Progress and trends of application of hyperspectral remote sensing in plant diversity research
- Vol. 27, Issue 11, Pages: 2467-2483(2023)
Received:11 March 2021,
Published:07 November 2023
DOI: 10.11834/jrs.20211120
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Received:11 March 2021,
Published:07 November 2023
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人类活动、极端气候、物种入侵等事件导致植物的生物多样性丧失加剧,生物多样性保护迫切需要快速准确地收集陆地植物多样性信息。高光谱遥感的出现,为大空间尺度上的植物多样性研究提供了技术基础,为群落和景观水平的生物多样性相关理论的验证提供了契机。本文简要回顾了近年来高光谱遥感技术的发展及其在植物多样性研究中的应用。重点介绍了两类高光谱遥感反演多样性的手段,即直接反演和间接反演。直接反演手段以光谱变异假说为理论基础,从光谱曲线特征入手直接建立光谱信息与植物多样性的关系;间接反演手段则通过植被指数将光谱信息关联植物多样性,或通过定量反演功能性状计算功能多样性指标,进而实现植物多样性的间接估测。论文进一步结合实例,论述了高光谱遥感技术在大尺度生物多样性相关研究中的应用,如物种入侵监测、物种分布及多样性格局制图、生物多样性与生态系统功能关系研究。最后分析了高光谱遥感技术在生态研究应用中的局限性。随着多源遥感技术的发展日渐成熟,高光谱遥感技术与地面通量监测、激光雷达、计算机可视化等其他技术的协同应用可能是在生物多样性研究领域中一个新的发展方向。
Plant diversity is closely related to ecosystem productivity
stability
and resource use efficiency. The rate of plant biodiversity loss due to human activities
extreme climate
and species invasions is accelerating annually
and an urgent need is recognized for rapid and accurate collection of information of terrestrial plant diversity for biodiversity conservation.
Remote sensing techniques are important methods of earth observation from space. In recent years
image data from remote sensing have been developing toward refinement and comprehensiveness
and high-quality data covering more ground information have been gradually applied. The emergence of hyperspectral remote sensing technology enables sensors to collect continuous spectral curves of ground targets in fine spectral resolution
which consequently provides massive information of ground objects and realizes the quantitative inversion of ground object parameters. Hyperspectral remote sensing technology offers a technical basis for the large-scale observation of plant diversity and functional traits. It further brings opportunities for the verification of theories of community assembly with the continuous variation in spatial scales.
In this study
we review the development of hyperspectral remote sensing technology and its application in detecting plant diversity and functional traits. Two types of inversion approaches for quantifying biodiversity through hyperspectral remote sensing
namely
direct inversion and indirect inversion
are introduced. The direct inversion approach takes the spectral variation hypothesis (SVH) as its theoretical basis
which posits that biodiversity is linked to the heterogeneity of spectral image. The SVH-based approaches
also known as “spectral diversity metrics
” are to directly establish the relationship between spectral information and plant diversity. Common spectral diversity metrics include the coefficient of variation of spectral bands
the convex hull volume in spectral space
the spectral angle mapper
the divergence of spectral information
and the convex hull area. Numerous studies have proven that these spectral diversity metrics can be used to effectively track the variation in biodiversity indicators
such as species richness
Shannon index
and Rao’s
Q
index.
The indirect inversion approach correlates spectral information with plant diversity via quantitative remote sensing. Plant functional traits can be retrieved from hyperspectral image data through empirical and physically-based models with convincing accuracy. With the quantitative retrieved traits from image data
functional diversity indices
which can be closely linked to ecosystem functioning
such as FRic (functional richness)
FDiv (functional divergence)
and FEve (functional evenness)
can be characterized and spatially mapped. Studies also confirmed that the indirect approach can be employed to assess taxonomic and even phylogenetic diversity through the quantification of vegetation indices.
Combined with existing application examples
we then discuss the technical advantages of hyperspectral remote sensing technology in the studies on species invasion
species mapping
biodiversity spatial patterns
and the large-scale biodiversity and ecosystem functioning relationship. At the end of this review
limitations of the application of hyperspectral remote sensing technology in ecological studies are analyzed. With the development of multisource remote sensing technology
hyperspectral remote sensing coordinated with other technological means (e.g.
ground flux monitoring
laser radar technique
and computer visualization) will be applied more extensively in biodiversity-relevant studies.
Ali A M , Darvishzadeh R , Skidmore A K , Duren I v , Heiden U and Heurich M . 2016 . Estimating leaf functional traits by inversion of PROSPECT: assessing leaf dry matter content and specific leaf area in mixed mountainous forest . International Journal of Applied Earth Observation and Geoinformation , 45 : 66 - 76 [ DOI: 10.1016/j.jag.2015.11.004 http://dx.doi.org/10.1016/j.jag.2015.11.004 ]
Andrew M E and Ustin S L . 2008 . The role of environmental context in mapping invasive plants with hyperspectral image data . Remote Sensing of Environment , 112 ( 12 ): 4301 - 4317 [ DOI: 10.1016/j.rse.2008.07.016 http://dx.doi.org/10.1016/j.rse.2008.07.016 ]
Asner G P . 1998 . Biophysical and biochemical sources of variability in canopy reflectance . Remote Sensing of Environment , 64 ( 3 ): 234 - 253 [ DOI: 10.1016/S0034-4257(98)00014-5 http://dx.doi.org/10.1016/S0034-4257(98)00014-5 ]
Asner G P , Anderson C B , Martin R E , Tupayachi R , Knapp D E and Sinca F . 2015a . Landscape biogeochemistry reflected in shifting distributions of chemical traits in the Amazon forest canopy . Nature Geoscience , 8 ( 7 ): 567 - 573 [ DOI: 10.1038/ngeo2443 http://dx.doi.org/10.1038/ngeo2443 ]
Asner G P and Heidebrecht K B . 2002 . Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations . International Journal of Remote Sensing , 23 ( 19 ): 3939 - 3958 [ DOI: 10.1080/01431160110115960 http://dx.doi.org/10.1080/01431160110115960 ]
Asner G P , Jones M O , Martin R E , Knapp D E and Hughes R F . 2008 . Remote sensing of native and invasive species in hawaiian forests . Remote Sensing of Environment , 112 ( 5 ): 1912 - 1926 [ DOI: 10.1016/j.rse.2007.02.043 http://dx.doi.org/10.1016/j.rse.2007.02.043 ]
Asner G P and Martin R E . 2009 . Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests . Frontiers in Ecology and the Environment , 7 ( 5 ): 269 - 276 [ DOI: 10.1890/070152 http://dx.doi.org/10.1890/070152 ]
Asner G P and Martin R E . 2016 . Spectranomics: emerging science and conservation opportunities at the interface of biodiversity and remote sensing . Global Ecology and Conservation , 8 : 212 - 219 [ DOI: 10.1016/j.gecco.2016.09.010 http://dx.doi.org/10.1016/j.gecco.2016.09.010 ]
Asner G P , Martin R E , Anderson C B and Knapp D E . 2015b . Quantifying forest canopy traits: imaging spectroscopy versus field survey . Remote Sensing of Environment , 158 : 15 - 27 [ DOI: 10.1016/j.rse.2014.11.011 http://dx.doi.org/10.1016/j.rse.2014.11.011 ]
Asner G P , Martin R E , Knapp D E , Tupayachi R , Anderson C , Carranza L , Martinez P , Houcheime M , Sinca F and Weiss P . 2011 . Spectroscopy of canopy chemicals in humid tropical forests . Remote Sensing of Environment , 115 ( 12 ): 3587 - 3598 [ DOI: 10.1016/j.rse.2011.08.020 http://dx.doi.org/10.1016/j.rse.2011.08.020 ]
Asner G P , Martin R E , Tupayachi R , Anderson C B , Sinca F , Carranza-Jiménez L and Martinez P . 2014 . Amazonian functional diversity from forest canopy chemical assembly . Proceedings of the National Academy of Sciences of the United States of America , 111 ( 15 ): 5604 - 5609 [ DOI: 10.1073/pnas.1401181111 http://dx.doi.org/10.1073/pnas.1401181111 ]
Balzotti C S , Asner G P , Taylor P G , Cleveland C C , Cole R , Martin R E , Nasto M , Osborne B B , Porder S and Townsend A R . 2016 . Environmental controls on canopy foliar nitrogen distributions in a neotropical lowland forest . Ecological Applications , 26 ( 8 ): 2451 - 2464 [ DOI: 10.1002/eap.1408 http://dx.doi.org/10.1002/eap.1408 ]
Blackburn G A . 2007 . Hyperspectral remote sensing of plant pigments . Journal of Experimental Botany , 58 ( 4 ): 855 - 867 [ DOI: 10.1093/jxb/erl123 http://dx.doi.org/10.1093/jxb/erl123 ]
Blonder B , Graae B J , Greer B , Haagsma M , Helsen K , Kapás R E , Pai H , Rieksta J , Sapena D , Still C J and Strimbeck R . 2020 . Remote sensing of ploidy level in quaking aspen ( Populus tremuloides michx.) . Journal of Ecology , 108 ( 1 ): 175 - 188 [ DOI: 10.1111/1365-2745.13296 http://dx.doi.org/10.1111/1365-2745.13296 ]
Botta-Dukát Z . 2005 . Rao's quadratic entropy as a measure of functional diversity based on multiple traits . Journal of Vegetation Science , 16 ( 5 ): 533 - 540 [ DOI: 10.1111/j.1654-1103.2005.tb02393.x http://dx.doi.org/10.1111/j.1654-1103.2005.tb02393.x ]
Cadotte M W , Arnillas C A , Livingstone S W and Yasui S . 2015 . Predicting communities from functional traits . Trends in Ecology and Evolution , 30 ( 9 ): 510 - 511 [ DOI: 10.1016/j.tree.2015.07.001 http://dx.doi.org/10.1016/j.tree.2015.07.001 ]
Cadotte M W , Carscadden K and Mirotchnick N . 2011 . Beyond species: functional diversity and the maintenance of ecological processes and services . Journal of Applied Ecology , 48 ( 5 ): 1079 - 1087 [ DOI: 10.1111/j.1365-2664.2011.02048.x http://dx.doi.org/10.1111/j.1365-2664.2011.02048.x ]
Carmona C P , De Bello F , Mason N W H and Lepš J . 2016 . Traits without borders: integrating functional diversity across scales . Trends in Ecology and Evolution , 31 ( 5 ): 382 - 394 [ DOI: 10.1016/j.tree.2016.02.003 http://dx.doi.org/10.1016/j.tree.2016.02.003 ]
Cavender-Bares J , Gamon J A and Townsend P A . 2020a . The use of remote sensing to enhance biodiversity monitoring and detection: a critical challenge for the twenty-first century //Cavender-Bares J, Gamon J A and Townsend P A, eds. Remote Sensing of Plant Biodiversity . Cham : Springer: 1 - 12 [ DOI: 10.1007/978-3-030-33157-3_1 http://dx.doi.org/10.1007/978-3-030-33157-3_1 ]
Cavender-Bares J , Meireles J E , Couture J J , Kaproth M A , Kingdon C C , Singh A , Serbin S P , Center A , Zuniga E , Pilz G and Townsend P A . 2016 . Associations of leaf spectra with genetic and phylogenetic variation in oaks: prospects for remote detection of biodiversity . Remote Sensing , 8 ( 3 ): 221 [ DOI: 10.3390/rs8030221 http://dx.doi.org/10.3390/rs8030221 ]
Cavender-Bares J , Schweiger A K , Pinto-Ledezma J N and Meireles J E . 2020b . Applying remote sensing to biodiversity science //Cavender-Bares J, Gamon J A and Townsend P A, eds. Remote Sensing of Plant Biodiversity . Cham : Springer: 13 - 42 [ DOI: 10.1007/978-3-030-33157-3_2 http://dx.doi.org/10.1007/978-3-030-33157-3_2 ]
Chadwick K D and Asner G P . 2016 . Organismic-scale remote sensing of canopy foliar traits in lowland tropical forests . Remote Sensing , 8 ( 2 ): 87 [ DOI: 10.3390/rs8020087 http://dx.doi.org/10.3390/rs8020087 ]
Chan J C W and Paelinckx D . 2008 . Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery . Remote Sensing of Environment , 112 ( 6 ): 2999 - 3011 [ DOI: 10.1016/j.rse.2008.02.011 http://dx.doi.org/10.1016/j.rse.2008.02.011 ]
Chang C I . 2000 . An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis . IEEE Transactions on Information Theory , 46 ( 6 ): 1927 - 1932 [ DOI: 10.1109/18.857802 http://dx.doi.org/10.1109/18.857802 ]
Chave J , Coomes D , Jansen S , Lewis S L , Swenson N G and Zanne A E . 2009 . Towards a worldwide wood economics spectrum . Ecology Letters , 12 ( 4 ): 351 - 366 [ DOI: 10.1111/j.1461-0248.2009.01285.x http://dx.doi.org/10.1111/j.1461-0248.2009.01285.x ]
Chen G and Hay G J . 2011 . A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and quickbird data . Photogrammetric Engineering and Remote Sensing , 77 ( 7 ): 733 - 741 [ DOI: 10.14358/pers.77.7.733 http://dx.doi.org/10.14358/pers.77.7.733 ]
Dahlin K M . 2016 . Spectral diversity area relationships for assessing biodiversity in a wildland–agriculture matrix . Ecological Applications , 26 ( 8 ): 2758 - 2768 [ DOI: 10.1002/eap.1390 http://dx.doi.org/10.1002/eap.1390 ]
Danner M , Berger K , Wocher M , Mauser W and Hank T . 2017 . Retrieval of biophysical crop variables from multi-angular canopy spectroscopy . Remote Sensing , 9 ( 7 ): 726 [ DOI: 10.3390/rs9070726 http://dx.doi.org/10.3390/rs9070726 ]
Darvishzadeh R , Atzberger C , Skidmore A and Schlerf M . 2011 . Mapping grassland leaf area index with airborne hyperspectral imagery: a comparison study of statistical approaches and inversion of radiative transfer models . ISPRS Journal of Photogrammetry and Remote Sensing , 66 ( 6 ): 894 - 906 [ DOI: 10.1016/j.isprsjprs.2011.09.013 http://dx.doi.org/10.1016/j.isprsjprs.2011.09.013 ]
Dawson T P and Curran P J . 1998 . Technical note a new technique for interpolating the reflectance red edge position . International Journal of Remote Sensing , 19 ( 11 ): 2133 - 2139 [ DOI: 10.1080/014311698214910 http://dx.doi.org/10.1080/014311698214910 ]
Díaz S , Lavorel S , de Bello F , Quétier F , Grigulis K and Robson T M . 2007 . Incorporating plant functional diversity effects in ecosystem service assessments . Proceedings of the National Academy of Sciences of the United States of America , 104 ( 52 ): 20684 - 20689 [ DOI: 10.1073/pnas.0704716104 http://dx.doi.org/10.1073/pnas.0704716104 ]
Durán S M , Martin R E , Díaz S , Maitner B S , Malhi Y , Salinas N , Shenkin A , Silman M R , Wieczynski D J , Asner G P , Bentley L P , Savage V M and Enquist B J . 2019 . Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature gradient . Science Advances , 5 ( 12 ): eaaw 8114 [ DOI: 10.1126/sciadv.aaw8114 http://dx.doi.org/10.1126/sciadv.aaw8114 ]
Fairbanks D H K and McGwire K C . 2004 . Patterns of floristic richness in vegetation communities of california: regional scale analysis with multi-temporal NDVI . Global Ecology and Biogeography , 13 ( 3 ): 221 - 235 [ DOI: 10.1111/j.1466-822X.2004.00092.x http://dx.doi.org/10.1111/j.1466-822X.2004.00092.x ]
Farrand W H and Harsanyi J C . 1995 . Mineralogic variations in fluvial sediments contaminated by mine tailings as determined from aviris data, coeur d'alene river valley, idaho //Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. [s.l.]: JPL .
Fassnacht F E , Latifi H , Stereńczak K , Modzelewska A , Lefsky M , Waser L T , Straub C and Ghosh A . 2016 . Review of studies on tree species classification from remotely sensed data . Remote Sensing of Environment , 186 : 64 - 87 [ DOI: 10.1016/j.rse.2016.08.013 http://dx.doi.org/10.1016/j.rse.2016.08.013 ]
Fei S L , Jo I , Guo Q F , Wardle D A , Fang J Y , Chen A P , Oswalt C M and Brockerhoff E G . 2018 . Impacts of climate on the biodiversity-productivity relationship in natural forests . Nature Communications , 9 ( 1 ): 5436 [ DOI: 10.1038/s41467-018-07880-w http://dx.doi.org/10.1038/s41467-018-07880-w ]
Féret J B and Asner G P . 2014 . Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy . Ecological Applications , 24 ( 6 ): 1289 - 1296 [ DOI: 10.1890/13-1824.1 http://dx.doi.org/10.1890/13-1824.1 ]
Féret J B , François C , Gitelson A , Asner G P , Barry K M , Panigada C , Richardson A D and Jacquemoud S . 2011 . Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling . Remote Sensing of Environment , 115 ( 10 ): 2742 - 2750 [ DOI: 10.1016/j.rse.2011.06.016 http://dx.doi.org/10.1016/j.rse.2011.06.016 ]
Féret J B , Gitelson A A , Noble S D and Jacquemoud S . 2017 . PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle . Remote Sensing of Environment , 193 : 204 - 215 [ DOI: 10.1016/j.rse.2017.03.004 http://dx.doi.org/10.1016/j.rse.2017.03.004 ]
Féret J B , Le Maire G , Jay S , Berveiller D , Bendoula R , Hmimina G , Cheraiet A , Oliveira J C , Ponzoni F J , Solanki T , de Boissieu F , Chave J , Nouvellon Y , Porcar-Castell A , Proisy C , Soudani K , Gastellu-Etchegorry J P and Lefèvre-Fonollosa M J . 2019 . Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: potential and limitations of physical modeling and machine learning . Remote Sensing of Environment , 231 : 110959 [ DOI: 10.1016/j.rse.2018.11.002 http://dx.doi.org/10.1016/j.rse.2018.11.002 ]
Fontana S , Petchey O L and Pomati F . 2016 . Individual-level trait diversity concepts and indices to comprehensively describe community change in multidimensional trait space . Functional Ecology , 30 ( 5 ): 808 - 818 [ DOI: 10.1111/1365-2435.12551 http://dx.doi.org/10.1111/1365-2435.12551 ]
Fraser L H , Pither J , Jentsch A , Sternberg M , Zobel M , Askarizadeh D , Bartha S , Beierkuhnlein C , Bennett J A , Bittel A , Boldgiv B , Boldrini I I , Bork E , Brown L , Cabido M , Cahill J , Carlyle C N , Campetella G , Chelli S , Cohen O , Csergo A M , Díaz S , Enrico L , Ensing D , Fidelis A , Fridley J D , Foster B , Garris H , Goheen J R , Henry H A L , Hohn M , Jouri M H , Klironomos J , Koorem K , Lawrence-Lodge R , Long R J , Manning P , Mitchell R , Moora M , Müller S C , Nabinger C , Naseri K , Overbeck G E , Palmer T M , Parsons S , Pesek M , Pillar V D , Pringle R M , Roccaforte K , Schmidt A , Shang Z H , Stahlmann R , Stotz G C , Sugiyama S I , Szentes S , Thompson D , Tungalag R , Undrakhbold S , Van Rooyen M , Wellstein C , Wilson J B and Zupo T . 2015 . Worldwide evidence of a unimodal relationship between productivity and plant species richness . Science , 349 ( 6245 ): 302 - 305 [ DOI: 10.1126/science.aab3916 http://dx.doi.org/10.1126/science.aab3916 ]
Gholizadeh H , Gamon J A , Townsend P A , Zygielbaum A I , Helzer C J , Hmimina G Y , Yu R , Moore R M , Schweiger A K and Cavender-Bares J . 2019 . Detecting prairie biodiversity with airborne remote sensing . Remote Sensing of Environment , 221 : 38 - 49 [ DOI: 10.1016/j.rse.2018.10.037 http://dx.doi.org/10.1016/j.rse.2018.10.037 ]
Gholizadeh H , Gamon J A , Zygielbaum A I , Wang R , Schweiger A K and Cavender-Bares J . 2018 . Remote sensing of biodiversity: soil correction and data dimension reduction methods improve assessment of α-diversity (species richness) in prairie ecosystems . Remote Sensing of Environment , 206 : 240 - 253 [ DOI: 10.1016/j.rse.2017.12.014 http://dx.doi.org/10.1016/j.rse.2017.12.014 ]
Gillman L N , Wright S D , Cusens J , McBride P D , Malhi Y and Whittaker R J . 2015 . Latitude, productivity and species richness . Global Ecology and Biogeography , 24 ( 1 ): 107 - 117 [ DOI: 10.1111/geb.12245 http://dx.doi.org/10.1111/geb.12245 ]
Gitelson A A , Gritz Y and Merzlyak M N . 2003 . Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves . Journal of Plant Physiology , 160 ( 3 ): 271 - 282 [ DOI: 10.1078/0176-1617-00887 http://dx.doi.org/10.1078/0176-1617-00887 ]
Goetz A F H . 2009 . Three decades of hyperspectral remote sensing of the earth: a personal view . Remote Sensing of Environment , 113 ( S1 ): S5 - S16 [ DOI: 10.1016/j.rse.2007.12.014 http://dx.doi.org/10.1016/j.rse.2007.12.014 ]
Goetz A F H , Vane G , Solomon J E and Rock B N . 1985 . Imaging spectrometry for earth remote sensing . Science , 228 ( 4704 ): 1147 - 1153 [ DOI: 10.1126/science.228.4704.1147 http://dx.doi.org/10.1126/science.228.4704.1147 ]
Guo Q H , Hu T Y , Jiang Y Q , Jin S C , Wang R , Guan H C , Yang Q L , Li Y M , Wu F F , Zhai Q P , Liu J and Su Y J . 2018 . Advances in remote sensing application for biodiversity research . Biodiversity Science , 26 ( 8 ): 789 - 806
郭庆华 , 胡天宇 , 姜媛茜 , 金时超 , 王瑞 , 关宏灿 , 杨秋丽 , 李玉美 , 吴芳芳 , 翟秋萍 , 刘瑾 , 苏艳军 . 2018 . 遥感在生物多样性研究中的应用进展 . 生物多样性 , 26 ( 8 ): 789 - 806 [ DOI: 10.17520/biods.2018054 http://dx.doi.org/10.17520/biods.2018054 ]
Guo Q H , Liu J , Li Y M , Zhai Q P , Wang Y C , Wu F F , Hu T Y , Wan H W , Liu H M and Shen W M . 2016a . A near-surface remote sensing platform for biodiversity monitoring: perspectives and prospects . Biodiversity Science , 24 ( 11 ): 1249 - 1266
郭庆华 , 刘瑾 , 李玉美 , 翟秋萍 , 王永财 , 吴芳芳 , 胡天宇 , 万华伟 , 刘慧明 , 申文明 . 2016a . 生物多样性近地面遥感监测: 应用现状与前景展望 . 生物多样性 , 24 ( 11 ): 1249 - 1266 [ DOI: 10.17520/biods.2016059 http://dx.doi.org/10.17520/biods.2016059 ]
Guo Q H , Wu F F , Hu T Y , Chen L H , Liu J , Zhao X Q , Gao S and Pang S X . 2016b . Perspectives and prospects of unmanned aerial vehicle in remote sensing monitoring of biodiversity . Biodiversity Science , 24 ( 11 ): 1267 - 1278
郭庆华 , 吴芳芳 , 胡天宇 , 陈琳海 , 刘瑾 , 赵晓倩 , 高上 , 庞树鑫 . 2016b . 无人机在生物多样性遥感监测中的应用现状与展望 . 生物多样性 , 24 ( 11 ): 1267 - 1278 [ DOI: 10.17520/biods.2016105 http://dx.doi.org/10.17520/biods.2016105 ]
Guo Y P , Schöb C , Ma W H , Mohammat A , Liu H Y , Yu S L , Jiang Y X , Schmid B and Tang Z Y . 2019 . Increasing water availability and facilitation weaken biodiversity–biomass relationships in shrublands . Ecology , 100 ( 3 ): e 02624 [ DOI: 10.1002/ecy.2624 http://dx.doi.org/10.1002/ecy.2624 ]
Gutman G G . 1991 . Vegetation indices from AVHRR: an update and future prospects . Remote Sensing of Environment , 35 ( 2/3 ): 121 - 136 [ DOI: 10.1016/0034-4257(91)90005-Q http://dx.doi.org/10.1016/0034-4257(91)90005-Q ]
Haboudane D , Miller J R , Pattey E , Zarco-Tejada P J and Strachan I B . 2004 . Hyperspectral vegetation indices and novel algorithms for predicting green lai of crop canopies: modeling and validation in the context of precision agriculture . Remote Sensing of Environment , 90 ( 3 ): 337 - 352 [ DOI: 10.1016/j.rse.2003.12.013 http://dx.doi.org/10.1016/j.rse.2003.12.013 ]
Hakkenberg C R . 2017 . Mapping Plant Diversity and Composition Across North Carolina Piedmont Forest Landscapes using Lidar-Hyperspectral Remote Sensing . Chapel Hill : The University of North Carolina at Chapel Hill .
Hall K , Johansson L J , Sykes M T , Reitalu T , Larsson K and Prentice H C . 2010 . Inventorying management status and plant species richness in semi-natural grasslands using high spatial resolution imagery . Applied Vegetation Science , 13 ( 2 ): 221 - 233 [ DOI: 10.1111/j.1654-109X.2009.01063.x http://dx.doi.org/10.1111/j.1654-109X.2009.01063.x ]
Hansen M C , Defries R S , Townshend J R G and Sohlberg R . 2000 . Global land cover classification at 1 km spatial resolution using a classification tree approach . International Journal of Remote Sensing , 21 ( 6/7 ): 1331 - 1364 [ DOI: 10.1080/014311600210209 http://dx.doi.org/10.1080/014311600210209 ]
Hector A and Bagchi R . 2007 . Biodiversity and ecosystem multifunctionality . Nature , 448 ( 7150 ): 188 - 190 [ DOI: 10.1038/nature05947 http://dx.doi.org/10.1038/nature05947 ]
Held A , Ticehurst C , Lymburner L and Williams N . 2003 . High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing . International Journal of Remote Sensing , 24 ( 13 ): 2739 - 2759 [ DOI: 10.1080/0143116031000066323 http://dx.doi.org/10.1080/0143116031000066323 ]
Hilker T , Gitelson A , Coops N C , Hall F G and Black T A . 2011 . Tracking plant physiological properties from multi-angular tower-based remote sensing . Oecologia , 165 ( 4 ): 865 - 876 [ DOI: 10.1007/s00442-010-1901-0 http://dx.doi.org/10.1007/s00442-010-1901-0 ]
Houborg R and Boegh E . 2008 . Mapping leaf chlorophyll and leaf area index using inverse and forward canopy reflectance modeling and SPOT reflectance data . Remote Sensing of Environment , 112 ( 1 ): 186 - 202 [ DOI: 10.1016/j.rse.2007.04.012 http://dx.doi.org/10.1016/j.rse.2007.04.012 ]
Inoue Y , Peñuelas J , Miyata A and Mano M . 2008 . Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and co2 flux measurements in rice . Remote Sensing of Environment , 112 ( 1 ): 156 - 172 [ DOI: 10.1016/j.rse.2007.04.011 http://dx.doi.org/10.1016/j.rse.2007.04.011 ]
Jacquemoud S and Baret F . 1990 . PROSPECT: a model of leaf optical properties spectra . Remote Sensing of Environment , 34 ( 2 ): 75 - 91 [ DOI: 10.1016/0034-4257(90)90100-Z http://dx.doi.org/10.1016/0034-4257(90)90100-Z ]
Jacquemoud S , Verhoef W , Baret F , Bacour C , Zarco-Tejada P J , Asner G P , François C and Ustin S L . 2009 . PROSPECT+SAIL models: a review of use for vegetation characterization . Remote Sensing of Environment , 113 ( S1 ): S56 - S66 [ DOI: 10.1016/j.rse.2008.01.026 http://dx.doi.org/10.1016/j.rse.2008.01.026 ]
Jetz W , Cavender-Bares J , Pavlick R , Schimel D , Davis F W , Asner G P , Guralnick R , Kattge J , Latimer A M , Moorcroft P , Schaepman M E , Schildhauer M P , Schneider F D , Schrodt F , Stahl U and Ustin S L . 2016 . Monitoring plant functional diversity from space . Nature Plants , 2 ( 3 ): 16024 [ DOI: 10.1038/nplants.2016.24 http://dx.doi.org/10.1038/nplants.2016.24 ]
Ji J C , Zhao Y , Zou X J , Xuan K F , Wang W P , Liu J L and Li X P . 2019 . Advancement in application of UAV remote sensing to monitoring of farmlands . Acta Pedologica Sinica , 56 ( 4 ): 773 - 784
纪景纯 , 赵原 , 邹晓娟 , 宣可凡 , 王伟鹏 , 刘建立 , 李晓鹏 . 2019 . 无人机遥感在农田信息监测中的应用进展 . 土壤学报 , 56 ( 4 ): 773 - 784 [ DOI: 10.11766/trxb201811190508 http://dx.doi.org/10.11766/trxb201811190508 ]
John R , Chen J Q , Lu N , Guo K , Liang C Z , Wei Y F , Noormets A , Ma K P and Han X G . 2008 . Predicting plant diversity based on remote sensing products in the semi-arid region of inner mongolia . Remote Sensing of Environment , 112 ( 5 ): 2018 - 2032 [ DOI: 10.1016/j.rse.2007.09.013 http://dx.doi.org/10.1016/j.rse.2007.09.013 ]
Jurdao S , Yebra M , Guerschman J P and Chuvieco E . 2013 . Regional estimation of woodland moisture content by inverting radiative transfer models . Remote Sensing of Environment , 132 : 59 - 70 [ DOI: 10.1016/j.rse.2013.01.004 http://dx.doi.org/10.1016/j.rse.2013.01.004 ]
Khanna S , Santos M J , Ustin S L and Haverkamp P J . 2011 . An integrated approach to a biophysiologically based classification of floating aquatic macrophytes . International Journal of Remote Sensing , 32 ( 9 ): 1067 - 1094 [ DOI: 10.1080/01431160903505328 http://dx.doi.org/10.1080/01431160903505328 ]
Khare S , Latifi H and Rossi S . 2019 . Forest beta-diversity analysis by remote sensing: how scale and sensors affect the Rao’s Q index . Ecological Indicators , 106 : 105520 [ DOI: 10.1016/j.ecolind.2019.105520 http://dx.doi.org/10.1016/j.ecolind.2019.105520 ]
Knyazikhin Y , Schull M A , Stenberg P , Mõttus M , Rautiainen M , Yang Y , Marshak A , Carmona P L , Kaufmann R K , Lewis P , Disney M I , Vanderbilt V , Davis A B , Baret F , Jacquemoud S , Lyapustin A and Myneni R B . 2013 . Hyperspectral remote sensing of foliar nitrogen content . Proceedings of the National Academy of Sciences of the United States of America , 110 ( 3 ): E185 - E192 [ DOI: 10.1073/pnas.1210196109 http://dx.doi.org/10.1073/pnas.1210196109 ]
Kong J X , Zhang Z C and Zhang J . 2019 . Classification and identification of plant species based on multi-source remote sensing data: research progress and prospect . Biodiversity Science , 27 ( 7 ): 796 - 812 [ DOI: 10.17520/biods.2019197 http://dx.doi.org/10.17520/biods.2019197 ]
Kruse F A , Lefkoff A B , Boardman J W , Heidebrecht K B , Shapiro A T , Barloon P J and Goetz A F H . 1993 . The spectral image processing system (SIPS)‐interactive visualization and analysis of imaging spectrometer data . AIP Conference Proceedings , 283 ( 1 ): 192 - 201 [ DOI: 10.1063/1.44433 http://dx.doi.org/10.1063/1.44433 ]
Laliberté E and Legendre P . 2010 . A distance-based framework for measuring functional diversity from multiple traits . Ecology , 91 ( 1 ): 299 - 305 [ DOI: 10.1890/08-2244.1 http://dx.doi.org/10.1890/08-2244.1 ]
Laliberté E , Schweiger A K and Legendre P . 2020 . Partitioning plant spectral diversity into alpha and beta components . Ecology Letters , 23 ( 2 ): 370 - 380 [ DOI: 10.1111/ele.13429 http://dx.doi.org/10.1111/ele.13429 ]
Lausch A , Bannehr L , Beckmann M , Boehm C , Feilhauer H , Hacker J M , Heurich M , Jung A , Klenke R , Neumann C , Pause M , Rocchini D , Schaepman M E , Schmidtlein S , Schulz K , Selsam P , Settele J , Skidmore A K and Cord A F . 2016 . Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives . Ecological Indicators , 70 : 317 - 339 [ DOI: 10.1016/j.ecolind.2016.06.022 http://dx.doi.org/10.1016/j.ecolind.2016.06.022 ]
Li Y M , Guo Q H , Wan B , Qin H N , Wang D Z , Xu K X , Song S L , Sun Q H , Zhao X X , Yang M H , Wu X Y , Wei D J , Hu T Y and Su Y J . 2021 . Current status and prospect of three-dimensional dynamic monitoring of natural resources based on LiDAR . Journal of Remote Sensing , 25 ( 1 ): 381 - 402
李玉美 , 郭庆华 , 万波 , 秦宏楠 , 王德智 , 徐可心 , 宋师琳 , 孙千惠 , 赵晓霞 , 杨默含 , 吴晓永 , 魏邓杰 , 胡天宇 , 苏艳军 . 2021 . 基于激光雷达的自然资源三维动态监测现状与展望 . 遥感学报 , 25 ( 1 ): 381 - 402 [ DOI: 10.11834/jrs.20210351 http://dx.doi.org/10.11834/jrs.20210351 ]
Liang J J , Crowther T W , Picard N , Wiser S , Zhou M , Alberti G , Schulze E D , McGuire A D , Bozzato F , Pretzsch H , de-Miguel S , Paquette A , Hérault B , Scherer-Lorenzen M , Barrett C B , Glick H B , Hengeveld G M , Nabuurs G J , Pfautsch S , Viana H , Vibrans A C , Ammer C , Schall P , Verbyla D , Tchebakova N , Fischer M , Watson J V , Chen H Y H , Lei X D , Schelhaas M J , Lu H C , Gianelle D , Parfenova E I , Salas C , Lee E , Lee B , Kim H S , Bruelheide H , Coomes D A , Piotto D , Sunderland T , Schmid B , Gourlet-Fleury S , Sonké B , Tavani R , Zhu J , Brandl S , Vayreda J , Kitahara F , Searle E B , Neldner V J , Ngugi M R , Baraloto C , Frizzera L , Bałazy R , Oleksyn J , Zawiła-Niedźwiecki T , Bouriaud O , Bussotti F , Finér L , Jaroszewicz B , Jucker T , Valladares F , Jagodzinski A M , Peri P L , Gonmadje C , Marthy W , O’Brien T , Martin E H , Marshall A R , Rovero F , Bitariho R , Niklaus P A , Alvarez-Loayza P , Chamuya N , Valencia R , Mortier F , Wortel V , Engone-Obiang N L , Ferreira L V , Odeke D E , Vasquez R M , Lewis S L and Reich P B . 2016 . Positive biodiversity-productivity relationship predominant in global forests . Science , 354 ( 6309 ): eaaf 8957 [ DOI: 10.1126/science.aaf8957 http://dx.doi.org/10.1126/science.aaf8957 ]
Lin H and Zhang H S . 2021 . Tropical and subtropical remote sensing: needs, challenges, and opportunities . Journal of Remote Sensing , 25 ( 1 ): 276 - 291
林珲 , 张鸿生 . 2021 . 热带与亚热带遥感: 需求、挑战与机遇 . 遥感学报 , 24 ( 1 ): 511 - 520 [ DOI: 10.11834/jrs.20210237 http://dx.doi.org/10.11834/jrs.20210237 ]
Lucas K L and Carter G A . 2008 . The use of hyperspectral remote sensing to assess vascular plant species richness on horn island, mississippi . Remote Sensing of Environment , 112 ( 10 ): 3908 - 3915 [ DOI: 10.1016/j.rse.2008.06.009 http://dx.doi.org/10.1016/j.rse.2008.06.009 ]
Malenovský Z , Turnbull J D , Lucieer A and Robinson S A . 2015 . Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data . New Phytologist , 208 ( 2 ): 608 - 624 [ DOI: 10.1111/nph.13524 http://dx.doi.org/10.1111/nph.13524 ]
Martin R E , Chadwick K D , Brodrick P G , Carranza-Jimenez L , Vaughn N R and Asner G P . 2018 . An approach for foliar trait retrieval from airborne imaging spectroscopy of tropical forests . Remote Sensing , 10 ( 2 ): 199 [ DOI: 10.3390/rs10020199 http://dx.doi.org/10.3390/rs10020199 ]
Marvin D C and Asner G P . 2016 . Spatially explicit analysis of field inventories for national forest carbon monitoring . Carbon Balance and Management , 11 ( 1 ): 9 [ DOI: 10.1186/s13021-016-0050-0 http://dx.doi.org/10.1186/s13021-016-0050-0 ]
Mason N W H , Mouillot D , Lee W G and Wilson J B . 2005 . Functional richness, functional evenness and functional divergence: the primary components of functional diversity . Oikos , 111 ( 1 ): 112 - 118 [ DOI: 10.1111/j.0030-1299.2005.13886.x http://dx.doi.org/10.1111/j.0030-1299.2005.13886.x ]
McManus K M , Asner G P , Martin R E , Dexter K G , Kress W J and Field C B . 2016 . Phylogenetic structure of foliar spectral traits in tropical forest canopies . Remote Sensing , 8 ( 3 ): 196 [ DOI: 10.3390/rs8030196 http://dx.doi.org/10.3390/rs8030196 ]
Mi X C , Feng G , Hu Y B , Zhang J , Chen L , Corlett R T , Hughes A C , Pimm S , Schmid B , Shi S , Svenning J C and Ma K P . 2021 . The global significance of biodiversity science in China: an overview . National Science Review , 8 ( 7 ): nwab 032 [ DOI: 10.1093/nsr/nwab032 http://dx.doi.org/10.1093/nsr/nwab032 ]
Mutanga O and Kumar L . 2007 . Estimating and mapping grass phosphorus concentration in an African savanna using hyperspectral image data . International Journal of Remote Sensing , 28 ( 21 ): 4897 - 4911 [ DOI: 10.1080/01431160701253253 http://dx.doi.org/10.1080/01431160701253253 ]
Oehri J , Schmid B , Schaepman-Strub G and Niklaus P A . 2020 . Terrestrial land-cover type richness is positively linked to landscape-level functioning . Nature Communications , 11 ( 1 ): 154 [ DOI: 10.1038/s41467-019-14002-7 http://dx.doi.org/10.1038/s41467-019-14002-7 ]
Ollinger S V . 2011 . Sources of variability in canopy reflectance and the convergent properties of plants . New Phytologist , 189 ( 2 ): 375 - 394 [ DOI: 10.1111/j.1469-8137.2010.03536.x http://dx.doi.org/10.1111/j.1469-8137.2010.03536.x ]
Palmer M W , Earls P G , Hoagland B W , White P S and Wohlgemuth T . 2002 . Quantitative tools for perfecting species lists . Environmetrics , 13 ( 2 ): 121 - 137 [ DOI: 10.1002/env.516 http://dx.doi.org/10.1002/env.516 ]
Pandey P , Ge Y F , Stoerger V and Schnable J C . 2017 . High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging . Frontiers in Plant Science , 8 : 1348 [ DOI: 10.3389/fpls.2017.01348 http://dx.doi.org/10.3389/fpls.2017.01348 ]
Peng Y , Wang Y , Ma J W , Fan M , Bai L and Zhou T . 2019 . Assessment of plant species alpha diversity in central hunshandak sandland, China based on field surveys and hyperspectral data . Acta Ecologica Sinica , 39 ( 13 ): 4883 - 4891
彭羽 , 王越 , 马江文 , 范敏 , 白岚 , 周涛 . 2019 . 基于实地调查和高光谱数据的浑善达克沙地中部植物alpha多样性遥感估测 . 生态学报 , 39 ( 13 ): 4883 - 4891 [ DOI: 10.5846/stxb201806221377 http://dx.doi.org/10.5846/stxb201806221377 ]
Petchey O L and Gaston K J . 2002 . Functional diversity (FD), species richness and community composition . Ecology Letters , 5 ( 3 ): 402 - 411 [ DOI: 10.1046/j.1461-0248.2002.00339.x http://dx.doi.org/10.1046/j.1461-0248.2002.00339.x ]
Polley H W , Yang C H , Wilsey B J and Fay P A . 2019 . Spectral heterogeneity predicts local-scale gamma and beta diversity of mesic grasslands . Remote Sensing , 11 ( 4 ): 458 [ DOI: 10.3390/rs11040458 http://dx.doi.org/10.3390/rs11040458 ]
Pressey R L , Cowling R M and Rouget M . 2003 . Formulating conservation targets for biodiversity pattern and process in the cape floristic region, south Africa . Biological Conservation , 112 ( 1/2 ): 99 - 127 [ DOI: 10.1016/S0006-3207(02)00424-X http://dx.doi.org/10.1016/S0006-3207(02)00424-X ]
Qi J G , Chehbouni A , Huete A R , Kerr Y H and Sorooshian S . 1994 . A modified soil adjusted vegetation index . Remote Sensing of Environment , 48 ( 2 ): 119 - 126 [ DOI: 10.1016/0034-4257(94)90134-1 http://dx.doi.org/10.1016/0034-4257(94)90134-1 ]
Qin X L , Li X T , Liu S C , Liu Q and Li Z Y . 2020 . Forest fire early warning and monitoring techniques using satellite remote sensing in China . Journal of Remote Sensing , 24 ( 5 ): 511 - 520
覃先林 , 李晓彤 , 刘树超 , 刘倩 , 李增元 . 2020 . 中国林火卫星遥感预警监测技术研究进展 . 遥感学报 , 24 ( 5 ): 511 - 520 [ DOI: 10.11834/jrs.20209135 http://dx.doi.org/10.11834/jrs.20209135 ]
Ricciardi A . 2007 . Are modern biological invasions an unprecedented form of global change? Conservation Biology , 21 ( 2 ): 329 - 336 [ DOI: 10.1111/j.1523-1739.2006.00615.x http://dx.doi.org/10.1111/j.1523-1739.2006.00615.x ]
Rondeaux G. , M. Steven and F. Baret 1996 . Optimization of soil-adjusted vegetation indices . Remote Sensing of Environment , 55 ( 2 ): 95 - 107 [ DOI: 10.1016/0034-4257(95)00186-7 http://dx.doi.org/10.1016/0034-4257(95)00186-7 ]
Rocchini D , Balkenhol N , Carter G A , Foody G M , Gillespie T W , He K S , Kark S , Levin N , Lucas K , Luoto M , Nagendra H , Oldeland J , Ricotta C , Southworth J and Neteler M . 2010 . Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges . Ecological Informatics , 5 ( 5 ): 318 - 329 [ DOI: 10.1016/j.ecoinf.2010.06.001 http://dx.doi.org/10.1016/j.ecoinf.2010.06.001 ]
Schaepman M E , Ustin S L , Plaza A J , Painter T H , Verrelst J and Liang S L . 2009 . Earth system science related imaging spectroscopy—an assessment . Remote Sensing of Environment , 113 ( S1 ): S123 - S137 [ DOI: 10.1016/j.rse.2009.03.001 http://dx.doi.org/10.1016/j.rse.2009.03.001 ]
Schaub S , Finger R , Leiber F , Probst S , Kreuzer M , Weigelt A , Buchmann N and Scherer-Lorenzen M . 2020 . Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands . Nature Communications , 11 ( 1 ): 768 [ DOI: 10.1038/s41467-020-14541-4 http://dx.doi.org/10.1038/s41467-020-14541-4 ]
Schlemmer M , Gitelson A , Schepers J , Ferguson R , Peng Y , Shanahan J and Rundquist D . 2013 . Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels . International Journal of Applied Earth Observation and Geoinformation , 25 : 47 - 54 [ DOI: 10.1016/j.jag.2013.04.003 http://dx.doi.org/10.1016/j.jag.2013.04.003 ]
Schleuter D , Daufresne M , Massol F and Argillier C . 2010 . A user's guide to functional diversity indices . Ecological Monographs , 80 ( 3 ): 469 - 484 [ DOI: 10.1890/08-2225.1 http://dx.doi.org/10.1890/08-2225.1 ]
Schneider F D , Morsdorf F , Schmid B , Petchey O L , Hueni A , Schimel D S and Schaepman M E . 2017 . Mapping functional diversity from remotely sensed morphological and physiological forest traits . Nature Communications , 8 ( 1 ): 1441 [ DOI: 10.1038/s41467-017-01530-3 http://dx.doi.org/10.1038/s41467-017-01530-3 ]
Schweiger A K , Cavender-Bares J , Townsend P A , Hobbie S E , Madritch M D , Wang R , Tilman D and Gamon J A . 2018 . Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function . Nature Ecology and Evolution , 2 ( 6 ): 976 - 982 [ DOI: 10.1038/s41559-018-0551-1 http://dx.doi.org/10.1038/s41559-018-0551-1 ]
Schweiger A K , Schütz M , Risch A C , Kneubühler M , Haller R and Schaepman M E . 2017 . How to predict plant functional types using imaging spectroscopy: linking vegetation community traits, plant functional types and spectral response . Methods in Ecology and Evolution , 8 ( 61 ): 86 - 95 [ DOI: 10.1111/2041-210X.12642 http://dx.doi.org/10.1111/2041-210X.12642 ]
She X J , Zhang L F , Baig M H A and Li Y . 2014 . Calculating vegetation index based on the universal pattern decomposition method (VIUPD) using landsat 8//2014 IEEE Geoscience and Remote Sensing Symposium . Quebec City : IEEE : 4734 - 4737 [ DOI: 10.1109/IGARSS.2014.6947551 http://dx.doi.org/10.1109/IGARSS.2014.6947551 ]
Singh A , Serbin S P , McNeil B E , Kingdon C C and Townsend P A . 2015 . Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties . Ecological Applications , 25 ( 8 ): 2180 - 2197 [ DOI: 10.1890/14-2098.1 http://dx.doi.org/10.1890/14-2098.1 ]
Skidmore A K , Ferwerda J G , Mutanga O , Van Wieren S E , Peel M , Grant R C , Prins H H T , Balcik F B and Venus V . 2010 . Forage quality of savannas — simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery . Remote Sensing of Environment , 114 ( 1 ): 64 - 72 [ DOI: 10.1016/j.rse.2009.08.010 http://dx.doi.org/10.1016/j.rse.2009.08.010 ]
Stein A , Gerstner K and Kreft H . 2014 . Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales . Ecology Letters , 17 ( 7 ): 866 - 880 [ DOI: 10.1111/ele.12277 http://dx.doi.org/10.1111/ele.12277 ]
Thenkabail P S , Enclona E A , Ashton M S and Van Der Meer B . 2004 . Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications . Remote Sensing of Environment , 91 ( 3/4 ): 354 - 376 [ DOI: 10.1016/j.rse.2004.03.013 http://dx.doi.org/10.1016/j.rse.2004.03.013 ]
Thenkabail P S , Lyon J G and Huete A . 2018 . Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation . 2nd ed . Boca Raton : CRC Press [ DOI: 10.1201/9781315164151 http://dx.doi.org/10.1201/9781315164151 ]
Thompson P L , Isbell F , Loreau M , O'Connor M I and Gonzalez A . 2018 . The strength of the biodiversity–ecosystem function relationship depends on spatial scale . Proceedings of the Royal Society B: Biological Sciences , 285 ( 1880 ): 20180038 [ DOI: 10.1098/rspb.2018.0038 http://dx.doi.org/10.1098/rspb.2018.0038 ]
Tilman D , Reich P B and Knops J M H . 2006 . Biodiversity and ecosystem stability in a decade-long grassland experiment . Nature , 441 ( 7093 ): 629 - 632 [ DOI: 10.1038/nature04742 http://dx.doi.org/10.1038/nature04742 ]
Tong Q X , Zhang B and Zhang L F . 2016 . Current progress of hyperspectral remote sensing in China . Journal of Remote Sensing , 20 ( 5 ): 689 - 707
童庆禧 , 张兵 , 张立福 . 2016 . 中国高光谱遥感的前沿进展 . 遥感学报 , 20 ( 5 ): 689 - 707 [ DOI: 10.11834jrs.20166264 http://dx.doi.org/10.11834jrs.20166264 ]
Townsend A R , Cleveland C C , Asner G P and Bustamante M M C . 2007 . Controls over foliar N: P ratios in tropical rain forests . Ecology , 88 ( 1 ): 107 - 118 [ DOI: 10.1890/0012-9658(2007)88 http://dx.doi.org/10.1890/0012-9658(2007)88 [107:COFNRI]2.0.CO; 2 ]
Turner W , Spector S , Gardiner N , Fladeland M , Sterling E and Steininger M . 2003 . Remote sensing for biodiversity science and conservation . Trends in Ecology and Evolution , 18 ( 6 ): 306 - 314 [ DOI: 10.1016/S0169-5347(03)00070-3 http://dx.doi.org/10.1016/S0169-5347(03)00070-3 ]
Ustin S L and Gamon J A . 2010 . Remote sensing of plant functional types . New Phytologist , 186 ( 4 ): 795 - 816 [ DOI: 10.1111/j.1469-8137.2010.03284.x http://dx.doi.org/10.1111/j.1469-8137.2010.03284.x ]
Verrelst J , Rivera J P , Gitelson A , Delegido J , Moreno J and Camps-Valls G . 2016 . Spectral band selection for vegetation properties retrieval using Gaussian processes regression . International Journal of Applied Earth Observation and Geoinformation , 52 : 554 - 567 [ DOI: 10.1016/j.jag.2016.07.016 http://dx.doi.org/10.1016/j.jag.2016.07.016 ]
Villéger S , Mason N W H and Mouillot D . 2008 . New multidimensional functional diversity indices for a multifaceted framework in functional ecology . Ecology , 89 ( 8 ): 2290 - 2301 [ DOI: 10.1890/07-1206.1 http://dx.doi.org/10.1890/07-1206.1 ]
Wang R and Gamon J A . 2019 . Remote sensing of terrestrial plant biodiversity . Remote Sensing of Environment , 231 : 111218 [ DOI: 10.1016/j.rse.2019.111218 http://dx.doi.org/10.1016/j.rse.2019.111218 ]
Wang R , Gamon J A , Cavender‐Bares J , Townsend P A and Zygielbaum A I . 2018a . The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland . Ecological Applications , 28 ( 2 ): 541 - 556 [ DOI: 10.1002/eap.1669 http://dx.doi.org/10.1002/eap.1669 ]
Wang R , Gamon J A , Montgomery R A , Townsend P A , Zygielbaum A I , Bitan K , Tilman D and Cavender-Bares J . 2016 . Seasonal variation in the NDVI-species richness relationship in a prairie grassland experiment (cedar creek) . Remote Sensing , 8 ( 2 ): 128 [ DOI: 10.3390/rs8020128 http://dx.doi.org/10.3390/rs8020128 ]
Wang Z H , Chlus A , Geygan R , Ye Z W , Zheng T , Singh A , Couture J J , Cavender‐Bares J , Kruger E L and Townsend P A . 2020 . Foliar functional traits from imaging spectroscopy across biomes in eastern north America . New Phytologist , 228 ( 8 ): 494 - 511 [ DOI: 10.1111/nph.16711 http://dx.doi.org/10.1111/nph.16711 ]
Wang Z H , Skidmore A K , Darvishzadeh R and Wang T J . 2018b . Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery . Agricultural and Forest Meteorology , 253 - 254 : 247 - 260 [ DOI: 10.1016/j.agrformet.2018.02.010 http://dx.doi.org/10.1016/j.agrformet.2018.02.010 ]
Wang Z H , Townsend P A , Schweiger A K , Couture J J , Singh A , Hobbie S E and Cavender-Bares J . 2019 . Mapping foliar functional traits and their uncertainties across three years in a grassland experiment . Remote Sensing of Environment , 221 : 405 - 416 [ DOI: 10.1016/j.rse.2018.11.016 http://dx.doi.org/10.1016/j.rse.2018.11.016 ]
Wessman C A , Aber J D , Peterson D L and Melillo J M . 1988 . Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems . Nature , 335 ( 6186 ): 154 - 156 [ DOI: 10.1038/335154a0 http://dx.doi.org/10.1038/335154a0 ]
Wilson B T , Lister A J and Riemann R I . 2012 . A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data . Forest Ecology and Management , 271 : 182 - 198 [ DOI: 10.1016/j.foreco.2012.02.002 http://dx.doi.org/10.1016/j.foreco.2012.02.002 ]
Wright I J , Reich P B , Westoby M , Ackerly D D , Baruch Z , Bongers F , Cavender-Bares J , Chapin T , Cornelissen J H C , Diemer M , Flexas J , Garnier E , Groom P K , Gulias J , Hikosaka K , Lamont B B , Lee T , Lee W , Lusk C , Midgley J J , Navas M L , Niinemets Ü , Oleksyn J , Osada N , Poorter H , Poot P , Prior L , Pyankov V I , Roumet C , Thomas S C , Tjoelker M G , Veneklaas E J and Villar R . 2004 . The worldwide leaf economics spectrum . Nature , 428 ( 6985 ): 821 - 827 [ DOI: 10.1038/nature02403 http://dx.doi.org/10.1038/nature02403 ]
Wu B F , Zeng Y , Yan N N , Zeng H W , Zhao D and Zhang M . 2020 . Remote sensing for ecosystem: definition and prospects . Journal of Remote Sensing , 24 ( 6 ): 609 - 617
吴炳方 , 曾源 , 闫娜娜 , 曾红伟 , 赵旦 , 张淼 . 2020 . 生态系统遥感: 内涵与挑战 . 遥感学报 , 24 ( 6 ): 609 - 617 [ DOI: 10.11834/jrs.20209247 http://dx.doi.org/10.11834/jrs.20209247 ]
Yebra M and Chuvieco E . 2009 . Linking ecological information and radiative transfer models to estimate fuel moisture content in the mediterranean region of spain: solving the ill-posed inverse problem . Remote Sensing of Environment , 113 ( 11 ): 2403 - 2411 [ DOI: 10.1016/j.rse.2009.07.001 http://dx.doi.org/10.1016/j.rse.2009.07.001 ]
Yi H Y , Zeng Y , Zhao Y J , Zheng Z J , Xiong J and Zhao D . 2020 . Forest species diversity mapping based on clustering algorithm . Chinese Journal of Plant Ecology , 44 ( 6 ): 598 - 615
衣海燕 , 曾源 , 赵玉金 , 郑朝菊 , 熊杰 , 赵旦 . 2020 . 利用聚类算法监测森林乔木物种多样性 . 植物生态学报 , 44 ( 6 ): 598 - 615 [ DOI: 10.17521/cjpe.2019.0347 http://dx.doi.org/10.17521/cjpe.2019.0347 ]
Zald H S J , Wulder M A , White J C , Hilker T , Hermosilla T , Hobart G W and Coops N C . 2016 . Integrating landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada . Remote Sensing of Environment , 176 : 188 - 201 [ DOI: 10.1016/j.rse.2016.01.015 http://dx.doi.org/10.1016/j.rse.2016.01.015 ]
Zhao Y J , Sun Y H , Lu X M , Zhao X Z , Yang L , Sun Z Y and Bai Y F . 2021 . Hyperspectral retrieval of leaf physiological traits and their links to ecosystem productivity in grassland monocultures . Ecological Indicators , 122 : 107267 [ DOI: 10.1016/j.ecolind.2020.107267 http://dx.doi.org/10.1016/j.ecolind.2020.107267 ]
Zhao Y J , Zeng Y , Zhao D , Wu B F and Zhao Q J . 2016 . The optimal leaf biochemical selection for mapping species diversity based on imaging spectroscopy . Remote Sensing , 8 ( 3 ): 216 [ DOI: 10.3390/rs8030216 http://dx.doi.org/10.3390/rs8030216 ]
Zhao Y J , Zeng Y , Zheng Z J , Dong W X , Zhao D , Wu B F and Zhao Q J . 2018 . Forest species diversity mapping using airborne lidar and hyperspectral data in a subtropical forest in China . Remote Sensing of Environment , 213 : 104 - 114 [ DOI: 10.1016/j.rse.2018.05.014 http://dx.doi.org/10.1016/j.rse.2018.05.014 ]
Zheng G and Moskal L M . 2009 . Retrieving leaf area index (LAI) using remote sensing: theories, methods and sensors . Sensors , 9 ( 4 ): 2719 - 2745 [ DOI: 10.3390/s90402719 http://dx.doi.org/10.3390/s90402719 ]
Zheng Z J , Zeng Y , Schneider F D , Zhao Y J , Zhao D , Schmid B , Schaepman M E and Morsdorf F . 2021 . Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest . Remote Sensing of Environment , 252 : 112170 [ DOI: 10.1016/j.rse.2020.112170 http://dx.doi.org/10.1016/j.rse.2020.112170 ]
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