Advertisement

Remote Sensing of Vegetation

  • Shin NagaiEmail author
  • Hideki Kobayashi
  • Rikie Suzuki
Chapter
Part of the Ecological Studies book series (ECOLSTUD, volume 236)

Abstract

To deeply understand how interactions between global warming and carbon, water, and energy cycles above the permafrost affect terrestrial ecosystems, accurate observations of spatiotemporal variability in (1) aboveground biomass (stems, branches, and leaves), (2) plant functional type (PFT; e.g., deciduous coniferous or deciduous broadleaf forest), and (3) growing season duration (i.e., the period from leaf-flush to leaf-fall) are required from plot to continental scales. Toward this aim, remote-sensing techniques can be used to obtain data remotely and nondestructively. To observe the targeted parameters, these techniques use sensors such as digital cameras, spectral radiometers, and lasers mounted on various platforms including towers, drones, aircraft, and satellites. In this chapter, we first review previous studies that have employed remote-sensing techniques to investigate aboveground biomass, PFT, and growing season duration in eastern Siberia. We then introduce our own studies, which compared in situ field observations with satellite observations, and examine the usability and prospects of remote-sensing techniques, including uncertainties and other related issues, for observing terrestrial ecosystems in eastern Siberia.

Keywords

Aboveground biomass Growing season duration Ground-truth Plant functional type Remote sensing 

References

  1. Buitenwerf R, Rose L, Higgins SI (2015) Three decades of multi-dimensional change in global leaf phenology. Nat Clim Chang 5:364–368CrossRefGoogle Scholar
  2. Delbart N, Le Toan T, Kergoat L, Fedotova V (2006) Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA–AVHRR and SPOT–VGT data (1982–2004). Remote Sens Environ 101:52–62CrossRefGoogle Scholar
  3. Flessa H, Rodionov A, Guggenberger G, Fuchs H, Magdon P, Shibistova O, Zrazhevskaya G, Mikheyeva N, Kasansky OA, Blodau C (2008) Landscape controls of CH4 fluxes in a catchment of the forest tundra ecotone in northern Siberia. Glob Chang Biol 14:2040–2056CrossRefGoogle Scholar
  4. Frankenberg C, Butz A, Toon GC (2011) Disentangling chlorophyll fluorescence from atmospheric scattering effects in O-2 A-band spectra of reflected sun-light. Geophys Res Lett 38:L03801.  https://doi.org/10.1029/2010GL045896 CrossRefGoogle Scholar
  5. Giglio L, Randerson JT, Werf GR (2013) Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J Geophys Res Biogeo 118(1):317–328CrossRefGoogle Scholar
  6. Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213CrossRefGoogle Scholar
  7. Iida SI, Ohta T, Matsumoto K, Nakai T, Kuwada T, Kononov AV et al (2009) Evapotranspiration from understory vegetation in an eastern Siberian boreal larch forest. Agric For Meteorol 149(6):1129–1139CrossRefGoogle Scholar
  8. Iijima Y, Ohta T, Kotani A, Fedorov AN, Kodama Y, Maximov TC (2014) Sap flow changes in relation to permafrost degradation under increasing precipitation in an eastern Siberian larch forest. Ecohydrology 7:177–187CrossRefGoogle Scholar
  9. James ME, Kalluri SN (1994) The Pathfinder AVHRR land data set: an improved coarse resolution data set for terrestrial monitoring. Int J Remote Sens 15(17):3347–3363CrossRefGoogle Scholar
  10. Jin M, Treadon RE (2003) Correcting the orbit drift effect on AVHRR land surface skin temperature measurements. Int J Remote Sens 20:4543–4558CrossRefGoogle Scholar
  11. Kajimoto T, Matsuura Y, Sofronov MA, Volokitina AV, Mori S, Osawa A, Abaimov AP (1999) Above- and belowground biomass and net primary productivity of a Larix gmelinii stand near Tura, Central Siberia. Tree Physiol 19:815–822CrossRefGoogle Scholar
  12. Kajimoto T, Matsuura Y, Osawa A, Abaimov AP, Zyryanova OA, Isaev AP, Yefremov DP, Mori S, Koike T (2006) Size-mass allometry and biomass allocation of two larch species growing on the continuous permafrost region in Siberia. For Ecol Manag 222:314–325CrossRefGoogle Scholar
  13. Knyazikhin Y, Martonchik JV, Myneni RB, Diner DJ, Running SW (1998) Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. J Geophys Res 103(D24):32257CrossRefGoogle Scholar
  14. Kobayashi H, Suzuki R, Kobayashi S (2007) Reflectance seasonality and its relation to the canopy leaf area index in an eastern Siberian larch forest: multi-satellite data and radiative transfer analyses. Remote Sens Environ 106(2):238–252CrossRefGoogle Scholar
  15. Kobayashi H, Delbart N, Suzuki R, Kushida K (2010) A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest. J Geophys Res Biogeo 115:G01002.  https://doi.org/10.1029/2009JG000939 CrossRefGoogle Scholar
  16. Kobayashi H, Yunus AP, Nagai S, Sugiura K, Kim Y, Van Dam B, Nagano H, Zona D, Harazono Y, Bret-Harte MS, Ichii K, Ikawa H, Iwata H, Oechel WC, Ueyama M, Suzuki R (2016) Latitudinal gradient of spruce forest understory and tundra phenology in Alaska as observed from satellite and ground-based data. Remote Sens Environ 177:160–170CrossRefGoogle Scholar
  17. Kushida K, Isaev AP, Maximov TC, Takao G, Fukuda M (2007) Remote sensing of upper canopy leaf area index and forest floor vegetation cover as indicators of net primary productivity in a Siberian larch forest. J Geophys Res Biogeo 112(112):G02003.  https://doi.org/10.1029/2006JG000269 CrossRefGoogle Scholar
  18. Liu Y, Liu R, Pisek J, Chen JM (2017) Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14(5):1093CrossRefGoogle Scholar
  19. Motohka T, Nasahara KN, Oguma H, Tsuchida S (2010) Applicability of green–red vegetation index for remote sensing of vegetation phenology. Remote Sens 2:2369–2387CrossRefGoogle Scholar
  20. Motohka T, Nasahara KN, Murakami K, Nagai S (2011) Evaluation of sub-pixel cloud noises on MODIS daily spectral indices based on in situ measurements. Remote Sens 3:1644–1662CrossRefGoogle Scholar
  21. Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR (1997a) Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386(6626):698CrossRefGoogle Scholar
  22. Myneni RB, Nemani RR, Running SW (1997b) Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans Geosci Remote Sens 35(6):1380–1393CrossRefGoogle Scholar
  23. Myneni RB, Dong J, Tucker CJ, Kaufmann RK, Kauppi PE, Liski J, Zhou L, Alexeyev V, Hughes MK (2001) A large carbon sink in the woody biomass of northern forests. Proc Nat Acad Sci USA 98(26):14784–14789CrossRefGoogle Scholar
  24. Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y, Wang Y, Song X, Zhang Y, Smith GR, Lotsch A (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83(1):214–231CrossRefGoogle Scholar
  25. Nagai S, Nasahara KN, Muraoka H, Akiyama T, Tsuchida S (2010) Field experiments to test the use of the normalized difference vegetation index for phenology detection. Agric For Meteorol 150:152–160CrossRefGoogle Scholar
  26. Nagai S, Maeda T, Gamo M, Muraoka H, Suzuki R, Nasahara KN (2011) Using digital camera images to detect canopy condition of deciduous broad-leaved trees. Plant Ecol Diver 4:78–88CrossRefGoogle Scholar
  27. Nagai S, Saitoh TM, Kurumado K, Tamagawa I, Kobayashi K, Inoue T, Suzuki R, Gamo M, Muraoka H, Nasahara KN (2013) Detection of bio-meteorological year-to-year variation by using digital canopy surface images of a deciduous broad-leaved forest. SOLA 9:106–110CrossRefGoogle Scholar
  28. Nagai S, Inoue T, Ohtsuka T, Kobayashi H, Kurumado K, Muraoka H, Nasahara KN (2014) Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan. Int J Remote Sens 35:3520–3536CrossRefGoogle Scholar
  29. Nagai S, Akitsu T et al (2018) 8 million phenological and sky images from 29 ecosystems from the Arctic to the tropics: the Phenological eyes network. Ecol Res 33(6):1091.  https://doi.org/10.1007/s11284-018-1633-x CrossRefGoogle Scholar
  30. Nasahara KN, Nagai S (2015) Review: development of an in-situ observation network for terrestrial ecological remote sensing—the Phenological eyes network (PEN). Ecol Res 30:211–223CrossRefGoogle Scholar
  31. Piao S, Wang X, Ciais P, Zhu B, Wang TAO, Liu JIE (2011) Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob Chang Biol 17(10):3228–3239CrossRefGoogle Scholar
  32. Pinzon JE, Tucker CJ (2014) A non-stationary 1981-2012 AVHRR NDVI3g time series. Remote Sens 6(8):6929–6960CrossRefGoogle Scholar
  33. Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) A global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr 19:117–134CrossRefGoogle Scholar
  34. Richardson AD, Jenkins JP, Braswell BH, Hollinger DY, Ollinger SV, Smith M-L (2007) Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323–334CrossRefGoogle Scholar
  35. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agri Forest Meteorol 169:156–173CrossRefGoogle Scholar
  36. Sato H, Kobayashi H, Iwahana G, Ohta T (2016) Endurance of larch forest ecosystems in eastern Siberia under warming trends. Ecol Evol 6(16):5690–5704CrossRefGoogle Scholar
  37. Simard M, Pinto N, Fisher JB, Baccini A (2011) Mapping forest canopy height globally with spaceborne lidar. J Geophys Res 116:G04021.  https://doi.org/10.1029/2011JG001708 CrossRefGoogle Scholar
  38. Suzuki R, Nomaki T, Yasunari T (2001) Spatial distribution and its seasonality of satellite-derived vegetation index (NDVI) and climate in Siberia. Int J Climatol 21:1321–1335CrossRefGoogle Scholar
  39. Suzuki R, Hiyama T, Asanuma J, Ohata T (2004) Land surface identification near Yakutsk in eastern Siberia using video images taken from a hedgehopping aircraft. Int J Remote Sens 25(19):4015–4028CrossRefGoogle Scholar
  40. Suzuki R, Masuda K, Dye DG (2007) Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia. Remote Sens Environ 106:387–398CrossRefGoogle Scholar
  41. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  42. Wingate L, Ogée J, Cremonese E, Filippa G, Mizunuma T, Migliavacca M et al (2015) Interpreting canopy development and physiology using a European phenology camera network at flux sites. Biogeosciences 12:5995–6015CrossRefGoogle Scholar
  43. Wright IJ, Reich PB, Westoby M, Ackerly DD, Baruch Z, Bongers F et al (2004) The worldwide leaf economics spectrum. Nature 428:821–827CrossRefGoogle Scholar
  44. Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res Atmos 106(D17):20069–20083CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Japan Agency for Marine-Earth Science and TechnologyYokohamaJapan

Personalised recommendations