联合主被动遥感数据定量评价林下植被对叶面积指数估算的影响
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摘要
天然森林具有冠层和林下植被(即灌丛、草地)的垂直立体结构,准确、定量地分离林下植被对于改善森林冠层叶面积指数反演精度具有重要的科学意义和实用价值。传统被动光学遥感数据由于在直接获取三维信息方面存在局限性,联合主被动的航空激光雷达(ALS)和高光谱数据(HyMap),以美国华盛顿州植物园为重点研究区,首先在单木分割的基础上实现了森林的垂直分层(即森林冠层和林下植被层)。在此基础上,利用森林冠层激光点云数据对光学影像数据进行林下植被信息剔除。通过对比利用航空光学影像和地面实测得到的森林有效叶面积指数结果发现:①森林郁闭度对于ALS数据的穿透性具有显著影响;②去除林下植被信息能够有效改善森林冠层有效叶面积指数(LAIe)估算精度。通过剔除林下植被信息,植被指数(NDVI)与地面实测有效叶面积指数的相关性由0.087提升到0.591。此外,基于剔除林下植被信息的光学遥感影像,与简单比值植被指数(SR)(相关性由0.209提升到0.559)和简化简单比例植被指数(RSR)(相关性由0.147提升到0.358)相比,归一化植被指数(NDVI)对冠层叶面积指数的变化最为敏感(相关性提高0.5)。本研究所提出的联合主被动遥感数据定量分离林下植被的方法能够有效地改善森林冠层叶面积指数的反演精度,为准确定量地估算森林生物物理参数和研究碳、水循环过程提供坚实的基础。
Natural forests have the vertical three-dimensional structure of canopy and understory vegetation(shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest(forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that:(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index(NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index(SR)(the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index(RSR)(the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index(correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.
Natural forests have the vertical three-dimensional structure of canopy and understory vegetation(shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest(forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that:(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index(NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index(SR)(the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index(RSR)(the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index(correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.
引文
[1] Shugart H H,Saatchi S,Hall F G.Importance of Structure and Its Measurement in Quantifying Function of Forest Ecosystems[J].Journal of Geophysical Research Biogeosciences,2015,115:333-345.
[2] Jiao Tong,Liu Ronggao,Liu Yang,et al.The Progress of Forest Understory Retrieve from Remote Sensing[J].Journal of Geo-Information Scicence,2014,16:602-608.[焦桐,刘荣高,刘洋,等.林下植被遥感反演研究进展[J].地球信息科学学报,2014,16:602-608.]
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[4] Chen J M.Black T A.Defining Leaf Area Index for Non-Flat Leave[J].Plant Cell and Environment,2010,15:421-429.
[5] Cleugh H A,Leuning R,Mu Q,et al.Regional Evaporation Estimates from Flux Tower and MODIS Satellite Data[J].Remote Sensing of Environment,2007,106:285-304.
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[7] Bonan G B.Importance of Leaf Area Index and Forest Type When Estimating Photosynthesis in Boreal Forests[J].Remote Sensing of Environment,1993,43:303-314.
[8] Zhao Jing,Li Jing,Liu Qinhuo.Review of Forest Vertical Structure Parameter Inversion based on Remote Sensing Technology[J].Journal of Remote Sensing,2013,17:697-716.[赵静,李静,柳钦火.森林垂直结构参数遥感反演综述[J].遥感学报,2013,17:697-716.]
[9] Ganguly S,Nemani R R,Zhang G,et al.Generating Global Leaf Area Index from Landsat:Algorithm Formulation and Demonstration[J].Remote Sensing of Environment,2012,122:185-202.
[10] Pisek J,Chen J M.Mapping Forest Background Reflectivity over North America with Multi-angle Imaging Spectroradiometer (MISR) Data[J].Remote Sensing of Environment,2009,113:2412-2423.
[11] Ma Jiande.Impact of Forest Background Reflectance on the Canopy Leaf Area Index Inversion[D].Nanjing:Nanjing Univerisity,2013.[马健德,森林背景反射率对冠层叶面积指数反演的影响研究[D].南京:南京大学,2013.]
[12] Deering D W,Eck T F.Banerjee B.Characterization of the Reflectance Anisotropy of Three Boreal Forest Canopies in Spring-Summer[J].Remote Sensing of Environment,1999,67:205-229.
[13] Chen J M.Cihlar J.Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images[J].Remote Sensing of Environment,1996,55:153-162.
[14] Deng F,Chen J M,Plummer S,et al.Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44:2219-2229.
[15] Heiskanen J,Rautiainen M,Stenberg P,et al.Seasonal Variation in MODIS LAI for A Boreal Forest Area in Finland[J].Remote Sensing of Environment,2012,126:104-115.
[16] Tong J,Ronggao L,Yang L,et al.Mapping Global Seasonal Forest Background Reflectivity with Multi-Angle Imaging Spectroradiometer Data[J].Journal of Geophysical Research:Biogeosciences,2014,119(6):1063-1077.
[17] Spanner M,Pierce L,Peterson D.et al.Remote Sensing of Temperate Coniferous Forest Leaf Area Index the Influence of Canopy Closure,Understory Vegetation and Background Reflectance[J].International Journal of Remote Sensing,1990,11:95-111.
[18] Baret F.Guyot G.Potentials and Limits of Vegetation Indices for LAI and APAR Assessment[J].Remote Sensing of Environment,1991,35:161-173.
[19] Wulder M A,Ledrew E F,Franklin S E.et al.Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI)[J].Remote Sensing of Environment,1998,64:64-76.
[20] Peduzzi A,Wynne R H,Fox T R,et al.Estimating Leaf Area Index in Intensively Managed Pine Plantations Using Airborne Laser Scanner Data[J].2012,270:1-65.
[21] Solberg S.Mapping Gap Fraction,LAI and Defoliation Using Various ALS Penetration Variables[J].International Journal of Remote Sensing,2010,31:1227-1244.
[22] Riano D,Valladares F,Condes S.et al.Estimation of Leaf Area Index and Covered Ground from Airborne Laser Scanner (LiDAR) in Two Contrasting Forests[J].Agricultural & Forest Meteorology,2004,124:269-275.
[23] Morsdorf F,K?tz B,Meier E,et al.Estimation of LAI and Fractional Cover from Small Footprint Airborne Laser Scanning Data based on Gap Fraction[J].Remote Sensing of Environment,2006,104:50-61.
[24] Richardson J J,Moskal L M.Kim S H.Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LiDAR[J].Agricultural & Forest Meteorology,2009,149:1152-1160.
[25] Tang H,Dubayah R,Swatantran A,et al.Retrieval of Vertical LAI Profiles over Tropical Rain Forests Using Waveform LiDAR at La Selva,Costa Rica[J].Remote Sensing of Environment,2012,124:242-250.
[26] Hu R,Yan G,Nerry F,et al.Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index[J].IEEE Transactions on Geoscience & Remote Sensing,2018,56(6):3196-3209.
[27] Lu Lu,Zheng Guang,Ma Lixia.Combing Point Cloud Slicing and Terrestrial Laser Scanning Data to Retrieve an Effective Leaf Area Index[J].Journal of Remote Sening,2018,22(3):432-449.[路璐,郑光,马利霞.激光雷达和点云切片算法结合的森林有效叶面积指数估算[J].遥感学报,2018,22(3):432-449.]
[28] Lee K S.Hyperspectral Versus Multispectral Data for Estimating Leaf Area Index in Four Different Biomes[J].Remote Sensing of Environment,2004,91(3):508-520.
[29] Luo Siezhou,Wang Cheng,Zhang Guibing,et al.Forest Leaf Area Index (LAI) Inversion Using Airborne LiDAR Data.[J].Chinese Journal of Geophysics,2013,56(5):1467-1475.[骆社周,王成,张贵宾,等.机载激光雷达森林叶面积指数反演研究[J].地球物理学报,2013,56(5):1467-1475.]
[30] Hui Jinxi.Leaf Area Index Inversion of the Broadleaf Shrub based on the Synergy of the Hyperspectral and Terrestral LiDAR Data[D].Chengdu:University of Electronic Science and Technology of China,2016.[惠金喜.基于高光谱和LiDAR数据协同的阔叶灌木LAI反演[D].成都:电子科技大学,2016.]
[31] Kraus K,Pfeifer N.Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data[J].ISPRS Journal of Photogrammetry & Remote Sensing,1998,53:193-203.
[32] Li W,Guo Q,Jakubowski M K.et al.A New Method for Segmenting Individual Trees from the LiDAR Point Cloud[J].Photogrammetric Engineering & Remote Sensing,2015,78:75-84.
[2] Jiao Tong,Liu Ronggao,Liu Yang,et al.The Progress of Forest Understory Retrieve from Remote Sensing[J].Journal of Geo-Information Scicence,2014,16:602-608.[焦桐,刘荣高,刘洋,等.林下植被遥感反演研究进展[J].地球信息科学学报,2014,16:602-608.]
[3] Food and Agriculture Organization of the United Nations (FAO).Global Forest Resources Assessment Update:Terms and Definitions(Final Version)[S].Rome:Forest Resources Assessment Programme,2003:1-34.
[4] Chen J M.Black T A.Defining Leaf Area Index for Non-Flat Leave[J].Plant Cell and Environment,2010,15:421-429.
[5] Cleugh H A,Leuning R,Mu Q,et al.Regional Evaporation Estimates from Flux Tower and MODIS Satellite Data[J].Remote Sensing of Environment,2007,106:285-304.
[6] Leuning R,Cleugh H A,Zegelin S J,et al.Carbon and Water Fluxes over A Temperate Eucalyptus Forest and A Tropical Wet/Dry Savanna in Australia:Measurements and Comparison with MODIS Remote Sensing Estimates[J].Agricultural & Forest Meteorology,2005,129:151-173.
[7] Bonan G B.Importance of Leaf Area Index and Forest Type When Estimating Photosynthesis in Boreal Forests[J].Remote Sensing of Environment,1993,43:303-314.
[8] Zhao Jing,Li Jing,Liu Qinhuo.Review of Forest Vertical Structure Parameter Inversion based on Remote Sensing Technology[J].Journal of Remote Sensing,2013,17:697-716.[赵静,李静,柳钦火.森林垂直结构参数遥感反演综述[J].遥感学报,2013,17:697-716.]
[9] Ganguly S,Nemani R R,Zhang G,et al.Generating Global Leaf Area Index from Landsat:Algorithm Formulation and Demonstration[J].Remote Sensing of Environment,2012,122:185-202.
[10] Pisek J,Chen J M.Mapping Forest Background Reflectivity over North America with Multi-angle Imaging Spectroradiometer (MISR) Data[J].Remote Sensing of Environment,2009,113:2412-2423.
[11] Ma Jiande.Impact of Forest Background Reflectance on the Canopy Leaf Area Index Inversion[D].Nanjing:Nanjing Univerisity,2013.[马健德,森林背景反射率对冠层叶面积指数反演的影响研究[D].南京:南京大学,2013.]
[12] Deering D W,Eck T F.Banerjee B.Characterization of the Reflectance Anisotropy of Three Boreal Forest Canopies in Spring-Summer[J].Remote Sensing of Environment,1999,67:205-229.
[13] Chen J M.Cihlar J.Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images[J].Remote Sensing of Environment,1996,55:153-162.
[14] Deng F,Chen J M,Plummer S,et al.Algorithm for Global Leaf Area Index Retrieval Using Satellite Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44:2219-2229.
[15] Heiskanen J,Rautiainen M,Stenberg P,et al.Seasonal Variation in MODIS LAI for A Boreal Forest Area in Finland[J].Remote Sensing of Environment,2012,126:104-115.
[16] Tong J,Ronggao L,Yang L,et al.Mapping Global Seasonal Forest Background Reflectivity with Multi-Angle Imaging Spectroradiometer Data[J].Journal of Geophysical Research:Biogeosciences,2014,119(6):1063-1077.
[17] Spanner M,Pierce L,Peterson D.et al.Remote Sensing of Temperate Coniferous Forest Leaf Area Index the Influence of Canopy Closure,Understory Vegetation and Background Reflectance[J].International Journal of Remote Sensing,1990,11:95-111.
[18] Baret F.Guyot G.Potentials and Limits of Vegetation Indices for LAI and APAR Assessment[J].Remote Sensing of Environment,1991,35:161-173.
[19] Wulder M A,Ledrew E F,Franklin S E.et al.Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI)[J].Remote Sensing of Environment,1998,64:64-76.
[20] Peduzzi A,Wynne R H,Fox T R,et al.Estimating Leaf Area Index in Intensively Managed Pine Plantations Using Airborne Laser Scanner Data[J].2012,270:1-65.
[21] Solberg S.Mapping Gap Fraction,LAI and Defoliation Using Various ALS Penetration Variables[J].International Journal of Remote Sensing,2010,31:1227-1244.
[22] Riano D,Valladares F,Condes S.et al.Estimation of Leaf Area Index and Covered Ground from Airborne Laser Scanner (LiDAR) in Two Contrasting Forests[J].Agricultural & Forest Meteorology,2004,124:269-275.
[23] Morsdorf F,K?tz B,Meier E,et al.Estimation of LAI and Fractional Cover from Small Footprint Airborne Laser Scanning Data based on Gap Fraction[J].Remote Sensing of Environment,2006,104:50-61.
[24] Richardson J J,Moskal L M.Kim S H.Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LiDAR[J].Agricultural & Forest Meteorology,2009,149:1152-1160.
[25] Tang H,Dubayah R,Swatantran A,et al.Retrieval of Vertical LAI Profiles over Tropical Rain Forests Using Waveform LiDAR at La Selva,Costa Rica[J].Remote Sensing of Environment,2012,124:242-250.
[26] Hu R,Yan G,Nerry F,et al.Using Airborne Laser Scanner and Path Length Distribution Model to Quantify Clumping Effect and Estimate Leaf Area Index[J].IEEE Transactions on Geoscience & Remote Sensing,2018,56(6):3196-3209.
[27] Lu Lu,Zheng Guang,Ma Lixia.Combing Point Cloud Slicing and Terrestrial Laser Scanning Data to Retrieve an Effective Leaf Area Index[J].Journal of Remote Sening,2018,22(3):432-449.[路璐,郑光,马利霞.激光雷达和点云切片算法结合的森林有效叶面积指数估算[J].遥感学报,2018,22(3):432-449.]
[28] Lee K S.Hyperspectral Versus Multispectral Data for Estimating Leaf Area Index in Four Different Biomes[J].Remote Sensing of Environment,2004,91(3):508-520.
[29] Luo Siezhou,Wang Cheng,Zhang Guibing,et al.Forest Leaf Area Index (LAI) Inversion Using Airborne LiDAR Data.[J].Chinese Journal of Geophysics,2013,56(5):1467-1475.[骆社周,王成,张贵宾,等.机载激光雷达森林叶面积指数反演研究[J].地球物理学报,2013,56(5):1467-1475.]
[30] Hui Jinxi.Leaf Area Index Inversion of the Broadleaf Shrub based on the Synergy of the Hyperspectral and Terrestral LiDAR Data[D].Chengdu:University of Electronic Science and Technology of China,2016.[惠金喜.基于高光谱和LiDAR数据协同的阔叶灌木LAI反演[D].成都:电子科技大学,2016.]
[31] Kraus K,Pfeifer N.Determination of Terrain Models in Wooded Areas with Airborne Laser Scanner Data[J].ISPRS Journal of Photogrammetry & Remote Sensing,1998,53:193-203.
[32] Li W,Guo Q,Jakubowski M K.et al.A New Method for Segmenting Individual Trees from the LiDAR Point Cloud[J].Photogrammetric Engineering & Remote Sensing,2015,78:75-84.