Changing Trends of Biomass and Carbon Pools in Mediterranean Pine Forests

  • Cristina GómezEmail author
  • Joanne C. White
  • Michael A. Wulder
Part of the Managing Forest Ecosystems book series (MAFE, volume 34)


The amount of biomass in forest ecosystems is critical information for global carbon cycle modelling. Determination of forest function as a sink or source of carbon is likewise relevant for both scientific applications and policy formulation. The quantity and function of forest biomass in the global carbon cycle is dynamic and changes as a result of natural and anthropogenic processes. This dynamism necessitates monitoring capacity that enables the characterization of changes in forest biomass over time and space. By combining field inventory and remotely sensed data, it is possible to characterize the quantity of biomass for a single date, or to characterize trends in quantity and function of forest biomass through time. Field inventory data provides accurate information for calibration of spatially extensive remotely sensed data models and for model validation as well. Historical, repeat measures of the same field plots facilitate the estimation of temporal trends in biomass accrual or removal, as well as carbon pooling processes. Remotely sensed data enable the inference of trends over large areas, and historical data archives can support retrospective analyses and the establishment of a baseline for future monitoring efforts. This chapter describes some of the opportunities provided by synergies between field measures and remotely sensed data for biomass and carbon assessment over large areas, and describes a case study in the Mediterranean pines of Spain, in which biomass and carbon pooling for the period 1984 to 2009 are estimated with a time series of Landsat imagery supported with data from the Spanish National Forest Inventory.


Carbon Stock Forest Biomass National Forest Inventory Process Indicator Temporal Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was done under the project “Estructura, dinámica y selvicultura para la conservación y el uso sostenible de los bosques en el Sistema Central” (VA-096-A05) with funding from Consejería de Educación, Junta de Castilla y León, Plan Regional I+D+I. Field data was provided by Consejería de Medio Ambiente y Ordenación Territorial de Castilla y León.


  1. Andersson K, Evans TP, Richards KR (2009) National forest carbon inventories: policy needs and assessment capacity. Clim Chang 93:69–101CrossRefGoogle Scholar
  2. Baccini A, Friedl MA, Woodcock CE, Warbington R (2004) Forest biomass estimation over regional scales using multisource data. Geophys Res Lett, 31, L10501, doi:10.1029/2004GL019782Google Scholar
  3. Baccini A, Friedl MA, Woodcock CE, Zhu Z (2007) Scaling field data to calibrate and validate moderate spatial resolution remote sensing models. Photogramm Eng Remote Sens 73:945–954CrossRefGoogle Scholar
  4. Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese RE, White JC (2014) Forest monitoring using Landsat time series data: a review. Can J Remote Sens 40:362–384CrossRefGoogle Scholar
  5. Barlow J, Peres CA (2004) Ecological responses to El Niño-induced surface fires in central Brazilian Amazonia: management implications for flammable tropical forests. Philos Trans R Soc 359:367–380CrossRefGoogle Scholar
  6. Blackard JA, Finco MV, Helmer EH, Holden GR, Hoppus ML, Jacobs DM, Lister AJ, Moisen GG, Nelson MD, Riemann R, Ruefenacht B, Slajanu D, Weyermann DL, Winterberger KC, Brandeis TJ, Czaplewski RL, McRoberts RE, Patterson PL, Tymcio RP (2008) Mapping US forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sens Environ 112:1658–1677CrossRefGoogle Scholar
  7. Bortolot ZJ, Wynne RH (2005) Estimating forest biomass using small footprint LiDAR data: an individual tree-based approach that incorporates training data. IPRS J Photogramm Remote Sens 59:342–360CrossRefGoogle Scholar
  8. Bravo F, Osorio LF, Pando V, Del Peso C (2010) Long-term implications of traditional forest regulation methods applied to Maritime pine (Pinus pinaster Ait.) forests in central Spain: a century of management plans. iForest 3, 33–38. Available online at: Accessed 10 Nov 2010
  9. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees, vol 358. Chapman and Hall/CRC, Boca RatonGoogle Scholar
  10. Brown S (2002) Measuring carbon in forests: current status and future challenges. Environ Pollut 116:363–372PubMedCrossRefGoogle Scholar
  11. Calama R, Montero G (2007) Cone and seed production from stone pine (Pinus pinea L.) stands in central range (Spain). Eur J For Res 126:23–35CrossRefGoogle Scholar
  12. Campbell JL, Kennedy RE, Cohen WB, Miller RF (2012) Assessing the carbon consequences of western juniper (Juniperus occidentalis) encroachment across Oregon, USA. Rangel Ecol Manag 5:223–231CrossRefGoogle Scholar
  13. Canty MJ, Nielsen AA, Schmidt M (2004) Automatic radiometric normalization of multitemporal satellite imagery. Remote Sens Environ 91:441–451CrossRefGoogle Scholar
  14. Chander G, Markham BL, Helder DH (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903CrossRefGoogle Scholar
  15. Chávez PS (1988) An improved dark object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24:459–479CrossRefGoogle Scholar
  16. Cohen WB, Spies T, Fiorella M (1995) Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A. Int J Remote Sens 16(4):721–746CrossRefGoogle Scholar
  17. Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data, principles and practices, Second edn. CRC Press, Boca Raton, 177 ppGoogle Scholar
  18. Coppin P, Jonckheere I, Nackaerts K, Muys B (2004) Digital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25:1565–1596CrossRefGoogle Scholar
  19. Crist EP, Cicone RC (1984) A physically based transformation of Thematic Mapper data- the TM tasseled cap. IEEE Trans Geosci Remote Sens GE-22:256–263CrossRefGoogle Scholar
  20. Crist EP (1985) A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens Environ 17:301–306CrossRefGoogle Scholar
  21. Daubechies I, Guskov I, Schröder P, Sweldens W (1999) Wavelets on irregular point sets. Philos Trans R Soc A Math Phys Eng Sci 357(1760):2397–2413CrossRefGoogle Scholar
  22. Duane MV, Cohen WB, Campbell JL, Hudiburg T, Turner DP, Weyermann DL (2010) Implications of alternative field-sampling designs on Landsat-based mapping of stand age and carbon stocks in Oregon forests. For Sci 56:405–416Google Scholar
  23. Duncanson LI, Neimann KO, Wulder MA (2010) Integration of GLAS and Landsat TM data for aboveground biomass estimation. Can J Remote Sens 36(2):129–141CrossRefGoogle Scholar
  24. Englhart S, Keuck V, Siegert F (2011) Aboveground biomass retrieval in tropical forests—the potential of combined X- and L-band SAR data use. Remote Sens Environ 115:1260–1271CrossRefGoogle Scholar
  25. FAO (2010) Global forest resources assessment. Rome, Italy. Available at Accessed 8 Aug, 2013
  26. FAO (2013) State of the Mediterranean forests 2013. Rome, Italy. Available at: Accessed 20 July, 2015
  27. Frazier RJ, Coops NC, Wulder MA, Kennedy R (2014) Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics. ISPRS J Photogramm Remote Sens 92:137–142CrossRefGoogle Scholar
  28. Fuller RM, Smith GM, Devereux BJ (2003) The characterization and measurement of land cover change through remote sensing: problems in operational applications? Int J Appl Earth Obs Geoinf 4:243–253CrossRefGoogle Scholar
  29. Gemmell F (1995) Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a rocky mountain site. Remote Sens Environ 51:291–305CrossRefGoogle Scholar
  30. Gillanders SN, Coops NC, Wulder MA, Gergel S, Nelson T (2008a) Multitemporal remote sensing of landscape dynamics and pattern change: describing natural and anthropogenic trends. Prog Phys Geogr 32:503–528CrossRefGoogle Scholar
  31. Gillanders SN, Coops NC, Wulder MA, Goodwin NR (2008b) Application of Landsat satellite imagery to monitor land-cover changes at the Athabasca oil sands, Alberta, Canada. Can Geogr 52:466–485CrossRefGoogle Scholar
  32. Giorgino T (2009) Computing and visualizing dynamic time warping alignments in R: the dtw package. J Stat Softw 31(7):1–24CrossRefGoogle Scholar
  33. Goetz SJ, Fiske GJ, Bunn AG (2006) Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada. Remote Sens Environ 101:352–365CrossRefGoogle Scholar
  34. Goetz ST, Baccini A, Laporte NT, Johns T, Walker W, Kellndorfer J, Houghton RA, Sun M (2009) Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance Manag, 4(2),
  35. Gómez C (2006) Estimación de volumen de P. sylvestris L. mediante imágenes Landsat y QuickBird en el Sistema Central español. DEA dissertation. Universidad de Valladolid, Spain, 32 ppGoogle Scholar
  36. Gómez C, White JC, Wulder MA (2011) Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation. Remote Sens Environ 115:1665–1679CrossRefGoogle Scholar
  37. Gómez C, Wulder MA, White JC, Montes F, Delgado JA (2012) Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain. Int J Remote Sens 33(17):5546–5573CrossRefGoogle Scholar
  38. Gómez C, White JC, Wulder MA, Alejandro P (2014) Historical forest biomass dynamics modeled with Landsat spectral trajectories. IPRS J Photogrammetry and Remote Sensing 93:14–28CrossRefGoogle Scholar
  39. Gong P, Xu B (2003) Chapter 11: Remote sensing of forests over time: change types, methods, and opportunities. In: Wulder MA, Franklin SE (eds) Remote sensing of forest environments: concepts and case studies. Kluwer Academic Publishers, Dordrecht/Boston/LondonGoogle Scholar
  40. Goodwin NR, Coops NC, Wulder MA, Gillanders S, Schroeder TA, Nelson T (2008) Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sens Environ 112:3680–3689CrossRefGoogle Scholar
  41. Goodwin NR, Magnussen S, Coops NC, Wulder MA (2010) Curve fitting of time series Landsat imagery for characterising a mountain pine beetle infestation disturbance. Int J Remote Sens 31(12):3263–3271CrossRefGoogle Scholar
  42. Goward SN, Masek JG, Cohen WB, Moisen G, Collatz GJ, Healey S, Houghton RA, Huang C, Kennedy R, Law B, Powell S, Turner D, Wulder MA (2008) Forest disturbance and North American carbon flux. Earth Obs Sys 89:105–108Google Scholar
  43. Hall RJ, Skakun RS, Arsenault EJ, Case BS (2006) Modeling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. For Ecol Manage 225:378–390CrossRefGoogle Scholar
  44. Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74CrossRefGoogle Scholar
  45. Hansen MC, Egorov A, Potapov PV, Stehman SV, Tyukavina A, Turubanova SA, Roy DP, Goetz SJ, Loveland TR, Ju J, Kommareddy A, Kovalskyy V, Forsyth C, Bents T (2014) Monitoring conterminous Unite States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sens Environ 140:466–484CrossRefGoogle Scholar
  46. Hayes DJ, Cohen WB (2007) Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data. Remote Sens Environ 106:1–16CrossRefGoogle Scholar
  47. Healey SP, Cohen WB, Zhiqiang Y, Krankina ON (2005) Comparison of tasseled cap-based landsat data structures for use in forest disturbance detection. Remote Sens Environ 97:301–310CrossRefGoogle Scholar
  48. Healey SP, Yang Z, Cohen WB, Pierce DJ (2006) Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data. Remote Sens Environ 101:115–126CrossRefGoogle Scholar
  49. Herrero C, Bravo F (2012) Can we get an operational indicator of forest carbon sequestration? A case study from two forest regions in Spain. Ecol Indic 17:120–126CrossRefGoogle Scholar
  50. Homer C, Huan C, Yang L, Wylie B, Coan M (2004) Development of a 2001 national land-cover database for the United States. Photogramm Eng Remote Sens 70:829–884CrossRefGoogle Scholar
  51. Houghton RA (2005) Aboveground forest biomass and the global carbon balance. Glob Chang Biol 11:945–958CrossRefGoogle Scholar
  52. Houghton RA (2007) Balancing the global carbon budget. Annu Rev Earth Planet Sci 35. doi: 10.1146/
  53. Huang C, Wylie B, Yang L, Homer C, Zylstra G (2002) Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance. Int J Remote Sens 23:1741–1748CrossRefGoogle Scholar
  54. Huang C, Goward SN, Schleeweis K, Thomas N, Masek JG, Zhu Z (2009) Dynamics of national forests assessed using Landsat record: case studies in eastern United States. Remote Sens Environ 113:1430–1442CrossRefGoogle Scholar
  55. Jensen JR (2005) Introductory digital image processing. A remote sensing perspective, 3rd edn. Upper Saddle River, NJ, Prentice HallGoogle Scholar
  56. Jin S, Sader SA (2005) Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances. Remote Sens Environ 94:364–372CrossRefGoogle Scholar
  57. Kangas A, Maltamo M (2006) Managing forest ecosystems: forest inventory: methodology and applications. Springer, DordrechtGoogle Scholar
  58. Kauth RJ, Thomas GS (1976) The tasseled cap – a graphic description of the spectral-temporal development of agricultural crops as seen in Landsat. In: Proceedings on the symposium on machine processing of remotely sensed data, West Lafayette, Indiana, LARS, Purdue University, West Lafayette, Indiana, 41–51 June 29–July 1, 1976Google Scholar
  59. Kennedy R, Yang Z, Cohen WB (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — temporal segmentation algorithms. Remote Sens Environ 114:2897–2910CrossRefGoogle Scholar
  60. Kennedy RE, Andréfouët S, Gómez C, Griffiths P, Hais M, Healey S, Helmer EH, Hostert P, Lyons M, Meigs GW, Pflugmacher D, Phinn S, Powell S, Scarth PF, Sen S, Schroeder TA, Schneider AM, Sonnenschein R, Vogelmann JE, Wulder MA, Zhu Z (2014) Bringing an ecological view of change to Landsat-based remote sensing. Front Ecol Environ 12(6):339–346CrossRefGoogle Scholar
  61. Kollmann F (1959) Tecnología de la madera y sus aplicaciones. Translation of second edition. In: German of ‘Tecnologie des Holzes und der Holzwerkstoffe: mit 1194 Abbildungen im Text und 6 Tafeln’. Springer, BerlínGoogle Scholar
  62. Kwak DA, Lee WK, Cho HK, Lee SH, Son Y, Kafatos M, Kim SR (2010) Estimating stem volume and biomass of Pinus koraiensis using LiDAR data. J Plant Res 123:421–432PubMedCrossRefGoogle Scholar
  63. Law BE, Ryan MG, Anthoni PM (1999) Seasonal and annual respiration in a ponderosa pine ecosystem. Glob Chang Biol 5:169–182CrossRefGoogle Scholar
  64. LeQuéré C, Raupach MR, Canadell JG, Marland G et al (2009) Trends in the sources and carbon sinks of carbon dioxide. Nat Geosci 2:831–836CrossRefGoogle Scholar
  65. Le Quéré C, Moriarty R, Andrew RM, Peters GP et al (2015) Global carbon budget 2014. Earth Sys Sci Data 7:47–85CrossRefGoogle Scholar
  66. Liu W, Song C, Schroeder TA, Cohen WB (2008) Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data. Int J Remote Sens 29(13):3855–3872CrossRefGoogle Scholar
  67. Lu D (2005) Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. Int J Remote Sens 26(12):2509–2525CrossRefGoogle Scholar
  68. Lu D (2006) The potential and challenge of remote sensing-based biomass estimation. Int J Remote Sens 27:1297–1328CrossRefGoogle Scholar
  69. Lu D, Batistella M, Moran E (2005) Satellite estimation of aboveground biomass and impacts of forest stand structure. Photogramm Eng Remote Sens 71(8):967–974CrossRefGoogle Scholar
  70. Lu D, Chen Q, Wang G, Moran E, Batistella M, Zhang M, Laurin GV, Saah D (2012) Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates. Int J For Res, 2012, Article ID 436537, 16 pages, doi:10.1155/2012/436537Google Scholar
  71. Lu D, Mause P, Brondizios E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2407CrossRefGoogle Scholar
  72. Lunetta R, Johnson DM, Lyon J, Crotwell J (2004) Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sens Environ 89:444–454CrossRefGoogle Scholar
  73. Main-Korn M, Cohen WB, Kennedy RE, Grodzki W, Pflugmacher D, Griffiths P, Hostert P (2013) Monitoring coniferous forest biomass change using a Landsat trajectory approach. Remote Sens Environ 139:277–290CrossRefGoogle Scholar
  74. Mallat S, Hwang WL (1992) Singularity detection and processing with wavelets. IEEE Trans Inf Theory 38(2):617–643CrossRefGoogle Scholar
  75. Masera OR, Garza-Caligaris JF, Kanninen M, Karjalainen T, Liski J, Nabuurs GJ, Pussinen A, De Jong BHJ, Mohren GMJ (2003) Modeling carbon sequestration in afforestation, agroforestry and forest management projects: the CO2FIX V. 2 approach. Ecol Model 164:177–199CrossRefGoogle Scholar
  76. Merlo M, Croitoru L (2005) Valuing Mediterranean forests – towards total economic value. CABI Publishing, Wallingford, 397 ppCrossRefGoogle Scholar
  77. Mitchard ETA, Saatchi SS, Woodhouse IH, Nangendo G, Ribeiro NS, Williams M, Ryan M, 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, L23401, doi:10.1029/2009GL040692Google Scholar
  78. MMA 2008 Historia del Inventario Forestal Nacional de España. Available online at: Accessed 15 Nov 2010
  79. Montero G, Muñoz M, Donés J, Rojo A (2004) Fijación de CO2 por Pinus sylvestris L. y Quercus pyrenaica Willd. en los montes “Pinar de Valsaín” y “Matas de Valsaín”. Sistemas y Recursos Forestales 13(2):399–415Google Scholar
  80. Montero G, Ruiz-Peinado R, Muñoz M (2005) Producción de biomasa y fijación de CO2 por parte de los bosques españoles. Monografías INIA: Serie Forestal n° 13, Madrid, 270 ppGoogle Scholar
  81. Myers N, Mittelmeier RA, Mittelmeier CG, Da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858PubMedCrossRefGoogle Scholar
  82. 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 Natl Acad Sci 98:14784–14789PubMedPubMedCentralCrossRefGoogle Scholar
  83. Næsset E, Gobakken T (2008) Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ 112:3079–3090CrossRefGoogle Scholar
  84. Odum EP (1969) The strategy of ecosystem development. Science 164:262–270PubMedCrossRefGoogle Scholar
  85. Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sens Environ 148:42–57CrossRefGoogle Scholar
  86. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais P, Jackson RB, Pacala S, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D (2011) A large and persistent carbon sink in the World’s forests. Science 333:988–993PubMedCrossRefGoogle Scholar
  87. Penman J, Gytarsky M, Hiraishi T, Krug T, Kruger D, Pipatti R, Buendia L, Miwa K, Ngara T, Tanabe K, Wagner F (2003) Good practice guidance for land use, land-use change and forestry. Intergovernmental Panel on Climate Change (IPCC), HayamaGoogle Scholar
  88. Peterson U, Nilson T (1993) Successional reflectance trajectories in northern temperate forests. Int J Remote Sens 14:609–613CrossRefGoogle Scholar
  89. Pflugmacher D, Cohen WB, Kennedy RE (2012) Using Landsat-derived disturbance history (1972–2010) to predict current forest structure. Remote Sens Environ 122:146–165CrossRefGoogle Scholar
  90. Potapov P, Turubanova S, Hansen MC (2011) Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sens Environ 115:548–561CrossRefGoogle Scholar
  91. Powell, S.L., Cohen, W.B., Healey, S.P., Kennedy, R.E., Moisen, G.G, Pierce, K.B., & Ohmann, J.L. (2010). Quantification of live aboveground biomass dynamics with Landsat time-series and field inventory data: a comparison of empirical modeling approaches. Remote Sens Environ, 114, 1053–1068.Google Scholar
  92. Price KP, Jakubauskas ME (1998) Spectral retrogression and insect damage in lodgepole pine successional forests. Int J Remote Sens 19:1627–1632CrossRefGoogle Scholar
  93. Rivas-Martínez S (1963) Estudio de la vegetación y flora de la Sierra de Guadarrama y Gredos. Anales del Instituto Botánico AJ Cavanilles 21:5–325Google Scholar
  94. Rouse JW Jr, Haas RH, Schell JA, Deering DW (1973) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation, Prog. Rep. RSC 1978–1, Remote Sensing Center, Texas A&M Univ., College Station, nr. E73-106393, 93. (NTIS No. E73-106393)Google Scholar
  95. Roy DP, Ju J, Mbow C, Frost P, Loveland T (2010) Accessing free Landsat data via the internet: Africa’s challenge. Remote Sens Lett 1(2):111–117CrossRefGoogle Scholar
  96. Ruiz-Peinado R, Río M, Montero G (2011) New models for estimating the carbon sink capacity of Spanish softwood species. For Sys 20(1):176–188Google Scholar
  97. Salvador R, Pons X (1998) On the applicability of Landsat TM images to Mediterranean forest inventories. For Ecol Manag 104:193–208CrossRefGoogle Scholar
  98. Schroeder TA, Cohen WB, Song C, Canty MJ, Yang Z (2006) Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens Environ 103:16–26CrossRefGoogle Scholar
  99. Schroeder TA, Cohen WB, Yang Z (2007) Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series. For Ecol Manag 243:259–273CrossRefGoogle Scholar
  100. Schulze ED, Wirth C, Heimann M (2000) Climate change: managing forests after Kyoto. Science 22:2058–2059. doi: 10.1126/science.289.5487.2058 CrossRefGoogle Scholar
  101. Senf C, Leitao PJ, Pflugmacher D, van der Linden S, Hostert P (2015) Mapping land cover in complex Mediterranean landscapes using Landsat: improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote Sens Environ 2015(156):527–536CrossRefGoogle Scholar
  102. Serrada R (2008) Apuntes de selvicultura. Servicio de publicaciones. EUIT Forestal, MadridGoogle Scholar
  103. Smeets EMW, Faaij APC (2007) Bioenergy potentials from forestry in 2050. Clim Chang 81(3):353–390CrossRefGoogle Scholar
  104. Song C, Woodcock CE, Seto KC, Lenney MP, Macomber SA (2001) Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote Sens Environ 75:230–244CrossRefGoogle Scholar
  105. Sonnenschein R, Kuemmerle T, Udelhoven T, Stellness M, Hostert P (2011) Differences in Landsat-based trend analyses in drylands due to the choice of vegetation estimate. Remote Sens Environ 115:1408–1420CrossRefGoogle Scholar
  106. Sun G, Ranson KJ, Guo Z, Zhang Z, Montesano P, Kimes D (2011) Forest biomass mapping from lidar and radar synergies. Remote Sens Environ 115:2906–2916CrossRefGoogle Scholar
  107. Tan K, Piao S, Peng C, Fang J (2007) Satellite-based estimation of biomass carbon stocks for northeast China’s forests between 1982 and 1999. For Ecol Manag 240:114–121CrossRefGoogle Scholar
  108. Tolomeo R, Lawson T, Lokey G, Dunn C, Stein C, Overton J (2009) The Landsat program is not meeting the goals and intent of the land remote sensing policy act of 1992, Audit report. Report n. IG-09–021 (assignment n. A–08–019–00). NASAGoogle Scholar
  109. Turner DP, Cohen WB, Kennedy RE, Fassnacht KS, Briggs JM (1999) Relationship between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens Environ 70:52–68CrossRefGoogle Scholar
  110. Vázquez de la Cueva A (2008) Structural attributes of three forest types in central Spain and Landsat ETM+ information evaluated with redundancy analysis. Int J Remote Sens 29(19):5657–5676CrossRefGoogle Scholar
  111. Velichko VM, Zagoruyko NG (1970) Automatic recognition of 200 words. Int J Man-Mach Stud 2:223–234CrossRefGoogle Scholar
  112. Vicente-Serrano SM, Perez-Cabello F, Lasanta T (2008) Assessment of radiometric correction techniques in analyzing vegetation variability and change using time series of Landsat images. Remote Sens Environ 112:3916–3934CrossRefGoogle Scholar
  113. Villa G, Arozarena A, Peces JJ, Domenech E (2009) Plan nacional de teledetección: estado actual y perspectivas futuras. Teledetección: agua y desarrollo sostenible. XIII Congreso de la Asociación Española de Teledetección. Calatayud, 23–26 Septiembre, pp. 521–524Google Scholar
  114. Villaescusa R, Vallejo R, De La Cita J (2001) Actualización del Mapa Forestal de España. III Congreso Nacional Forestal. Granada, Junta de Andalucía, pp 153–158Google Scholar
  115. Vogelmann JE, Tolk B, Zhu Z (2009) Monitoring forest changes in the southwestern United States using multitemporal landsat data. Remote Sens Environ 113:1739–1748CrossRefGoogle Scholar
  116. Wilson EH, Sader SA (2002) Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sens Environ 80:385–396CrossRefGoogle Scholar
  117. Woodcock CE, Allen R, Anderson M, Belward A, Bindschadler R, Cohen WB, Gao F, Goward SN, Helder D, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail PS, Vermote EF, Vogelmann J, Wulder MA, Wynne R (2008) Free access to Landsat imagery. Science 320:1011PubMedCrossRefGoogle Scholar
  118. Wulder MA, White JC, Fournier RA, Luther JE, Magnussen S (2008a) Spatially explicit large area biomass estimation: three approaches using forest inventory and remotely sensed imagery and GIS. Sensors 8:529–560PubMedPubMedCentralCrossRefGoogle Scholar
  119. Wulder MA, Ortlepp SM, White JC, Coops NC (2008b) Impacts of sun-surface-sensor geometry upon multitemporal high spatial resolution satellite imagery. Can J Remote Sens 34:455–461CrossRefGoogle Scholar
  120. Wulder MA, White JC, Goward SN, Masek JG, Irons JR, Herold M, Cohen WB, Loveland TR, Woodcock CE (2008c) Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens Environ 112:955–969CrossRefGoogle Scholar
  121. Wulder MA, White JC, Masek JG, Dwyer J, Roy DP (2011) Continuity of Landsat observations: short term considerations. Remote Sens Environ 115:747–751CrossRefGoogle Scholar
  122. Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10CrossRefGoogle Scholar
  123. Wulder MA, Hilker T, White JC, Coops NC, Masek JG, Pflugmacher D, Crevier Y (2015) Virtual constellations for global terrestrial monitoring. Remote Sens Environ 170:62–76CrossRefGoogle Scholar
  124. Yu Y, Saatchi S, Heath LS, LaPoint E, Myneni R, Knyazikhin Y (2010) Regional distribution of forest height and biomass from multisensor data fusion. J Geophys Res 115,, G00E12
  125. Zhu Z, Woodcock CE, Olofsson P (2012) Continuous monitoring of forest disturbance using all available Landsat images. Remote Sens Environ 122:75–91CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Cristina Gómez
    • 1
    • 2
    Email author
  • Joanne C. White
    • 3
  • Michael A. Wulder
    • 3
  1. 1.Sustainable Forest Management Research InstituteUniversidad de Valladolid & INIAValladolidSpain
  2. 2.Department of Geography and Environment, School of GeoscienceUniversity of AberdeenAberdeenUK
  3. 3.Canadian Forest Service (Pacific Forestry Centre), Natural Resources CanadaVictoriaCanada

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