Abstract
The emerging challenges in preserving and managing forest ecosystems are multiscale in terms of space and time, and therefore require spatially and temporally contiguous information sources. Imaging spectroscopy has the potential to contribute information that cannot be raised by other Earth Observation Systems. In particular, the spectral capacity to monitor the distributions of chemical traits, such as canopy foliar nitrogen distribution, and to track changes in water content or the percentage water in plants, has already opened novel pathways toward assessing the global variability of ecosystem functions and services. However, there is an ongoing debate on how to best extract this type of information from the spectral measurements. Empirical approaches have demonstrated their efficiency in a multitude of local studies, but are criticized with respect to poor generalization capacities. Alternative strategies, such as the use of physically based models of leaf or canopy reflectance, or hybrid approaches, have the potential advantage to be more widely applicable. This paper attempts to assess achievements and shortcomings of these strategies and finds that the often-cited disadvantages of using empirical approaches are becoming less pronounced in the light of recent research results. While retrievals based on physically based models on leaf/needle level are close to laboratory quality, results on canopy level available to date still have considerable deficits. Owing to improved instrumental designs, better data calibration, new approaches for compensating canopy effects, and the use of increasingly efficient methods for establishing data-driven models, the scope of empirical approaches has considerably widened and they have been successfully applied to large areas. The future availability of regularly acquired hyperspectral imagery from Earth orbits will substantially contribute to their generalizability.
Similar content being viewed by others
References
Abdullah H, Darvishzadeh R, Skidmore AK, Groen TA, Heurich M (2018) European spruce bark beetle (Ips typographus, L.) green attack affects foliar reflectance and biochemical properties. Int J Appl Earth Obs Geoinf 64:199–209. https://doi.org/10.1016/j.jag.2017.09.009
Adams HD et al (2017) A multi-species synthesis of physiological mechanisms in drought-induced tree mortality. Nat Ecol Evol 1:1285–1291. https://doi.org/10.1038/s41559-017-0248-x
Albaugh TJ, Allen HL, Zutter BR, Quicke HE (2003) Vegetation control and fertilization in midrotation Pinus taeda stands in the southeastern United States. Ann For Sci 60:619–624. https://doi.org/10.1051/forest:2003054
Allen HL, Fox TR, Campbell RG (2005) What is ahead for intensive pine plantation silviculture in the south? South J Appl For 29:62–69. https://doi.org/10.1093/sjaf/29.2.62
Anderegg LDL, Anderegg WRL, Berry JA (2013a) Not all droughts are created equal: translating meteorological drought into woody plant mortality. Tree Physiol 33:672–683. https://doi.org/10.1093/treephys/tpt044
Anderegg WRL, Kane JM, Anderegg LDL (2013b) Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3:30–36. https://doi.org/10.1038/nclimate1635
Asner GP (1998) Biophysical and biochemical sources of variability in canopy reflectance. Remote Sens Environ 64:234–253. https://doi.org/10.1016/S0034-4257(98)00014-5
Asner GP, Martin RE (2009) Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front Ecol Environ 7:269–276. https://doi.org/10.1890/070152
Asner GP, Martin RE (2016) Convergent elevation trends in canopy chemical traits of tropical forests. Glob Change Biol 22:2216–2227. https://doi.org/10.1111/gcb.13164
Asner GP, Nepstad D, Cardinot G, Ray D (2004) Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proc Natl Acad Sci USA 101:6039–6044. https://doi.org/10.1073/pnas.0400168101
Asner GP et al (2011) Spectroscopy of canopy chemicals in humid tropical forests. Remote Sens Environ 115:3587–3598. https://doi.org/10.1016/j.rse.2011.08.020
Asner GP et al (2012) Carnegie Airborne Observatory-2: increasing science data dimensionality via high-fidelity multi-sensor fusion. Remote Sens Environ 124:454–465. https://doi.org/10.1016/j.rse.2012.06.012
Asner GP, Martin RE, Anderson CB, Knapp DE (2015) Quantifying forest canopy traits: imaging spectroscopy versus field survey. Remote Sens Environ 158:15–27. https://doi.org/10.1016/j.rse.2014.11.011
Asner GP, Brodrick PG, Anderson CB, Vaughn N, Knapp DE, Martin RE (2016) Progressive forest canopy water loss during the 2012–2015 California drought. Proc Natl Acad Sci 113:E249–E255. https://doi.org/10.1073/pnas.1523397113
Asner GP et al (2017) Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355:385. https://doi.org/10.1126/science.aaj1987
Atzberger C (2000) Development of an invertible forest reflectance model: the INFOR-model. In: Buchroithner M (ed) A decade of trans-European remote sensing cooperation. Proceedings of the 20th EARSeL symposium, Dresden, Germany, 14–16 June 2000, pp 39–44
Atzberger C, Guérif M, Baret F, Werner W (2010) Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat. Comput Electron Agric 73:165–173. https://doi.org/10.1016/j.compag.2010.05.006
Barton CVM, North PRJ (2001) Remote sensing of canopy light use efficiency using the photochemical reflectance index: model and sensitivity analysis. Remote Sens Environ 78:264–273. https://doi.org/10.1016/S0034-4257(01)00224-3
Beamish AL, Coops NC, Hermosilla T, Chabrillat S, Heim B (2018) Monitoring pigment-driven vegetation changes in a low-Arctic tundra ecosystem using digital cameras. Ecosphere 9:e02123. https://doi.org/10.1002/ecs2.2123
Beniston M (2004) The 2003 heat wave in Europe: a shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys Res Lett. https://doi.org/10.1029/2003GL018857
Berger K, Atzberger C, Danner M, D’Urso G, Mauser W, Vuolo F, Hank T (2018) Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: a review study. Remote Sens 10:85. https://doi.org/10.3390/rs10010085
Bicheron P, Leroy M (1999) A method of biophysical parameter retrieval at global scale by inversion of a vegetation reflectance model. Remote Sens Environ 67:251–266. https://doi.org/10.1016/S0034-4257(98)00083-2
Birdsey R, Pan Y (2011) Ecology: drought and dead trees. Nat Clim Change 1:444–445. https://doi.org/10.1038/nclimate1298
Bond I, Chambwera M, Jones B, Nhantumbo I, Chundama M (2010) REDD+ in dryland forests: issues and prospects for pro-poor REDD in the miombo woodlands of southern Africa, natural resource issues, vol 21. International Institute for Environment and Development, London
Bréda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54:2403–2417. https://doi.org/10.1093/jxb/erg263
Buddenbaum H, Steffens M (2012) The effects of spectral pretreatments on chemometric analyses of soil profiles using laboratory imaging spectroscopy. Appl Environ Soil Sci 2012:12. https://doi.org/10.1155/2012/274903
Buddenbaum H, Pueschel P, Stellmes M, Werner W, Hill J (2011) Measuring water and chlorophyll content on the leaf and canopy scale. EARSeL eProc 10:66–72
Buddenbaum H, Stern O, Stellmes M, Stoffels J, Pueschel P, Hill J, Werner W (2012) Field imaging spectroscopy of beech seedlings under dryness stress. Remote Sens 4:3721–3740. https://doi.org/10.3390/rs4123721
Buddenbaum H et al (2015) Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings. Int J Remote Sens 36:4590–4605. https://doi.org/10.1080/01431161.2015.1084435
Carrere V et al (2013) HYPXIM: a second generation high spatial resolution hyperspectral satellite for dual applications. In: 5th Workshop on hyperspectral image and signal processing: evolution in remote sensing (WHISPERS), 26–28 June 2013, pp 1–4. https://doi.org/10.1109/whispers.2013.8080685
Chadwick K, Asner G (2016) Organismic-scale remote sensing of canopy foliar traits in lowland tropical forests. Remote Sens 8:87. https://doi.org/10.3390/rs8020087
Chávez R, Clevers J, Herold M, Acevedo E, Ortiz M (2013) Assessing water stress of desert tamarugo trees using in situ data and very high spatial resolution remote sensing. Remote Sens 5:5064. https://doi.org/10.3390/rs5105064
Cheng T, Rivard B, Sánchez-Azofeifa A (2011) Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens Environ 115:659–670. https://doi.org/10.1016/j.rse.2010.11.001
Cheng T, Rivard B, Sánchez-Azofeifa AG, Féret J-B, Jacquemoud S, Ustin SL (2014) Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis. ISPRS J Photogramm Remote Sens 87:28–38. https://doi.org/10.1016/j.isprsjprs.2013.10.009
Ciais P et al (2005) Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437:529–533. https://doi.org/10.1038/nature03972
Colombo R, Meroni M, Marchesi A, Busetto L, Rossini M, Giardino C, Panigada C (2008) Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sens Environ 112:1820–1834. https://doi.org/10.1016/j.rse.2007.09.005
Combal B et al (2002) Retrieval of canopy biophysical variables from bidirectional reflectance using prior information to solve the ill-posed inverse problem. Remote Sens Environ 84:1–15. https://doi.org/10.1016/S0034-4257(02)00035-4
Coops NC, Smith M-L, Martin ME, Ollinger SV (2003) Prediction of eucalypt foliage nitrogen content from satellite-derived hyperspectral data. IEEE Trans Geosci Remote Sens 41:1338–1346. https://doi.org/10.1109/TGRS.2003.813135
Curran PJ (1989) Remote sensing of foliar chemistry. Remote Sens Environ 30:271–278. https://doi.org/10.1016/0034-4257(89)90069-2
Dawson TP, Curran PJ, Plummer SE (1998) LIBERTY—modeling the effects of leaf biochemical concentration on reflectance spectra. Remote Sens Environ 65:50–60. https://doi.org/10.1016/S0034-4257(98)00007-8
Dechant B, Cuntz M, Vohland M, Schulz E, Doktor D (2017) Estimation of photosynthesis traits from leaf reflectance spectra: correlation to nitrogen content as the dominant mechanism. Remote Sens Environ 196:279–292. https://doi.org/10.1016/j.rse.2017.05.019
Dotzler S, Hill J, Buddenbaum H, Stoffels J (2015) The potential of EnMAP and Sentinel-2 data for detecting drought stress phenomena in deciduous forest communities. Remote Sens 7:14227–14258. https://doi.org/10.3390/rs71014227
Drusch M et al (2017) The FLuorescence EXplorer Mission Concep—ESA’s Earth Explorer 8. IEEE Trans Geosci Remote Sens 55:1273–1284. https://doi.org/10.1109/tgrs.2016.2621820
Eckardt A, Horack J, Lehmann F, Krutz D, Drescher J, Whorton M, Soutullo M (2015) DESIS (DLR earth sensing imaging spectrometer for the ISS-MUSES platform). In: IEEE international geoscience and remote sensing symposium (IGARSS), 26–31 July 2015, pp 1457–1459. https://doi.org/10.1109/igarss.2015.7326053
EEA (2012) Annual environmental indicator report 2012—ecosystem resilience and resource efficiency in a green economy in Europe. European Environment Agency, Copenhagen. https://doi.org/10.2800/487
Ewald M et al (2018) LiDAR derived forest structure data improves predictions of canopy N and P concentrations from imaging spectroscopy. Remote Sens Environ 211:13–25. https://doi.org/10.1016/j.rse.2018.03.038
Fang M, Ju W, Zhan W, Cheng T, Qiu F, Wang J (2017) A new spectral similarity water index for the estimation of leaf water content from hyperspectral data of leaves. Remote Sens Environ 196:13–27. https://doi.org/10.1016/j.rse.2017.04.029
FAO (2010) Guidelines on sustainable forest management in drylands of sub-Saharan Africa. FAO, Rome
Fassnacht F et al (2014) Comparison of feature reduction algorithms for classifying tree species with hyperspectral data on three central European test sites. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2547–2561. https://doi.org/10.1109/jstars.2014.2329390
Fassnacht FE, Stenzel S, Gitelson AA (2015) Non-destructive estimation of foliar carotenoid content of tree species using merged vegetation indices. J Plant Physiol 176:210–217. https://doi.org/10.1016/j.jplph.2014.11.003
Fassnacht FE et al (2016) Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186:64–87. https://doi.org/10.1016/j.rse.2016.08.013
Fawcett D, Verhoef W, Schläpfer D, Schneider FD, Schaepman ME, Damm A (2018) Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling. Remote Sens Environ 204:583–595. https://doi.org/10.1016/j.rse.2017.09.040
Feilhauer H, Asner GP, Martin RE (2015) Multi-method ensemble selection of spectral bands related to leaf biochemistry. Remote Sens Environ 164:57–65. https://doi.org/10.1016/j.rse.2015.03.033
Feingersh T, Ben Dor E (2015) SHALOM—a commercial hyperspectral space mission. In: Qian S-E (ed) Optical payloads for space missions. Wiley, Chichester. https://doi.org/10.1002/9781118945179.ch11
Feret J-B et al (2008) PROSPECT-4 and 5: advances in the leaf optical properties model separating photosynthetic pigments. Remote Sens Environ 112:3030–3043. https://doi.org/10.1016/j.rse.2008.02.012
Féret J-B et al (2011) Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sens Environ 115:2742–2750. https://doi.org/10.1016/j.rse.2011.06.016
Féret JB, Gitelson AA, Noble SD, Jacquemoud S (2017) PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle. Remote Sens Environ 193:204–215. https://doi.org/10.1016/j.rse.2017.03.004
Fink AH, Brücher T, Krüger A, Leckebusch GC, Pinto JG, Ulbrich U (2004) The 2003 European summer heatwaves and drought—synoptic diagnosis and impacts. Weather 59:209–216. https://doi.org/10.1256/wea.73.04
Foster JR, Townsend PA (2004) Linking hyperspectral imagery and forest inventories for forest assessment in the Central Appalachians. In: Yaussy DA, Hix DM, Long RP, Goebel PC (eds) 14th Central hardwood forest conference. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, pp 76–86
Franklin SE (2001) Remote sensing for sustainable forest management. Lewis Publishers, Boca Raton
Frantz D, Röder A, Stellmes M, Hill J (2016) An operational radiometric landsat preprocessing framework for large-area time series applications. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/tgrs.2016.2530856
Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens Environ 41:35–44. https://doi.org/10.1016/0034-4257(92)90059-s
Gara TW, Skidmore AK, Darvishzadeh R, Wang T (2018) Leaf to canopy upscaling approach affects the estimation of canopy traits. GISci Remote Sens. https://doi.org/10.1080/15481603.2018.1540170
Garbulsky MF, Peñuelas J, Gamon J, Inoue Y, Filella I (2011) The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sens Environ 115:281–297. https://doi.org/10.1016/j.rse.2010.08.023
Gastellu-Etchegorry JP, Martin E, Gascon F (2004) DART: a 3D model for simulating satellite images and studying surface radiation budget. Int J Remote Sens 25:73–96. https://doi.org/10.1080/0143116031000115166
Ghamisi P, Plaza J, Chen Y, Li J, Plaza AJ (2017) Advanced spectral classifiers for hyperspectral images: a review. IEEE Geosci Remote Sens Mag 5:8–32. https://doi.org/10.1109/mgrs.2016.2616418
Gitelson AA, Merzlyak MN (1997) Remote estimation of chlorophyll content in higher plant leaves. Int J Remote Sens 18:2691–2697. https://doi.org/10.1080/014311697217558
Gitelson AA, Gritz Y, Merzlyak MN (2003) Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol 160:271–282. https://doi.org/10.1078/0176-1617-00887
Gitelson AA, Keydan GP, Merzlyak MN (2006) Three-band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophys Res Lett. https://doi.org/10.1029/2006gl026457
Green AA, Berman M, Switzer P, Craig MD (1998) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74
Green RO, Painter TH, Roberts DA, Dozier J (2006) Measuring the expressed abundance of the three phases of water with an imaging spectrometer over melting snow. Water Resour Res. https://doi.org/10.1029/2005wr004509
Guanter L et al (2015) The EnMAP spaceborne imaging spectroscopy mission for earth observation. Remote Sens 7:8830–8857. https://doi.org/10.3390/rs70708830
Hank TB, Berger K, Bach H, Clevers JGPW, Gitelson A, Zarco-Tejada P, Mauser W (2019) Spaceborne imaging spectroscopy for sustainable agriculture: contributions and challenges. Surv Geophys. https://doi.org/10.1007/s10712-018-9492-0
Hansen MC, Roy DP, Lindquist E, Adusei B, Justice CO, Altstatt A (2008) A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens Environ 112:2495–2513. https://doi.org/10.1016/j.rse.2007.11.012
Heim R, Jürgens N, Große-Stoltenberg A, Oldeland J (2015) The effect of epidermal structures on leaf spectral signatures of ice plants (Aizoaceae). Remote Sens 7:15862
Held M, Rabe A, Senf C, Svd Linden, Hostert P (2015) Analyzing hyperspectral and hypertemporal data by decoupling feature redundancy and feature relevance. IEEE Geosci Remote Sens Lett 12:983–987. https://doi.org/10.1109/lgrs.2014.2371242
Hernández-Clemente R, Navarro-Cerrillo RM, Zarco-Tejada PJ (2012) Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT + DART simulations. Remote Sens Environ 127:298–315. https://doi.org/10.1016/j.rse.2012.09.014
Homolová L, Malenovský Z, Clevers JGPW, García-Santos G, Schaepman ME (2013) Review of optical-based remote sensing for plant trait mapping. Ecol Complex 15:1–16. https://doi.org/10.1016/j.ecocom.2013.06.003
Hosgood B, Jacquemoud S, Andreoli G, Verdebout J, Pedrini G, Schmuck G (1994) Leaf optical properties experiment 93 (LOPEX93). European Commission, Joint Research Center, Ispra
Hovi A, Raitio P, Rautiainen M (2017) A spectral analysis of 25 boreal tree species. Silva Fenn. https://doi.org/10.14214/sf.7753
Huang Z, Turner BJ, Dury SJ, Wallis IR, Foley WJ (2004) Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens Environ 93:18–29. https://doi.org/10.1016/j.rse.2004.06.008
Huber S, Kneubühler M, Psomas A, Itten K, Zimmermann NE (2008) Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. For Ecol Manag 256:491–501. https://doi.org/10.1016/j.foreco.2008.05.011
Huemmrich KF (2001) The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance. Remote Sens Environ 75:423–431. https://doi.org/10.1016/S0034-4257(00)00184-X
Hultquist C, Chen G, Zhao K (2014) A comparison of Gaussian process regression, random forests and support vector regression for burn severity assessment in diseased forests. Remote Sens Lett 5:723–732. https://doi.org/10.1080/2150704x.2014.963733
Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34:75–91. https://doi.org/10.1016/0034-4257(90)90100-Z
Jacquemoud S, Ustin SL, Verdebout J, Schmuck G, Andreoli G, Hosgood B (1996) Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sens Environ 56:194–202. https://doi.org/10.1016/0034-4257(95)00238-3
Jacquemoud S, Bacour C, Poilvé H, Frangi J-P (2000) Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sens Environ 74:471–481. https://doi.org/10.1016/S0034-4257(00)00139-5
Jacquemoud S et al (2009) PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:S56–S66. https://doi.org/10.1016/j.rse.2008.01.026
Jay S, Bendoula R, Hadoux X, Féret J-B, Gorretta N (2016) A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy. Remote Sens Environ 177:220–236. https://doi.org/10.1016/j.rse.2016.02.029
Knyazikhin Y et al (2013) Hyperspectral remote sensing of foliar nitrogen content. Proc Natl Acad Sci 110:E185–E192. https://doi.org/10.1073/pnas.1210196109
Koetz B, Schaepman M, Morsdorf F, Bowyer P, Itten K, Allgöwer B (2004) Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties. Remote Sens Environ 92:332–344. https://doi.org/10.1016/j.rse.2004.05.015
Koetz B, Sun G, Morsdorf F, Ranson KJ, Kneubühler M, Itten K, Allgöwer B (2007) Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization. Remote Sens Environ 106:449–459. https://doi.org/10.1016/j.rse.2006.09.013
Kokaly RF, Asner GP, Ollinger SV, Martin ME, Wessman CA (2009) Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies. Remote Sens Environ 113(Supplement 1):S78–S91. https://doi.org/10.1016/j.rse.2008.10.018
Kuusk A, Kuusk J, Lang M (2014) Modeling directional forest reflectance with the hybrid type forest reflectance model FRT. Remote Sens Environ 149:196–204. https://doi.org/10.1016/j.rse.2014.03.035
Labate D et al (2009) The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission. Acta Astronaut 65:1429–1436. https://doi.org/10.1016/j.actaastro.2009.03.077
Lausch A, Erasmi S, King D, Magdon P, Heurich M (2016) Understanding forest health with remote sensing-part I—a review of spectral traits, processes and remote-sensing characteristics. Remote Sens 8:1029. https://doi.org/10.3390/rs8121029
Lausch A, Erasmi S, King D, Magdon P, Heurich M (2017) Understanding forest health with remote sensing-part II—a review of approaches and data models. Remote Sens 9:129. https://doi.org/10.3390/rs9020129
Lausch A et al (2018) Understanding forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sens 10:1120. https://doi.org/10.3390/rs10071120
le Maire G, François C, Dufrêne E (2004) Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements. Remote Sens Environ 89:1–28. https://doi.org/10.1016/j.rse.2003.09.004
le Maire G et al (2008) Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass. Remote Sens Environ 112:3846–3864. https://doi.org/10.1016/j.rse.2008.06.005
Lee CM, Cable ML, Hook SJ, Green RO, Ustin SL, Mandl DJ, Middleton EM (2015) An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities. Remote Sens Environ 167:6–19. https://doi.org/10.1016/j.rse.2015.06.012
Li P, Wang Q (2011) Retrieval of leaf biochemical parameters using PROSPECT inversion: a new approach for alleviating ill-posed problems. IEEE Trans Geosci Remote Sens 49:2499–2506. https://doi.org/10.1109/tgrs.2011.2109390
Li D et al (2018) PROCWT: coupling PROSPECT with continuous wavelet transform to improve the retrieval of foliar chemistry from leaf bidirectional reflectance spectra. Remote Sens Environ 206:1–14. https://doi.org/10.1016/j.rse.2017.12.013
Liechty HO, Fristoe C (2013) Response of midrotation pine stands to fertilizer and herbicide application in the western Gulf coastal plain. South J Appl For 37:69–74
Lillesaeter O (1982) Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sens Environ 12:247–254. https://doi.org/10.1016/0034-4257(82)90057-8
Lindner M et al (2010) Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. For Ecol Manag 259:698–709. https://doi.org/10.1016/j.foreco.2009.09.023
Locherer M, Hank T, Danner M, Mauser W (2015) Retrieval of seasonal leaf area index from simulated EnMAP data through optimized LUT-based inversion of the PROSAIL model. Remote Sens 7:10321
Ma Z et al (2012) Regional drought-induced reduction in the biomass carbon sink of Canada’s boreal forests. Proc Natl Acad Sci 109:2423–2427. https://doi.org/10.1073/pnas.1111576109
Malenovský Z et al (in review) Variability and uncertainty challenges in upscaling imaging spectroscopy observations from leaves to vegetation canopies. Surv Geophys
Malenovský Z, Albrechtová J, Lhotáková Z, Zurita-Milla R, Clevers JGPW, Schaepman ME, Cudlín P (2006a) Applicability of the PROSPECT model for Norway spruce needles. Int J Remote Sens 27:5315–5340. https://doi.org/10.1080/01431160600762990
Malenovský Z, Ufer C, Lhotáková Z, Clevers JGPW, Schaepman ME, Albrechtová J, Cudlín P (2006b) A new hyperspectral index for chlorophyll estimation of a forest canopy: area under curve normalised to maximal band depth between 650–725 nm. EARSeL eProc 5:161–172
Martin ME, Plourde LC, Ollinger SV, Smith ML, McNeil BE (2008) A generalizable method for remote sensing of canopy nitrogen across a wide range of forest ecosystems. Remote Sens Environ 112:3511–3519. https://doi.org/10.1016/j.rse.2008.04.008
Martin R, Chadwick K, Brodrick P, Carranza-Jimenez L, Vaughn N, Asner G (2018a) An approach for foliar trait retrieval from airborne imaging spectroscopy of tropical forests. Remote Sens 10:199. https://doi.org/10.3390/rs10020199
Martin RE et al (2018b) Remote measurement of canopy water content in giant sequoias (Sequoiadendron giganteum) during drought. For Ecol Manag 419–420:279–290. https://doi.org/10.1016/j.foreco.2017.12.002
Masek JG et al (2006) A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geosci Remote Sens Lett 3:68–72. https://doi.org/10.1109/lgrs.2005.857030
Matsunaga T et al (2017) Current status of Hyperspectral Imager Suite (HISUI) onboard International Space Station (ISS). In: IEEE international geoscience and remote sensing symposium (IGARSS), 23–28 July 2017, pp 443–446. https://doi.org/10.1109/igarss.2017.8126989
McNeil BE, de Beurs KM, Eshleman KN, Foster JR, Townsend PA (2007a) Maintenance of ecosystem nitrogen limitation by ephemeral forest disturbance: an assessment using MODIS, Hyperion, and Landsat ETM+. Geophys Res Lett. https://doi.org/10.1029/2007gl031387
McNeil BE, Read JM, Driscoll CT (2007b) Foliar nitrogen responses to elevated atmospheric nitrogen deposition in nine temperate forest canopy species. Environ Sci Technol 41:5191–5197. https://doi.org/10.1021/es062901z
Miller J et al (2005) Development of a vegetation fluorescence canopy model—ESTEC contract no. 16365/02/NL/FF—final report
Nieke J, Rast M (2018) Towards the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). In: IGARSS 2018—IEEE international geoscience and remote sensing symposium, 22–27 July 2018, pp 157–159. https://doi.org/10.1109/igarss.2018.8518384
Nink S, Hill J, Buddenbaum H, Stoffels J, Sachtleber T, Langshausen J (2015) Assessing the suitability of future multi- and hyperspectral satellite systems for mapping the spatial distribution of Norway spruce timber volume. Remote Sens 7:12009–12040. https://doi.org/10.3390/rs70912009
North PRJ (1996) Three-dimensional forest light interaction model using a Monte Carlo method. IEEE Trans Geosci Remote Sens 34:946–956. https://doi.org/10.1109/36.508411
Ollinger SV et al (2008) Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proc Natl Acad Sci 105:19336–19341. https://doi.org/10.1073/pnas.0810021105
Omari K, White HP, Staenz K (2009) Multiple scattering within the FLAIR model incorporating the photon recollision probability approach. IEEE Trans Geosci Remote Sens 47:2931–2941. https://doi.org/10.1109/tgrs.2009.2014466
Omari K, White HP, Staenz K, King DJ (2013) Retrieval of forest canopy parameters by inversion of the PROFLAIR leaf-canopy reflectance model using the LUT approach. IEEE J Sel Top Appl Earth Obs Remote Sens 6:715–723. https://doi.org/10.1109/jstars.2013.2240264
Paz-Kagan T et al (2018) Landscape-scale variation in canopy water content of giant sequoias during drought. For Ecol Manag 419–420:291–304. https://doi.org/10.1016/j.foreco.2017.11.018
Peng C et al (2011) A drought-induced pervasive increase in tree mortality across Canada’s boreal forests. Nat Clim Change 1:467–471. https://doi.org/10.1038/nclimate1293
Peñuelas J, Baret F, Filella I (1995) Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica 31:221–230
Rascher U et al (2015) Sun-induced fluorescence—a new probe of photosynthesis: first maps from the imaging spectrometer HyPlant. Glob Change Biol 21:4673–4684. https://doi.org/10.1111/gcb.13017
Rennenberg H, Loreto F, Polle A, Brilli F, Fares S, Beniwal RS, Gessler A (2006) Physiological responses of forest trees to heat and drought. Plant Biol 8:556–571. https://doi.org/10.1055/s-2006-924084
Riaño D, Vaughan P, Chuvieco E, Zarco-Tejada PJ, Ustin SL (2005) Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Trans Geosci Remote Sens 43:819–826. https://doi.org/10.1109/tgrs.2005.843316
Roberts DA, Gardner M, Church R, Ustin S, Scheer G, Green RO (1998) Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models. Remote Sens Environ 65:267–279. https://doi.org/10.1016/S0034-4257(98)00037-6
Rogge DM, Rivard B, Jinkai Z, Jilu F (2006) Iterative spectral unmixing for optimizing per-pixel endmember sets. IEEE Trans Geosci Remote Sens 44:3725–3736. https://doi.org/10.1109/tgrs.2006.881123
Rogge D, Bachmann M, Rivard B, Nielsen AA, Feng J (2014) A spatial–spectral approach for deriving high signal quality eigenvectors for remote sensing image transformations. Int J Appl Earth Obs Geoinf 26:387–398. https://doi.org/10.1016/j.jag.2013.09.007
Schelhaas M-J, Nabuurs G-J, Schuck A (2003) Natural disturbances in the European forests in the 19th and 20th centuries. Glob Change Biol 9:1620–1633. https://doi.org/10.1046/j.1365-2486.2003.00684.x
Schlerf M, Atzberger C (2006) Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data. Remote Sens Environ 100:281–294. https://doi.org/10.1016/j.rse.2005.10.006
Schlerf M, Atzberger C, Hill J, Buddenbaum H, Werner W, Schüler G (2010) Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. Int J Appl Earth Obs Geoinf 12:17–26. https://doi.org/10.1016/j.jag.2009.08.006
Seidl R, Schelhaas M-J, Lexer MJ (2011) Unraveling the drivers of intensifying forest disturbance regimes in Europe. Glob Change Biol 17:2842–2852. https://doi.org/10.1111/j.1365-2486.2011.02452.x
Serbin SP, Singh A, McNeil BE, Kingdon CC, Townsend PA (2014) Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species. Ecol Appl 24:1651–1669. https://doi.org/10.1890/13-2110.1
Singh A, Serbin SP, McNeil BE, Kingdon CC, Townsend PA (2015) Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties. Ecol Appl 25:2180–2197. https://doi.org/10.1890/14-2098.1
Smith M-L, Martin ME, Plourde L, Ollinger SV (2003) Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor. IEEE Trans Geosci Remote Sens 41:1332–1337. https://doi.org/10.1109/tgrs.2003.813128
Somers B, Asner GP (2013) Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests. Remote Sens Environ 136:14–27. https://doi.org/10.1016/j.rse.2013.04.006
Sommer C, Holzwarth S, Heiden U, Heurich M, Müller J, Mauser W (2016) Feature-based tree species classification using hyperspectral and lidar data in the Bavarian Forest National Park. EARSeL eProc 14:49–70. https://doi.org/10.12760/02-2015-2-05
Sonobe R, Wang Q (2018) Nondestructive assessments of carotenoids content of broadleaved plant species using hyperspectral indices. Comput Electron Agric 145:18–26. https://doi.org/10.1016/j.compag.2017.12.022
Stoffels J et al (2015) Satellite-based derivation of high-resolution forest information layers for operational forest management. Forests 6:1982–2013. https://doi.org/10.3390/f6061982
Storch T, Habermeyer M, Eberle S, Muhle H, Mueller RM (2013) Towards a critical design of an operational ground segment for an Earth observation mission. J Appl Remote Sens 7:075381. https://doi.org/10.1117/1.JRS.7.073581
Suárez L, Zarco-Tejada PJ, Sepulcre-Cantó G, Pérez-Priego O, Miller JR, Jiménez-Muñoz JC, Sobrino J (2008) Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens Environ 112:560–575. https://doi.org/10.1016/j.rse.2007.05.009
Swann ALS et al (2018) Continental-scale consequences of tree die-offs in North America: identifying where forest loss matters most. Environ Res Lett 13:055014. https://doi.org/10.1088/1748-9326/aaba0f
Swatantran A, Dubayah R, Roberts D, Hofton M, Blair JB (2011) Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion. Remote Sens Environ 115:2917–2930. https://doi.org/10.1016/j.rse.2010.08.027
Thompson DR, Natraj V, Green RO, Helmlinger MC, Gao B-C, Eastwood ML (2018) Optimal estimation for imaging spectrometer atmospheric correction. Remote Sens Environ 216:355–373. https://doi.org/10.1016/j.rse.2018.07.003
Thompson DR, Guanter L, Berk A, Gao B-C, Richter R, Schläpfer D, Thome KJ (2019) Retrieval of atmospheric parameters and surface reflectance from visible and shortwave infrared imaging spectroscopy data. Surv Geophys. https://doi.org/10.1007/s10712-018-9488-9
Townsend PA, Foster JR, Chastain RA, Currie WS (2003) Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE Trans Geosci Remote Sens 41:1347–1354. https://doi.org/10.1109/tgrs.2003.813205
Townsend PA, Serbin SP, Kruger EL, Gamon JA (2013) Disentangling the contribution of biological and physical properties of leaves and canopies in imaging spectroscopy data. Proc Natl Acad Sci 110:E1074. https://doi.org/10.1073/pnas.1300952110
Townsend PA, Wang Z, Singh A (2017) Prospects for universal foliar trait-retrieval algorithms from imaging spectroscopy: cross-site and cross-platform. Paper presented at the 10th EARSeL SIG imaging spectroscopy workshop, Zurich, Switzerland
Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO (2004) Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54:523–534. https://doi.org/10.1641/0006-3568(2004)054%5b0523:uistse%5d2.0.co;2
Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, Gamon JA, Zarco-Tejada P (2009) Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens Environ 113:S67–S77. https://doi.org/10.1016/j.rse.2008.10.019
Verhoef W (1984) Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens Environ 16:125–141. https://doi.org/10.1016/0034-4257(84)90057-9
Vermote E, Justice C, Claverie M, Franch B (2016) Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sens Environ 185:46–56. https://doi.org/10.1016/j.rse.2016.04.008
Verrelst J, Camps-Valls G, Muñoz-Marí J, Rivera JP, Veroustraete F, Clevers JGPW, Moreno J (2015) Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties—a review. ISPRS J Photogramm Remote Sens 108:273–290. https://doi.org/10.1016/j.isprsjprs.2015.05.005
Verrelst J et al (2019) Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surv Geophys. https://doi.org/10.1007/s10712-018-9478-y
Vohland M, Ludwig M, Harbich M, Emmerling C, Thiele-Bruhn S (2016) Using variable selection and wavelets to exploit the full potential of visible/near infrared spectra for predicting soil properties. J Near Infrared Spectrosc 24:255–269. https://doi.org/10.1255/jnirs.1233
Wang Z, Skidmore AK, Wang T, Darvishzadeh R, Hearne J (2015) Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves. Remote Sens Environ 168:205–218. https://doi.org/10.1016/j.rse.2015.07.007
Wang Z et al (2017) Canopy foliar nitrogen retrieved from airborne hyperspectral imagery by correcting for canopy structure effects. Int J Appl Earth Obs Geoinf 54:84–94. https://doi.org/10.1016/j.jag.2016.09.008
Wang Z, Skidmore AK, Darvishzadeh R, Wang T (2018) Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery. Agric For Meteorol 253–254:247–260. https://doi.org/10.1016/j.agrformet.2018.02.010
Wang Z, Townsend PA, Schweiger AK, Couture JJ, Singh A, Hobbie SE, Cavender-Bares J (2019) Mapping foliar functional traits and their uncertainties across three years in a grassland experiment. Remote Sens Environ 221:405–416. https://doi.org/10.1016/j.rse.2018.11.016
Waske B, van der Linden S, Oldenburg C, Jakimow B, Rabe A, Hostert P (2012) imageRF—a user-oriented implementation for remote sensing image analysis with Random Forests. Environ Model Softw 35:192–193. https://doi.org/10.1016/j.envsoft.2012.01.014
Wessman CA, Aber JD, Peterson DL, Melillo JM (1988) Remote sensing of canopy chemistry and nitrogen cycling in temperate forest ecosystems. Nature 335:154. https://doi.org/10.1038/335154a0
Wessman CA, Aber JD, Peterson DL (1989) An evaluation of imaging spectrometry for estimating forest canopy chemistry. Int J Remote Sens 10:1293–1316. https://doi.org/10.1080/01431168908903969
White HP, Miller JR, Chen JM (2001) Four-scale linear model for anisotropic reflectance (FLAIR) for plant canopies. I. Model description and partial validation. IEEE Trans Geosci Remote Sens 39:1072–1083. https://doi.org/10.1109/36.921425
White JC, Coops NC, Hilker T, Wulder MA, Carroll AL (2007) Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices. Int J Remote Sens 28:2111–2121. https://doi.org/10.1080/01431160600944028
Wold S, Ruhe A, Wold H, Dunn IW (1984) The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 5:735–743. https://doi.org/10.1137/0905052
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst 58:109–130. https://doi.org/10.1016/S0169-7439(01)00155-1
Wolter PT, Townsend PA, Sturtevant BR (2009) Estimation of forest structural parameters using 5 and 10 meter SPOT-5 satellite data. Remote Sens Environ 113:2019–2036. https://doi.org/10.1016/j.rse.2009.05.009
Yáñez-Rausell L, Malenovský Z, Clevers JGPW, Schaepman ME (2014a) Minimizing measurement uncertainties of coniferous needle-leaf optical properties. Part II: experimental setup and error analysis. IEEE J Sel Top Appl Earth Obs Remote Sens 7:406–420. https://doi.org/10.1109/jstars.2013.2292817
Yáñez-Rausell L, Schaepman ME, Clevers JGPW, Malenovský Z (2014b) Minimizing measurement uncertainties of coniferous needle-leaf optical properties, part I: methodological review. IEEE J Sel Top Appl Earth Obs Remote Sens 7:399–405. https://doi.org/10.1109/jstars.2013.2272890
Zhang Q, Middleton EM, Gao B, Cheng Y (2012) Using EO-1 Hyperion to simulate HyspIRI products for a coniferous forest: the fraction of PAR absorbed by chlorophyll (fAPAR_chl) and leaf water content (LWC). IEEE Trans Geosci Remote Sens 50:1844–1852. https://doi.org/10.1109/tgrs.2011.2169267
Acknowledgements
The study was supported within the framework of the EnMAP project (Contract No. 50 EE 1530) by the German Aerospace Center (DLR) and the Federal Ministry of Economic Affairs and Energy, and the CalTech Jet Propulsion Laboratory (Contracts 1579654 and 1590148). The authors thank Willy Werner, Dorothee Krieger, Bernhard Backes, Martin Schlerf, Johannes Stoffels, Sandra Dotzler, Barbara Paschmionka, Marion Lusseau, Max Gerhards, and many others who helped gather the data presented here. We also thank the two anonymous reviewers for highly constructive comments that helped improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hill, J., Buddenbaum, H. & Townsend, P.A. Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems. Surv Geophys 40, 553–588 (2019). https://doi.org/10.1007/s10712-019-09514-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10712-019-09514-2