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Review of remote sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion

  • role： First author第一作者
  • Affiliation：

    College of Forestry, Nanjing Forestry University, Nanjing 210037, China

    Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

  • Email：swj19891018@163.com
  • Introduction：沈文娟，1989年生，女，讲师，研究方向为遥感、GIS及森林经营与管理。E-mail：swj19891018@163.com
Wenjuan SHEN12,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Forestry, Nanjing Forestry University, Nanjing 210037, China

    Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

  • Email：nfulms@njfu.edu.cn
  • Introduction：李明诗，1973年生，男，教授，研究方向为遥感、GIS及其生态应用及森林资源监测与管理。E-mail：nfulms@njfu.edu.cn
Mingshi LI12*,  
  • Affiliation：

    Department of Geographical Sciences, University of Maryland, College Park 20741, USA

Chengquan HUANG3

résumé

Spatio-temporally explicit forest disturbance information is regarded as a critical mechanism for net ecosystem productivity. However, quantitative and spatio-temporally explicit information on forest disturbance is currently rare for most regions of the world. Unique among earth observation programs, Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat Time Series (LTS) for multi-temporal characterizations. The science and application capacity of Landsat have developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of relevance to management and scientific activities is ensured in a short time. The spatial, spectral, and temporal resolution offered by Landsat data is well suited, increasingly established, and operational in usage for forest management and analysis. Particularly, forest change detection algorithms based on LTS stack provide robust tools for detecting near real-time forest ecosystem changes, whereby a baseline of conditions can be determined for both abrupt and gradual changes and attributed to different drivers. Interest in LTS has been further enhanced by the recent introduction of several novel automated data processing techniques suitable for multi-temporal analysis, especially after the successful launch of highly spatio-temporal resolution satellites. The benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, and sophisticated image processing approaches. Thus, change detection research for forest disturbance with time series remote sensing data has entered a brand-new stage. This review systematically summarizes the research progress and application of multi-temporal forest disturbance monitoring methods based on remote sensing data sources. Considering the significance and advantage of applying time series analysis in change detection, data availability selection, and data processing, various spectral indexes and dense time automatic monitoring methods for forest disturbance are analyzed extensively, and the characteristics of multi-source data and algorithms are summarized. Finally, the improvements based on the existing limitations are explored. At the time of this review, three forest disturbance change detection algorithms are commonly used for LTS: spectral variables, classification analysis, and trajectory analysis. Spectral-based techniques range from single-band reflectance to a host of indexes calculated from different algebraic manipulations of the original spectral bands and their derivatives. Classification-based techniques are an extension of traditional change detection techniques based on two-year images. Trajectory-based methods identify trends and breakpoints corresponding to disturbance events, stability, and recovery time, and they are useful for characterizing different disturbance regimes. The strength and limitations of each method prior to conducting an LTS analysis should be understood by forestry and remote sensing communities, who should ideally select a given method depending on their information needs. In summary, unprecedented advances in the assessment of forest disturbance have been realized in recent and future years using LTS data combined with other high spatial and temporal resolution imagery, especially domestic satellites as information sources.

mots-clés

forest disturbance;Landsat;time series;multiple-source data;change detection methodology

References

  1. 1.
    Alencar A, Asner G P, Knapp D and Zarin D. 2011. Temporal variability of forest fires in eastern Amazonia. Ecological Applications, 21(7): 2397–2412
  2. 2.
    Asner G P, Knapp D E, Balaji A and Paez-Acosta G. 2009. Automated mapping of tropical deforestation and forest degradation: CLASlite. Journal of Applied Remote Sensing, 3(1): 033543
  3. 3.
    Banskota A, Kayastha N, Falkowski M J, Wulder M A, Froese R E and White J C. 2014. Forest monitoring using Landsat time series data: a review. Canadian Journal of Remote Sensing, 40(5): 362–384
  4. 4.
    Brooks E B, Wynne R H, Thomas V A, Blinn C E and Coulston J W. 2014. On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3316–3332
  5. 5.
    Canty M J and Nielsen A A. 2008. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment, 112(3): 1025–1036
  6. 6.
    Canty M J, Nielsen A A and Schmidt M. 2004. Automatic radiometric normalization of multitemporal satellite imagery. Remote Sensing of Environment, 91(3/4): 441–451
  7. 7.
    Chandler R E and Scott E M. 2011. Statistical methods for trend detection and analysis in the environmental sciences. Chichester, West Sussex: John Wiley and Sons Inc
  8. 8.
    Chen G, Hay G J, Carvalho L M T and Wulder M A. 2012. Object-based change detection. International Journal of Remote Sensing, 33(14): 4434–4457
  9. 9.
    Chen X X, Vogelmann J E, Chander G, Ji L, Tolk B, Huang C Q and Rollins M. 2013. Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images. International Journal of Remote Sensing, 34(7): 2432–2453
  10. 10.
    Cohen W, Healey S P, Yang Z Q, Stehman S V, Brewer C K, Brooks E B, Gorelick N, Huang C Q, Hughes M J, Kennedy R E, Loveland T R, Moisen G G, Schroeder T A, Vogelmann J E, Woodcock C E, Yang L M and Zhu Z. 2017. How similar are forest disturbance maps derived from different Landsat time series algorithms?. Forests, 8(4): 98
  11. 11.
    Cohen W B and Goward S N. 2004. Landsat’s role in ecological applications of remote sensing. Bioscience, 54(6): 535–545
  12. 12.
    Cohen W B, Spies T A, Alig R J, Oetter D R, Maiersperger T K and Fiorella M. 2002. Characterizing 23 years (1972-95) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems, 5(2): 122–137
  13. 13.
    Cohen W B, Spies T A and Fiorella M. 1995. Estimating the age and structure of forests in a multi-ownership landscape of Western Oregon, U. S.A. International Journal of Remote Sensing, 16(4): 721–746
  14. 14.
    Cohen W B, Yang Z Q and Kennedy R. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—Tools for calibration and validation. Remote Sensing of Environment, 114(12): 2911–2924
  15. 15.
    Cohen W B, Yang Z Q, Stehman S V, Schroeder T A, Bell D M, Masek J G, Huang C Q and Meigs G W. 2016. Forest disturbance across the conterminous United States from 1985-2012: the emerging dominance of forest decline. Forest Ecology and Management, 360: 242–252
  16. 16.
    Coppin P, Jonckheere I, Nackaerts K, Muys B and Lambin E. 2004. Review article digital change detection methods in ecosystem monitoring: a review. International Journal of Remote Sensing, 25(9): 1565–1596
  17. 17.
    Coppin P R and Bauer M E. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3/4): 207–234
  18. 18.
    De Beurs K M and Henebry G M. 2005. A statistical framework for the analysis of long image time series. International Journal of Remote Sensing, 26(8): 1551–1573
  19. 19.
    Desclée B, Bogaert P and Defourny P. 2006. Forest change detection by statistical object-based method. Remote Sensing of Environment, 102(1/2): 1–11
  20. 20.
    DeVries B, Verbesselt J, Kooistra L and Herold M. 2015. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote Sensing of Environment, 161: 107–121
  21. 21.
    Dong X Y, Fan W Y and Tian T. 2016. Object-based forest type classification with ZY-3 remote sensing data. Journal of Zhejiang A and F University, 33(5): 816–825 (
  22. 22.
    Dutrieux L P, Verbesselt J, Kooistra L and Herold M. 2015. Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia. ISPRS Journal of Photogrammetry and Remote Sensing, 107: 112–125
  23. 23.
    Edwards D P, Tobias J A, Sheil D, Meijaard E and Laurance W F. 2014. Maintaining ecosystem function and services in logged tropical forests. Trends in Ecology and Evolution, 29(9): 511–520
  24. 24.
    Foga S, Scaramuzza P L, Guo S, Zhu Z, Dilley R D, Beckmann T, Schmidt G L, Dwyer J L, Joseph Hughes M and Laue B. 2017. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194: 379–390
  25. 25.
    Franklin S E, Ahmed O S, Wulder M A, White J C, Hermosilla T and Coops N C. 2015. Large area mapping of annual land cover dynamics using multitemporal change detection and classification of landsat time series data. Canadian Journal Of Remote Sensing, 41(4): 293–314
  26. 26.
    Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207–2218
  27. 27.
    Gao F, Masek J G and Wolfe R E. 2009. Automated registration and orthorectification package for Landsat and Landsat-like data processing. Journal of Applied Remote Sensing, 3(1): 033515
  28. 28.
    Gillanders S N, Coops N C, Wulder M A, Gergel S E and Nelson T. 2008. Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends. Progress in Physical Geography, 32(5): 503–528
  29. 29.
    Goodwin N R, Collett L J, Denham R J, Flood N and Tindall D. 2013. Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM + time series. Remote Sensing of Environment, 134: 50–65
  30. 30.
    Goodwin N R, Coops N C, Wulder M A, Gillanders S, Schroeder T A and Nelson T. 2008. Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment, 112(9): 3680–3689
  31. 31.
    Gómez C, White J C, Wulder M A and Alejandro P. 2015. Integrated object-based spatiotemporal characterization of forest change from an annual time series of Landsat image composites. Canadian Journal Of Remote Sensing, 41(4): 271–292
  32. 32.
    Gómez C, Wulder M A, White J C, Montes F and Delgado J A. 2012. Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain. International Journal of Remote Sensing, 33(17): 5546–5573
  33. 33.
    Griffiths P, Kuemmerle T, Kennedy R E, Abrudan I V, Knorn J and Hostert P. 2012. Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania. Remote Sensing of Environment, 118: 199–214
  34. 34.
    Hais M, Jonášová M, Langhammer J and Kučera T. 2009. Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data. Remote Sensing of Environment, 113(4): 835–845
  35. 35.
    Hansen M C, Roy D P, Lindquist E, Adusei B, Justice C O and Altstatt A. 2008. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sensing of Environment, 112(5): 2495–2513
  36. 36.
    Hayes D J and Cohen W B. 2007. Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data. Remote Sensing of Environment, 106(1): 1–16
  37. 37.
    Healey S P, Cohen W B, Yang Z Q and Krankina O N. 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97(3): 301–310
  38. 38.
    Helmer E H, Lefsky M A and Roberts D A. 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. Journal of Applied Remote Sensing, 3(1): 033505
  39. 39.
    Helmer E H, Ruzycki T S, Wunderle J M Jr, Vogesser S, Ruefenacht B, Kwit C, Brandeis T J and Ewert D N. 2010. Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat. Remote Sensing of Environment, 114(11): 2457–2473
  40. 40.
    Hermosilla T, Wulder M A, White J C, Coops N C and Hobart G W. 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158: 220–234
  41. 41.
    Houghton R A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6): 945–958
  42. 42.
    Huang C B, Dian Y Y, Zhou Z X, Wang D and Chen R D. 2015. Forest change detection based on time series images with statistical properties. Journal of Remote Sensing, 19(4): 657–668 (
  43. 43.
    Huang C Q, Goward S N, Masek J G, Gao F, Vermote E F, Thomas N, Schleeweis K, Kennedy R E, Zhu Z L, Eidenshink J C and Townshend J R G. 2009. Development of time series stacks of Landsat images for reconstructing forest disturbance history. International Journal of Digital Earth, 2(3): 195–218
  44. 44.
    Huang C Q, Goward S N, Masek J G, Thomas N, Zhu Z L and Vogelmann J E. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1): 183–198
  45. 45.
    Huang C Q, Ling P Y and Zhu Z L. 2015. North Carolina’s forest disturbance and timber production assessed using time series Landsat observations. International Journal of Digital Earth, 8(12): 947–969
  46. 46.
    Jia M M, Ren C Y, Liu D W, Wang Z M, Tang X G and Dong Z Y. 2014. Object-oriented forest classification based on combination of HJ-1 CCD and MODIS-NDVI data. Acta Ecologica Sinica, 34(24): 7167–7174 (
  47. 47.
    Jin S M and Sader S A. 2005. Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sensing of Environment, 94(3): 364–372
  48. 48.
    Ju J C, Roy D P, Vermote E, Masek J and Kovalskyy V. 2012. Continental-scale validation of MODIS-based and LEDAPS Landsat ETM+ atmospheric correction methods. Remote Sensing of Environment, 122: 175–184
  49. 49.
    Kayastha N, Thomas V, Galbraith J and Banskota A. 2012. Monitoring wetland change using inter-annual landsat time-series data. Wetlands, 32(6): 1149–1162
  50. 50.
    Kennedy R E, Andréfouët S, Cohen W B, Gómez C, Griffiths P, Hais M, Healey S P, Helmer E H, Hostert P, Lyons M B, Meigs G W, Pflugmacher D, Phinn S R, Powell S L, Scarth P, Sen S, Schroeder T A, Schneider A, Sonnenschein R, Vogelmann J E, Wulder M A and Zhu Z. 2014. Bringing an ecological view of change to Landsat-based remote sensing. Frontiers in Ecology and the Environment, 12(6): 339–346
  51. 51.
    Kennedy R E, Cohen W B and Schroeder T A. 2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3): 370–386
  52. 52.
    Kennedy R E, Yang Z Q, Braaten J, Copass C, Antonova N, Jordan C and Nelson P. 2015. Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sensing of Environment, 166: 271–285
  53. 53.
    Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897–2910
  54. 54.
    Lehmann E A, Wallace J F, Caccetta P A, Furby S L and Zdunic K. 2013. Forest cover trends from time series Landsat data for the Australian continent. International Journal of Applied Earth Observation and Geoinformation, 21: 453–462
  55. 55.
    Li A M, Huang C Q, Sun G Q, Shi H, Toney C, Zhu Z L, Rollins M G, Goward S N and Masek J G. 2011. Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data. Remote Sensing of Environment, 115(8): 1837–1849
  56. 56.
    Li L X, Shen R P, Li X H and Guo J. 2016. Comparison of forest disturbance indices based on MODIS time-series data. Remote Sensing Technology and Application, 31(6): 1083–1090 (
  57. 57.
    Li M S, Huang C Q, Shen W J, Ren X Y, Lv Y Y, Wang J R and Zhu Z L. 2016. Characterizing long-term forest disturbance history and its drivers in the Ning-Zhen Mountains, Jiangsu Province of eastern China using yearly Landsat observations (1987-2011). Journal of Forestry Research, 27(6): 1329–1341
  58. 58.
    Li P, Jiang L G and Feng Z M. 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sensing, 6(1): 310–329
  59. 59.
    Li S M, Wang Z H, Han X W and Jia K. 2011. Overview of forest resources change detection methods using remote sensing techniques. Journal of Beijing Forestry University, 33(3): 132–138 (
  60. 60.
    Li T H, Zhang J and Wei J Y. 2015. Monitoring forest disturbances with bootstrapping support vector machine algorithm. Journal of Basic Science and Engineering, 23(2): 308–317 (
  61. 61.
    Liang S L. 2003. Recent algorithm developments in quantitative remote sensing of land surfaces//Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse, France: IEEE: 558–560
  62. 62.
    Liu L Y, Tang H, Caccetta P, Lehmann E A, Hu Y and Wu X L. 2013. Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China. Environmental Monitoring and Assessment, 185(12): 9949–9965
  63. 63.
    Liu S S, Wei X L, Li D Q and Lu D S. 2017. Examining forest disturbance and recovery in the subtropical forest region of Zhejiang Province using Landsat time-series data. Remote Sensing, 9(5): 479
  64. 64.
    Loveland T R and Dwyer J L. 2012. Landsat: building a strong future. Remote Sensing of Environment, 122: 22–29
  65. 65.
    Lu D, Mausel P, Brondízio E and Moran E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365–2401
  66. 66.
    Lyu Y Y, Ren X Y and Li M S. 2014. Assessing forest disturbance patterns over the three forested areas of Nanjing using multi-temporal TM/ETM+ imagery. Journal of Nanjing Forestry University (Natural Sciences Edition), 38(1): 77–82 (
  67. 67.
    Masek J G, Vermote E F, Saleous N E, Wolfe R, Hall F G, Huemmrich K F, Gao F, Kutler J and Lim T K. 2006. A Landsat surface reflectance dataset for North America, 1990-2000. IEEE Geoscience and Remote Sensing Letters, 3(1): 68–72
  68. 68.
    McManus K M, Morton D C, Masek J G, Wang D D, Sexton J O, Nagol J R, Ropars P and Boudreau S. 2012. Satellite-based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010. Global Change Biology, 18(7): 2313–2323
  69. 69.
    Meigs G W, Kennedy R E and Cohen W B. 2011. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests. Remote Sensing of Environment, 115(12): 3707–3718
  70. 70.
    Morton D C, DeFries R S, Nagol J, Souza C M Jr, Kasischke E S, Hurtt G C and Dubayah R. 2011. Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sensing of Environment, 115(7): 1706–1720
  71. 71.
    Muhlbauer A, Spichtinger P and Lohmann U. 2009. Application and comparison of robust linear regression methods for trend estimation. Journal of Applied Meteorology and Climatology, 48(9): 1961–1970
  72. 72.
    Neigh C S R, Masek J G, Bourget P, Rishmawi K, Zhao F, Huang C Q, Cook B D and Nelson R F. 2016. Regional rates of young US forest growth estimated from annual Landsat disturbance history and IKONOS stereo imagery. Remote Sensing of Environment, 173: 282–293
  73. 73.
    Neigh C S R, Masek J G and Nickeson J E. 2013. High-resolution satellite data open for government research. Eos, Transactions American Geophysical Union, 94(13): 121–123
  74. 74.
    Ohmann J L, Gregory M J, Roberts H M, Cohen W B, Kennedy R E and Yang Z Q. 2012. Mapping change of older forest with nearest-neighbor imputation and Landsat time-series. Forest Ecology and Management, 272: 13–25
  75. 75.
    Olsson H. 2009. A method for using Landsat time series for monitoring young plantations in boreal forests. International Journal of Remote Sensing, 30(19): 5117–5131
  76. 76.
    Pflugmacher D, Cohen W B and Kennedy R E. 2012. Using Landsat-derived disturbance history (1972-2010) to predict current forest structure. Remote Sensing of Environment, 122: 146–165
  77. 77.
    Potapov P, Turubanova S and Hansen M C. 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment, 115(2): 548–561
  78. 78.
    Potapov P V, Turubanova S A, Hansen M C, Adusei B, Broich M, Altstatt A, Mane L and Justice C O. 2012. Quantifying forest cover loss in Democratic Republic of the Congo, 2000-2010, with Landsat ETM + data. Remote Sensing of Environment, 122: 106–116
  79. 79.
    Powell S L, Cohen W B, Healey S P, Kennedy R E, Moisen G G, Pierce K B and Ohmann J L. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sensing of Environment, 114(5): 1053–1068
  80. 80.
    Powell S L, Cohen W B, Yang Z Q, Pierce J D and Alberti M. 2008. Quantification of impervious surface in the Snohomish Water Resources Inventory Area of Western Washington from 1972-2006. Remote Sensing of Environment, 112(4): 1895–1908
  81. 81.
    Reiche J, Verbesselt J, Hoekman D and Herold M. 2015. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote Sensing of Environment, 156: 276–293
  82. 82.
    Richter R, Kellenberger T and Kaufmann H. 2009. Comparison of topographic correction methods. Remote Sensing, 1(3): 184–196
  83. 83.
    Röder A, Hill J, Duguy B, Alloza J A and Vallejo R. 2008. Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain). Remote Sensing of Environment, 112(1): 259–273
  84. 84.
    Rogan J, Franklin J and Roberts D A. 2002. A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery. Remote Sensing of Environment, 80(1): 143–156
  85. 85.
    Roy D R, Boschetti L and Trigg S N. 2006. Remote sensing of fire severity: assessing the performance of the normalized Burn ratio. IEEE Geoscience and Remote Sensing Letters, 3(1): 112–116
  86. 86.
    Schmidt M, Lucas R, Bunting P, Verbesselt J and Armston J. 2015. Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158: 156–168
  87. 87.
    Schroeder T A, Cohen W B, Song C H, Canty M J and Yang Z Q. 2006. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sensing of Environment, 103(1): 16–26
  88. 88.
    Schroeder T A, Healey S P, Moisen G G, Frescino T S, Cohen W B, Huang C Q, Kennedy R E and Yang Z Q. 2014. Improving estimates of forest disturbance by combining observations from Landsat time series with U. S. Forest Service Forest Inventory and Analysis data. Remote Sensing of Environment, 154: 61–73
  89. 89.
    Schroeder T A, Schleeweis K G, Moisen G G, Toney C, Cohen W B, Freeman E A, Yang Z Q and Huang C Q. 2017. Testing a Landsat-based approach for mapping disturbance causality in U. S. forests. Remote Sensing of Environment, 195: 230–243
  90. 90.
    Schroeder T A, Wulder M A, Healey S P and Moisen G G. 2011. Mapping wildfire and clearcut harvest disturbances in boreal forests with Landsat time series data. Remote Sensing of Environment, 115(6): 1421–1433
  91. 91.
    Shen W J and Li M S. 2014. Method for Landsat dense time series data format unification and surface reflectance conversion. Remote Sensing for Land and Resources, 26(4): 78–84 (
  92. 92.
    Shen W J and Li M S. 2017. Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations. Acta Ecologica Sinica, 37(5): 1438–1449 (
  93. 93.
    Shen W J, Li M S, Huang C Q and Wei A S. 2016. Quantifying live aboveground biomass and forest disturbance of mountainous natural and plantation forests in Northern Guangdong, China, based on multi-temporal Landsat, PALSAR and field plot data. Remote Sensing, 8(7): 595
  94. 94.
    Shen W J, Li M S and Wei A S. 2017. Spatio-temporal variations in plantation forests’ disturbance and recovery of Northern Guangdong Province using yearly Landsat time series observations (1986-2015). Chinese Geographical Science, 27(4): 600–613
  95. 95.
    Singh A. 1989. Review article digital change detection techniques using remotely-sensed Data. International Journal of Remote Sensing, 10(6): 989–1003
  96. 96.
    Song C and Woodcock C E. 2003. Monitoring forest succession with multitemporal Landsat images: factors of uncertainty. IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2557–2567
  97. 97.
    Song C H, Woodcock C E and Li X W. 2002. The spectral/temporal manifestation of forest succession in optical imagery: the potential of multitemporal imagery. Remote Sensing of Environment, 82(2/3): 285–302
  98. 98.
    Song C H, Woodcock C E, Seto K C, Lenney M P and Macomber S A. 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects. Remote Sensing of Environment, 75(2): 230–244
  99. 99.
    Sonnenschein R, Kuemmerle T, Udelhoven T, Stellmes M and Hostert P. 2011. Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate. Remote Sensing of Environment, 115(6): 1408–1420
  100. 100.
    Souza C M Jr, Siqueira J V, Sales M H, Fonseca A V, Ribeiro J G, Numata I, Cochrane M A, Barber C P, Roberts D A and Barlow J. 2013. Ten-year Landsat classification of deforestation and forest degradation in the Brazilian Amazon. Remote Sensing, 5(11): 5493–5513
  101. 101.
    Sulla-Menashe D, Kennedy R E, Yang Z Q, Braaten J, Krankina O N and Friedl M A. 2014. Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation. Remote Sensing of Environment, 151: 114–123
  102. 102.
    Tan B, Masek J G, Wolfe R, Gao F, Huang C Q, Vermote E F, Sexton J O and Ederer G. 2013. Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment, 136: 469–483
  103. 103.
    Tan B, Wolfe R, Masek J, Gao F and Vermote E F. 2010. An illumination correction algorithm on Landsat-TM data//Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE: 1964–1967
  104. 104.
    Teillet P M, Barker J L, Markham B L, Irish R R, Fedosejevs G and Storey J C. 2001. Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote Sensing of Environment, 78(1/2): 39–54
  105. 105.
    Thomas N E, Huang C Q, Goward S N, Powell S, Rishmawi K, Schleeweis K and Hinds A. 2011. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sensing of Environment, 115(1): 19–32
  106. 106.
    Turner D P, Ritts W D, Kennedy R E, Gray A N and Yang Z Q. 2015. Effects of harvest, fire, and pest/pathogen disturbances on the West Cascades ecoregion carbon balance. Carbon Balance and Management, 10: 12
  107. 107.
    Verbesselt J, Hyndman R, Newnham G and Culvenor D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106–115
  108. 108.
    Verbesselt J, Zeileis A and Herold M. 2012. Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123: 98–108
  109. 109.
    Vermote E, Justice C, Claverie M and Franch B. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185: 46–56
  110. 110.
    Vermote E F, El Saleous N Z and Justice C O. 2002. Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment, 83(1/2): 97–111
  111. 111.
    Vicente-Serrano S M, Pérez-Cabello F and Lasanta T. 2008. Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sensing of Environment, 112(10): 3916–3934
  112. 112.
    Vogelmann J E, Gallant A L, Shi H and Zhu Z. 2016. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sensing of Environment, 185: 258–270
  113. 113.
    Vogelmann J E, Helder D, Morfitt R, Choate M J, Merchant J W and Bulley H. 2001. Effects of landsat 5 thematic mapper and Landsat 7 enhanced thematic mapper plus radiometric and geometric calibrations and corrections on landscape characterization. Remote Sensing of Environment, 78(1/2): 55–70
  114. 114.
    Vogelmann J E, Xian G, Homer C and Tolk B. 2012. Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment, 122: 92–105
  115. 115.
    Wang X Y, Huang H B, Gong P, Biging G S, Xin Q C, Chen Y L, Yang J and Liu C X. 2016. Quantifying multi-decadal change of planted forest cover using airborne lidar and Landsat imagery. Remote Sensing, 8(1): 62
  116. 116.
    Wulder M A, Hall R J, Coops N C and Franklin S E. 2004. High spatial resolution remotely sensed data for ecosystem characterization. Bioscience, 54(6): 511–521
  117. 117.
    Wulder M A, Masek J G, Cohen W B, Loveland T R and Woodcock C E. 2012. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122: 2–10
  118. 118.
    Wulder M A, White J C, Goward S N, Masek J G, Irons J R, Herold M, Cohen W B, Loveland T R and Woodcock C E. 2008. Landsat continuity: issues and opportunities for land cover monitoring. Remote Sensing of Environment, 112(3): 955–969
  119. 119.
    Xin Q C, Olofsson P, Zhu Z, Tan B and Woodcock C E. 2013. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sensing of Environment, 135: 234–247
  120. 120.
    Yang C and Shen R P. 2015. Progress in the study of forest disturbance by remote sensing. Remote Sensing for Land and Resources, 27(1): 1–8 (
  121. 121.
    Yang C, Shen R P, Yu D W, Liu R G and Chen J M. 2013. Forest disturbance monitoring based on the time-series trajectory of remote sensing index. Journal of Remote Sensing, 17(5): 1246–1263 (
  122. 122.
    Yang J, Weisberg P J and Bristow N A. 2012. Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: comparison of vegetation indices and spectral mixture analysis. Remote Sensing of Environment, 119: 62–71
  123. 123.
    Yu D W, Shen R P, Yang C and Liu R G. 2013. Analysis of forest disturbance and its driving factors in Wuning county. Journal of Ecology and Rural Environment, 29(5): 581–586 (
  124. 124.
    Zhang L H, Pang Y, Yue C R and Li Z Y. 2013. Forest disturbance automatic identification method based on time series Landsat image of tasseled cap transformation. Forest Inventory and Planning, 38(2): 6–12, 19 (
  125. 125.
    Zhao F, Huang C Q and Zhu Z L. 2015. Use of vegetation change tracker and support vector machine to map disturbance types in Greater Yellowstone Ecosystems in a 1984-2010 Landsat time series. IEEE Geoscience and Remote Sensing Letters, 12(8): 1650–1654
  126. 126.
    Zhu J J and Liu Z G. 2004. A review on disturbance ecology of forest. Chinese Journal of Applied Ecology, 15(10): 1703–1710 (
  127. 127.
    Zhu S Y, Zhang Y, Zhang H L, Cao Y and Zhang G X. 2014. Progress of researches on monitoring large-area forest disturbance by Landsat satellite images. Remote Sensing for Land and Resources, 26(2): 5–10 (
  128. 128.
    Zhu X L, Helmer E H, Gao F, Liu D S, Chen J and Lefsky M A. 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165–177
  129. 129.
    Zhu Z and Woodcock C E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144: 152–171
  130. 130.
    Zhu Z, Woodcock C E, Holden C and Yang Z Q. 2015. Generating synthetic Landsat images based on all available Landsat data: predicting Landsat surface reflectance at any given time. Remote Sensing of Environment, 162: 67–83
  131. 131.
    Zhu Z, Woodcock C E and Olofsson P. 2012. Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sensing of Environment, 122: 75–91

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