21]Sun Yunhua,Guo Tao,Cui Ximin.Intensity Analysis and Stationarity of Landuse Change in Kunming City[J].Progress in Geography,2016,35(2):245-254.[孙云华,郭涛,崔希民.昆明市土地利用变化的强度分析与稳定性研究[J].地理科学进展,2016,35(2):245-254.]
[2]Hu Y X,Huang J L,Du Y,et al.Monitoring Wetland Vegetation Pattern Response to Water-level Change Resulting from the Three Gorges Project in the Two Largest Freshwater Lakes of China[J].Ecological Engineering,2015,74:274-285.
[3]Zhao Jianyun,Zhang Xiaohua,Zhang Bo,et al.Analysis of SpatialTemporal Change of Vegetation Coverage of Three-River Source Region based on Landsat Data[J].Yellow River,2018,40(7):68-72.[赵健赟,张晓华,张波,等.基于Landsat的三江源区植被覆盖时空变化分析[J].人民黄河,2018,40(7):68-72.]
[4]Jia Kun,Yao Yunjun,Wei Xiangqin,et al.A Review on Fractional Vegetation Cover Estimation Using Remote Sensing[J].Advances in Earth Science,2013,28(7):774-782.[贾坤,姚云军,魏香琴,等.植被覆盖度遥感估算研究进展[J].地球科学进展,2013,28(7):774-782.]
[5]Agutu N O,Awange J L,Zerihun A,et al.Assessing Multi-satellite Remote Sensing,Reanalysis,and Land Surface Models'Products in Characterizing Agricultural Drought in East Africa[J].Remote Sensing of Environment,2017,194:287-302.
[6]Lawley V,Lewis M,Clarke K,et al.Site-based and Remote Sensing Methods for Monitoring Indicators of Vegetation Condition:An Australian Review[J].Ecological Indicators,2016,60:1273-1283.
[7]Xie Qiuxia,Sun Lin,Wei Jing,et al.Adaptive Evaluation of Vegetation Coverage Estimation in Arid Region based on Remote Sensing Technology[J].Chinese Journal of Ecology,2016,35(4):1117-1124.[谢秋霞,孙林,韦晶,等.基于遥感估算方法的干旱区植被覆盖度适应性评价[J].生态学杂志,2016,35(4):1117-1124.]
[8]Cui Tianxiang,Gong Zhaoning,Zhao Wenji,et al.Research on Estimating Wetland Vegetation Abundance based on Spectral Mixture Analysis with Different Endmember Model:A Case Study in Wild Duck Lake Wetland,Beijing[J].Acta Ecologica Sinica,2013,33(4):1160-1171.[崔天翔,宫兆宁,赵文吉,等.不同端元模型下湿地植被覆盖度的提取方法-以北京市野鸭湖湿地自然保护区为例[J].生态学报,2013,33(4):1160-1171.]
[9]Sousa D,Small C.Global Cross-calibration of Landsat Spectral Mixture Models[J].Remote Sensing of Environment,2017,192:139-149.
[10]Somers B,Asner G P,Tits L,et al.Endmember Variability in Spectral Mixture Analysis:A Review[J].Remote Sensing of Environment,2011,115(7):1603-1616.
[11]Liu Zhengchun,Lu Shuai,Zhang Hui.A Method of Multiple Endmember Spectral Mixture Analysis based on Object Oriented[J].Journal of Shanxi Agricultural University(Natural Science Edition),2015,35(2):202-208.[刘正春,卢帅,张辉.基于面向对象的多端元光谱混合分析方法[J].山西农业大学学报(自然科学版),2015,35(2):202-208.]
[12]Xu Xuezhe.The Home of the Red-crowned Crane-zhalong Wetland[J].Journal of Chinese Institute of Environmental Management,2015,25(5):91-92.[徐学哲.丹顶鹤之乡-扎龙湿地[J].中国环境管理干部学院学报,2015,25(5):91-92.]
[13]Qiao Yanwen,Zang Shuying,Na Xiaodong.The Information Extraction of Freshwater Marsh Wetland based on the Decision Tree Method:Taking Zhalong Wetland as an Example[J].Chinese Agricultural Science Bulletin,2013,29(8):169-174.[乔艳雯,臧淑英,那晓东.基于决策树方法的淡水沼泽湿地信息提取-以扎龙湿地为例[J].中国农学通报,2013,29(8):169-174.]
[14]Small C,Milesi C.Multi-scale Standardized Spectral Mixture Models[J].Remote Sensing of Environment,2013,136(5):442-454.
[15]Zhao Chunhui,Cui Shiling,Liu Wu.Multi-endmember Hierarchical Mixture Analysis Algorithm for Spectra[J].Journal of Optoelectronics·Laser,2014,25(9):1830-1836.[赵春晖,崔士玲,刘务.基于分层的多端元光谱解混算法[J].光电子·激光,2014,25(9):1830-1836.]
[16]Li Xiaoxue,An Ru,Wu Hong,et al.Extraction of the Vegetation Fraction based on an Improved Multiple Endmember Spectral Mixture Analysis for the Central and Eastern Area of Source Region of Yangtze,Yellow and Lantsang Rivers[J].Remote Sensing Technology and Application,2011,26(3):383-391.[李晓雪,安如,吴红,等.基于一种改进的多端元混合像元分解方法的三江源中东部地区植被盖度信息的提取[J].遥感技术与应用,2011,26(3):383-391.]
[17]Huang J,Jr R G P,Li Q,et al.Use of Intensity Analysis to Link Patterns with Processes of Land Change from 1986to2007in A Coastal Watershed of Southeast China[J].Applied Geography,2012,34:371-384.
[18]Cadonna A,Kottas A,Prado R.Bayesian Mixture Modeling for Spectral Density Estimation[J].Statistics&Probability Letters,2017,125:189-195.
[19]Lin Shangrong,Li Jing,Liu Qinhuo.Overview on Estimation Accuracy of Gross Primary Productivity with Remote Sensing Methods[J].Journal of Remote Sensing,2018,22(2):234-254.[林尚荣,李静,柳钦火.陆地总初级生产力遥感估算精度分析[J].遥感学报,2018,22(2):234-254.]
[20]Shao Weigeng,Han Qin,Liu Xinyu,et al.Ecological Response of Reeds to Fire in Zhalong Wetland[J].Protection Forest Science and Technology,2012,(3):58-60.[邵伟庚,韩勤,刘新宇,等.扎龙湿地芦苇对火烧的生态响应[J].防护林科技,2012,(3):58-60.]
[21]Sun Yunhua,Guo Tao,Cui Ximin.Intensity Analysis and Stationarity of Landuse Change in Kunming City[J].Progress in Geography,2016,35(2):245-254.[孙云华,郭涛,崔希民.昆明市土地利用变化的强度分析与稳定性研究[J].地理科学进展,2016,35(2):245-254.]