New Forests

, Volume 49, Issue 1, pp 105–121 | Cite as

Monitoring of post-fire forest regeneration under different restoration treatments based on ALOS/PALSAR data

  • Wei ChenEmail author
  • Houzhi Jiang
  • Kazuyuki Moriya
  • Tetsuro Sakai
  • Chunxiang Cao


Post-fire forest regeneration is crucial to forest management. Three different restoration treatments including natural regeneration (NR), artificial regeneration (AR), and artificial promotion (AP), were adopted in the Greater Hinggan Mountain area of China after a serious fire occurred on May 6, 1987. NR is a control treatment where recovery occurs naturally without intervention, AR comprises salvage logging followed by planting, while AP includes regeneration by removing dead trees, weeding, and tidying to promote seed germination. In this study, the objective was to detect and compare the effects of the three restoration treatments using radar indices derived from ALOS/PALSAR data. Four time-series SAR images were pre-processed to acquire the backscattering coefficients. Then the coefficients in both HH and HV polarization were examined and two radar vegetation indices were derived and evaluated, based on which, the post-fire forest dynamics under different restoration treatments were detected and compared. The results showed that the forests under NR presented a completely different recovery trajectory compared to those under the other two treatments. This difference could be characterized by both the backscattering intensity in HH and HV polarization and two radar indices. This study indicated the effects of different restoration treatments, as well as demonstrated the applicability and efficiency of radar remote sensing techniques in forest monitoring and management.


Post-fire restoration Forest regeneration Restoration treatments ALOS/PALSAR RRVI RNDVI 



The work in this paper was financially supported by the National Key Research and Development Program of China (No. 2016YFB0501505), the Natural Science Foundation of China (No. 41601368) and the Japanese Government Monbukagakusho (MEXT) Scholarship (Grant No. 113378). The authors are grateful to Professor Shilei Lu from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, for the guidance and suggestions in designing the technical plan of the sampling design. We express our gratitude to Dr. Zhou Fang, Dr. Haibing Xiang, and Dr. Mingren Huang, as well as the forestry technicians and workers from local forestry bureaus, for their assistance in the field work.


  1. Ascoli D, Castagneri D, Valsecchi C, Conedera M, Bovio G (2013) Post-fire restoration of beech stands in the Southern Alps by natural regeneration. Ecol Eng 54:210–217CrossRefGoogle Scholar
  2. Asselin H, Fortin MJ, Bergeron Y (2001) Spatial distribution of late-successional coniferous species regeneration following disturbance in southwestern Quebec boreal forest. For Ecol Manag 140:29–37CrossRefGoogle Scholar
  3. Avtar R, Sawada H, Takeuchi W, Singh G (2012) Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation. Geocarto Int 27:119–137CrossRefGoogle Scholar
  4. Basuki TM, Skidmore AK, Hussin YA, Van Duren I (2013) Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data. Int J Remote Sens 34:4871–4888CrossRefGoogle Scholar
  5. Beghin R, Lingua E, Garbarino M, Lonati M, Bovio G, Motta R, Marzano R (2010) Pinus sylvestris forest regeneration under different post-fire restoration practices in the northwestern Italian Alps. Ecol Eng 36:1365–1372CrossRefGoogle Scholar
  6. Boer MM, Macfarlane C, Norris J, Sadler RJ, Wallace J, Grierson PF (2008) Mapping burned areas and burn severity patterns in SW Australian eucalypt forest using remotely-sensed changes in leaf area index. Remote Sens Environ 112:4358–4369CrossRefGoogle Scholar
  7. Bontemps S, Langner A, Defourny P (2012) Monitoring forest changes in Borneo on a yearly basis by an object-based change detection algorithm using SPOT-VEGETATION time series. Int J Remote Sens 33:4673–4699CrossRefGoogle Scholar
  8. Camac JS, Williams RJ, Wahren CH, Morris WK, Morgan JW (2013) Post-fire regeneration in alpine heathland: does fire severity matter? Austral Ecol 38:199–207CrossRefGoogle Scholar
  9. Canisius F, Fernandes R (2012) ALOS PALSAR L-band polarimetric SAR data and in situ measurements for leaf area index assessment. Remote Sens Lett 3:221–229CrossRefGoogle Scholar
  10. Chang Y, He HS, Bishop I, Hu YM, Bu RC, Xu CG, Li XZ (2007) Long-term forest landscape responses to fire exclusion in the Great Xing’an Mountains, China. Int J Wildland Fire 16:34–44CrossRefGoogle Scholar
  11. Chen W, Cao CX, He QS, Guo HD, Zhang H, Li RQ, Zheng S, Xu M, Gao MX, Zhao J, Li S, Ni XL, Jia HC, Ji W, Tian R, Liu C, Zhao YX, Li JL (2010) Quantitative estimation of the shrub canopy LAI from atmosphere-corrected HJ-1 CCD data in Mu Us Sandland. Sci China Ser D Earth Sci 53:26–33CrossRefGoogle Scholar
  12. Chen HW, Hu YM, Chang Y, Bu RC, Li YH, Liu M (2011) Simulating impact of larch caterpillar (Dendrolimus superans) on fire regime and forest landscape in Da Hinggan Mountains, Northeast China. Chin Geogr Sci 21:575–586CrossRefGoogle Scholar
  13. Chen W, Moriya K, Sakai T, Koyama L, Cao CX (2014a) Post-fire forest regeneration under different restoration treatments in the Greater Hinggan Mountain area of China. Ecol Eng 70:304–311CrossRefGoogle Scholar
  14. Chen W, Moriya K, Sakai T, Koyama L, Cao CX (2014b) Monitoring of post-fire forest recovery under different restoration modes based on time series Landsat data. Eur J Remote Sens 47:153–168CrossRefGoogle Scholar
  15. Chowdhury TA, Thiel C, Schmullius C, Stelmaszczuk-Gorska M (2013) Polarimetric parameters for growing stock volume estimation using ALOS PALSAR L-band data over Siberian forests. Remote Sens 5:5725–5756CrossRefGoogle Scholar
  16. Cohen WB, Healey SP, Yang Z, Stehman SV, Brewer CK, Brooks EB, Gorelick N, Huang C, Hughes M, Kennedy R, Loveland T, Moisen G, Schroeder T, Vogelmann J, Woodcock C, Yang LM, Zhu Z (2017) How similar are forest disturbance maps derived from different Landsat time series algorithms? Forests 8:98CrossRefGoogle Scholar
  17. Cornforth WA, Fatoyinbo TE, Freemantle TP, Pettorelli N (2013) Advanced land observing satellite phased array type L-band SAR (ALOS PALSAR) to inform the conservation of mangroves: Sundarbans as a case study. Remote Sens 5:224–237CrossRefGoogle Scholar
  18. Cuevas-Gonzalez M, Gerard F, Balzter H, Riano D (2009) Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices. Glob Change Biol 15:561–577CrossRefGoogle Scholar
  19. David AP, Ram O, Stephen CH (2008) Forest ecosystems, 2nd edn. The Johns Hopkins University Press, BaltimoreGoogle Scholar
  20. Díaz-Delgado R, Pons X (2001) Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975–1995 analysis of vegetation recovery after fire. For Ecol Manag 147:67–74CrossRefGoogle Scholar
  21. Forkel M, Thonicke K, Beer C, Cramer W, Bartalev S, Schmullius C (2012) Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environ Res Lett 7:044021CrossRefGoogle Scholar
  22. Gauthier S, Boucher D, Morissette J, De Grandpre L (2010) Fifty-seven years of composition change in the eastern boreal forest of Canada. J Veg Sci 21:772–785Google Scholar
  23. Gromtsev A (2002) Natural disturbance dynamics in the boreal forests of European Russia: a review. Silva Fenn 36:41–55CrossRefGoogle Scholar
  24. Harrell PA, BourgeauChavez LL, Kasischke ES, French NHF, Christensen NL (1995) Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest. Remote Sens Environ 54:247–260CrossRefGoogle Scholar
  25. Hoan NT, Tateishi R, Alsaaideh B, Ngigi T, Alimuddin I, Johnson B (2013) Tropical forest mapping using a combination of optical and microwave data of ALOS. Int J Remote Sens 34:139–153CrossRefGoogle Scholar
  26. Isoguchi O, Shimada M, Uryu Y (2009) A preliminary study on deforestation monitoring in Sumatra Island by PALSAR. In: Proceedings of 2009 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE Geoscience and Remote Sensing Society, New York, USAGoogle Scholar
  27. Johnstone JF, Chapin FS, Foote J, Kemmett S, Price K, Viereck L (2004) Decadal observations of tree regeneration following fire in boreal forests. Can J For Res-Rev 34:267–273CrossRefGoogle Scholar
  28. Kasischke ES, Tanase MA, Bourgeau-Chavez LL, Borr M (2011) Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data. Remote Sens Environ 115:227–232CrossRefGoogle Scholar
  29. Kobayashi S, Widyorini R, Kawai S, Omura Y, Sanga-Ngoie K, Supriadi B (2012) Backscattering characteristics of L-band polarimetric and optical satellite imagery over planted acacia forests in Sumatra,Indonesia. J Appl Remote Sens 6:063525CrossRefGoogle Scholar
  30. Lucas R, Armston J, Fairfax R, Fensham R, Accad A, Carreiras J, Kelley J, Bunting P, Clewley D, Bray S, Metcalfe D, Dwyer J, Bowen M, Eyre T, Laidlaw M, Shimada M (2010) An evaluation of the ALOS PALSAR L-band backscatter-above ground biomass relationship Queensland, Australia: impacts of surface moisture condition and vegetation structure. IEEE J Sel Top Appl Earth Observ Remote Sens 3:576–593CrossRefGoogle Scholar
  31. Mari N, Laneve G, Cadau E, Porcasi X (2012) Fire damage assessment in Sardinia: the use of ALOS/PALSAR data for post fire effects management. Eur J Remote Sens 45:233–241CrossRefGoogle Scholar
  32. Marzano R, Garbarino M, Marcolin E, Pividori M, Lingua E (2013) Deadwood anisotropic facilitation on seedling establishment after a stand-replacing wildfire in Aosta Valley (NW Italy). Ecol Eng 51:117–122CrossRefGoogle Scholar
  33. Masek JG, Huang CQ, Wolfe R, Cohen W, Hall F, Kutler J, Nelson P (2008) North American forest disturbance mapped from a decadal Landsat record. Remote Sens Environ 112:2914–2926CrossRefGoogle Scholar
  34. Millin-Chalabi G, McMorrow J, Agnew C (2014) Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR. Int J Remote Sens 35:54–69CrossRefGoogle Scholar
  35. Mitchard ETA, Saatchi SS, Woodhouse IH, Nangendo G, Ribeiro NS, Williams M, Ryan CM, Lewis SL, Feldpausch TR, Meir P (2009) Using satellite radar backscatter to predict above-ground woody biomass: a consistent relationship across four different African landscapes. Geophys Res Lett 36:L23401CrossRefGoogle Scholar
  36. Moris JV, Vacchiano G, Ravetto Enri S, Lonati M, Motta R, Ascoli D (2017) Resilience of European larch (Larix decidua Mill.) forests to wildfires in the western Alps. New For 48:663–683CrossRefGoogle Scholar
  37. Otoda T, Doi T, Sakamoto K, Hirobe M, Nachin B, Yoshikawa K (2013) Frequent fires may alter the future composition of the boreal forest in northern Mongolia. J For Res 18:246–255CrossRefGoogle Scholar
  38. Palomeque X, Günter S, Siddons D, Hildebrandt P, Stimm B, Aguirre N, Arias R, Weber M (2017) Natural or assisted succession as approach of forest recovery on abandoned lands with different land use history in the Andes of Southern Ecuador. New For 48:643–662CrossRefGoogle Scholar
  39. Pearson RL, Miller LD (1972) Remote Mapping of Standing crop biomass for estimation of the productivity of the shortgrass prairie. In: Proceedings of the 8th international symposium on remote sensing of environment. Willow Run Laboratories, Environmental Research Institute of Michigan, Ann Arbor, Michigan USA, pp 1355–1381Google Scholar
  40. Polychronaki A, Gitas IZ, Veraverbeke S, Debien A (2013) Evaluation of ALOS PALSAR imagery for burned area mapping in greece using object-based classification. Remote Sens 5:5680–5701CrossRefGoogle Scholar
  41. Ranson KJ, Kovacs K, Sun G, Kharuk VI (2003) Disturbance recognition in the boreal forest using radar and Landsat-7. Can J Remote Sens 29:271–285CrossRefGoogle Scholar
  42. Ruthrof KX, Bader MKF, Matusick G, Jakob S, Hardy GESJ (2016) Promoting seedling physiological performance and early establishment in degraded Mediterranean-type ecosystems. New For 47:357–376CrossRefGoogle Scholar
  43. Sansevero JBB, Prieto PV, Sánchez-Tapia A, Braga JMA, Rodrigues PJFP (2017) Past land-use and ecological resilience in a lowland Brazilian Atlantic Forest: implications for passive restoration. New For 48:573–586CrossRefGoogle Scholar
  44. Schulze ED, Wirth C, Mollicone D, Ziegler W (2005) Succession after stand replacing disturbances by fire, wind throw, and insects in the dark Taiga of Central Siberia. Oecologia 146:77–88CrossRefPubMedGoogle Scholar
  45. Starr G, Staudhammer CL, Loescher HW, Mitchell R, Whelan A, Hiers JK, O’Brien JJ (2015) Time series analysis of forest carbon dynamics: recovery of Pinus palustris physiology following a prescribed fire. New For 46:63–90CrossRefGoogle Scholar
  46. Suzuki R, Kim Y, Ishii R (2013) Sensitivity of the backscatter intensity of ALOS/PALSAR to the above-ground biomass and other biophysical parameters of boreal forest in Alaska. Polar Sci 7:100–112CrossRefGoogle Scholar
  47. Tanase MA, Santoro M, de la Riva J, Perez-Cabello F, Le Toan T (2010) Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in mediterranean pine forests. IEEE Trans Geosci Remote Sens 48:3663–3675CrossRefGoogle Scholar
  48. Tanase MA, Santoro M, Aponte C, de la Riva J (2014) Polarimetric properties of burned forest areas at C- and L-band. IEEE J Sel Top Appl Earth Observ Remote Sens 7:267–276CrossRefGoogle Scholar
  49. Tian XR, McRae DJ, Jin JZ, Shu LF, Zhao FJ, Wang MY (2011) Wildfires and the Canadian forest fire weather index system for the Daxing’anling region of China. Int J Wildland Fire 20:963–973CrossRefGoogle Scholar
  50. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150CrossRefGoogle Scholar
  51. Uemura S, Kanda F, Isaev AP, Tsujii T (1997) Forest structure and succession in southeastern Siberia. Veg Sci 14:119–127Google Scholar
  52. Veraverbeke S, Gitas I, Katagis T, Polychronaki A, Somers B, Goossens R (2012) Assessing post-fire vegetation recovery using red-near infrared vegetation indices: accounting for background and vegetation variability. ISPRS-J Photogramm Remote Sens 68:28–39CrossRefGoogle Scholar
  53. Watanabe M, Shimada M, Rosenqvist A, Tadono T, Matsuoka M, Romshoo SA, Ohta K, Furuta R, Nakamura K, Moriyama T (2006) Forest structure dependency of the relation between L-band sigma0 and biophysical parameters. IEEE Trans Geosci Remote Sens 44:3154–3165CrossRefGoogle Scholar
  54. Whittle M, Quegan S, Uryu Y, Stuewe M, Yulianto K (2012) Detection of tropical deforestation using ALOS-PALSAR: a Sumatran case study. Remote Sens Environ 124:83–98CrossRefGoogle Scholar
  55. Wotton BM, Nock CA, Flannigan MD (2010) Forest fire occurrence and climate change in Canada. Int J Wildland Fire 19:253–271CrossRefGoogle Scholar
  56. Zhang QF, Pavlic G, Chen WJ, Latifovic R, Fraser R, Cihlar J (2004) Deriving stand age distribution in boreal forests using SPOT VEGETATION and NOAA AVHRR imagery. Remote Sens Environ 91:405–418CrossRefGoogle Scholar
  57. Zhao KY, Zhang WF, Zhou YW (1994) The impact of Da xing’an ling forest fires on environment and its countermeasures. Science Press, BeijingGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Wei Chen
    • 1
    • 2
    Email author
  • Houzhi Jiang
    • 1
  • Kazuyuki Moriya
    • 2
  • Tetsuro Sakai
    • 2
  • Chunxiang Cao
    • 1
  1. 1.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.Biosphere Informatics Laboratory, Department of Social Informatics, Graduate School of InformaticsKyoto UniversityKyotoJapan

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