European Commission, “Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: A new EU Forest Strategy: for forests and the forest-based sectors.” online, 2013, last access: 27 Jan 2017.
IPCC, “Good Practice Guidance for Land Use, Land-Use Change and Forestry (GPG-LULUCF).” http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_contents.html
, 2003, last accessed 27 Jan 2016.
FAO, “Forest Resources Assessment Working Paper 177: Assessing forest degradation: towards the development of globally applicable guidelines.” http://www.fao.org/docrep/015/i2479e/i2479e00.pdf
, Rome, 2011, last accessed 27 Jan 2017.
D. Schoene, W. Killmann, H. Lüpke, and M. LoycheWilkie, “Forest and Climate Change Working Paper 5: definitional issues related to reducing emissions from deforestation in developing countries.” ftp://ftp.fao.org/docrep/fao/009/j9345e/j9345e00.pdf
, 2007, last accessed: 27 Jan 2017.
A. Singh, “Review article digital change detection techniques using remotely-sensed data,” Int J Remote Sens, vol. 10, pp. 989–1003, jun 1989.
P. R. Coppin and M. E. Bauer, “Change detection in forest ecosystems with remote sensing digital imagery,” Remote Sensing Reviews, no. 13, pp. 207–234, 1996.
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG. “High-resolution global maps of 21st-century forest cover change.” Science. 2013;342(6160):850–3.CrossRefGoogle Scholar
Potapov P, Turubanova S, Hansen M, Adusei B, Broich M, Altstatt A, Mane L, Justice C. “Quantifying Forest cover loss in Democratic Republic of Congo, 2000-2010, with Landsat ETM+ data.” Remote Sensing of Envionment. 2012;122:106–16.CrossRefGoogle Scholar
B. Gardiner, K. Blennow, J.-M. Carnus, M. Fleischer, F. Ingemarson, G. Landmann, M. Lindner, M. Marzano, B. Nicoll, C. Orazio, J.-L. Peyron, M.-P. Reviron, M.-J. Schelhaas, A. Schuck, M. Spielmann, and T. Usbeck, “Destructive storms in European forests: past and forthcoming impacts.” http://ec.europa.eu/environment/forests/pdf/STORMS%20Final_Report.pdf
, 2010, last accessed 27 Jan 2017.
Koutsias N, Xanthopoulos G, Founda D, Xystrakis F, Nioti F, Pleniou M, Mallinis G, Arianoutsou M. “On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894-2010).” Int J Wildland Fire. 2013;22(4):493–507.CrossRefGoogle Scholar
M. Turco, J. Bedia, F. Di Liberto, P. Fiorucci, J. von Hardenberg, N. Koutsias, M.-C. Llasat, F. Xystrakis, and A. Provenzale, “Decreasing fires in Mediterranean Europe,” PLoS One, vol. 11, p. e0150663, March 2016.
• R. Seidl, M.-J. Schelhaas, W. Rammer, and P. J. Verkerk, “Increasing forest disturbances in Europe and their impact on carbon storage,” Nat Clim Chang, vol. 4, pp. 806–810, August 2014. On the basis of an ensemble of climate change scenarios, the authors find that damage from wind, bark beetles, and forest fires in Europe is likely to increase further in coming decades, and they estimate the rate of increase to be +0.91 × 10
of timber per year until 2030.
European Commission, “Green paper: on forest protection and information in the EU: Preparing forests for climate change.” http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2010:0066:FIN:EN:PDF
, 2010. Last accessed: Oct. 5th 2016.
“Full State of Europe’s Forests 2015.” http://foresteurope.org/state-europes-forests-2015-report/, 2015, last accessed 27 Jan 2017.
L. L. Susan Minnemeyer, N. Sizer, C. Saint-Laurent, and P. Potapov, “A world of opportunity.” http://www.wri.org/sites/default/files/world_of_opportunity_brochure_2011-09.pdf
, 2011, last accessed 27 Jan 2017.
D.-H. Kim, J. O. Sexton, and J. R. Townshend, “Accelerated deforestation in the humid tropics from the 1990s to the 2000s,” Geophys Res Lett, vol. 42, pp. 3495–3501, may 2015.
DeFries R, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B, de Souza C. “Earth observations for estimating greenhouse gas emissions from deforestation in developing countries.” Environ Sci Pol. 2007;10(4):385–94.CrossRefGoogle Scholar
N. L. Harris, S. Brown, S. C. Hagen, S. S. Saatchi, S. Petrova, W. Salas, M. C. Hansen, P. V. Potapov, and A. Lotsch, “Baseline map of carbon emissions from deforestation in tropical regions,” Science, vol. 336, pp. 1573–1576, June 2012.
R. A. Houghton, J. I. House, J. Pongratz, G. R. van der Werf, R. S. DeFries, M. C. Hansen, C. L. Quéré, and N. Ramankutty, “Carbon emissions from land use and land-cover change,” Biogeosciences, vol. 9, pp. 5125–5142, dec 2012.
J. Grace, E. Mitchard, and E. Gloor, “Perturbations in the carbon budget of the tropics,” Glob Chang Biol, vol. 20, pp. 3238–3255, June 2014.
J.-P. Lanly, “Deforestation and forest degradation factors,” in Proceedings of XII World Forestry Congress, 2003.
• C. Kuenzer, S. Dech, and W. Wagner, eds., Remote sensing time series: revealing land surface dynamics. Springer International Publishing, 2015. Comprehensive review of different time series approaches for different datasets and applications from around the globe.
Wulder M, Hilker T, White J, Coops N, Masek J, Pflugmacher D, Crevier Y. “Virtual constellations for global terrestrial monitoring.” Remote Sens Environ. 2015;170:62–76.CrossRefGoogle Scholar
P. Hostert, P. Griffiths, S. van der Linden, and D. Pflugmacher, Remote Sensing Time Series - Revealing Land Surface Dynamics, ch. 2: “Time series analyses in a new era of optical satellite data,” pp. 25–41. Springer International Publishing, 2015.
Margono BA, Turubanova S, Zhuravleva I, Potapov P, Tyukavina A, Baccini A, Goetz S, Hansen MC. “Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010.” Environ Res Lett. 2012;7:1–16.CrossRefGoogle Scholar
N. H. Ravindranath, N. Srivastava, I. K. Murthy, S. Malaviya, M. Munsi, and N. Sharma, “Deforestation and forest degradation in India—implications for REDD+,” Curr Sci, vol. 102, pp. 1117–1125, April 2012.
Matricardi EA, Skole DL, Pedlowski MA, Chomentowski W, Fernandes LC. “Assessment of tropical forest degradation by selective logging and fire using Landsat imagery.” Remote Sens Environ. 2010;114:1117–29.CrossRefGoogle Scholar
Huang C, Goward S, Masek J, Thomas N, Zhu Z, Vogelmann J. “An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks.” Remote Sensing of Envionment. 2010;114:183–98.CrossRefGoogle Scholar
Kayastha N, Thomas V, Galbraith J, Banskota A. “Monitoring wetland change using inter-annual Landsat time-series data.” Wetlands. 2012;32:1149–62.CrossRefGoogle Scholar
W. Hargrove, J. Spruce, G. Gasser, L. Martin, and S. Norman, “Monitoring regional forest disturbances across the US with near real time MODIS NDVI products included in the ForWarn forest threat early warning system.” Presented at the 2013 AGU Fall Meeting, 2013.
Vogelmann JE, Xian G, Homer C, Tolk B. “Monitoring gradual ecosystem change using Landsat time series analyses: case studies in selected forest and rangeland ecosystems.” Remote Sens Environ. 2012;122:92–105.CrossRefGoogle Scholar
Lehmann E, Wallace J, Caccetta P, Furby S, Zdunic K. “Forest cover trends from time series Landsat data for the Australian continent.” Int J Appl Earth Obs Geoinf. 2013;21:453–62.CrossRefGoogle Scholar
Bayr C, Gallaun H, Kleb U, Kornberger B, Steinegger M, Winter M. “Satellite based forest monitoring: spatial and temporal forecast of growing index and short wave infrared band.” Geospat Health. 2016;11(1):31–42.CrossRefGoogle Scholar
Kennedy RE, Cohen WB, Schroeder TA. “Trajectory-based change detection for automated characterization of forest disturbance dynamics.” Remote Sens Environ. 2007;110:370–86.CrossRefGoogle Scholar
•• P. Griffiths and P. Hostert, Remote sensing time series: revealing land surface dynamics, ch. 15: “Forest cover dynamics during massive ownership changes—annual disturbance mapping using annual landsat time-series,” pp. 307–322. Springer International Publishing, 2015. The authors showed that time series approaches can provide good results for gradual changes such as recovery or degradation in Europe, even if only annual data is available.
Kennedy RE, Yang Z, Cohen WB. “Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—temporal segmentation algorithms.” Remote Sens Environ. 2010;114(12):2897–910.CrossRefGoogle Scholar
Cohen WB, Yang Z, Kennedy R. “Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync—tools for calibration and validation.” Remote Sens Environ. 2010;114(12):2911–24.CrossRefGoogle Scholar
N. Koutsias, M. Pleniou, G. Mallinis, F. Nioti, and N. I. Sifakis, “A rule-based semi-automatic method to map burned areas: exploring the USGS historical Landsat archives to reconstruct recent fire history,” Int J Remote Sens, vol. 34, pp. 7049–7068, oct 2013.
Eklundh L, Johansson T, Solberg S. “Mapping insect defoliation in scots pine with MODIS time-series data.” Remote Sens Environ. 2009;113:1566–73.CrossRefGoogle Scholar
Verbesselt J, Hyndman R, Newnham G, Culvenor D. “Detecting trend and seasonal changes in satellite image time series.” Remote Sens Environ. 2010;114(1):106–15.CrossRefGoogle Scholar
• D. Pflugmacher, W. B. Cohen, and R. E. Kennedy, “Using landsat-derived disturbance history (1972-2010) to predict current forest structure,” Remote Sens Environ, vol. 122, pp. 146–165, jul 2012. This study demonstrates the unique value of the long, historic Landsat record and suggests new potentials for mapping current forest structure with time series data.
Asner GP, Keller M, Pereira R, Zweede JC. “Remote sensing of selective logging in Amazonia assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis.” Remote Sens Environ. 2002;80(3):483–96.CrossRefGoogle Scholar
Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller M, Silva JN. “Selective logging in the Brazilian Amazon.” American Association of the Advancement of Science. 2005; 310: 480–482.CrossRefGoogle Scholar
•• Hansen M, Krylov A, Tyukavina A, Potapov P, Turubanova S, Zutta B, Ifo S, Margono B, Stolle F, Moore R. Humid tropical forest disturbance alerts using Landsat data. Environ Res Lett. 2016: 11(3): 034008. This paper shows the first results of an operational forest disturbance alert system using Landsat data in three tropical countries. The results show very high user's accuracies and moderately high producer's accuracies and are freely available on the internet
Koltunov A, Ustin S, Asner GP, Fung I. “Selective logging changes forest phenology in the Brazilian Amazon: evidence from MODIS image time series analysis.” Remote Sens Environ. 2009;113:2431–40.CrossRefGoogle Scholar
• Hirschmugl M, Steinegger M, Gallaun H, Schardt M. Mapping forest degradation due to selective logging by means of time series analysis: case studies in Central Africa. Remote Sens. 2014; 6(1):756–75. Selective logging is a major driver of forest degradation in Central Africa but often goes undetected due to the fast regrowth in tropical areas. This paper presents a method to detect the affected areas in a 10-year Landsat time series
Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E. “Digital change detection methods in ecosystem monitoring: a review.” Int J Remote Sens. 2004;25(9):1565–96.CrossRefGoogle Scholar
Walter V. “Object-based classification of remote sensing data for change detection.” ISPRS J Photogramm Remote Sens. 2004;58(3–4):225–38.CrossRefGoogle Scholar
•Sedano F, Kempeneers P, Miguel JS, Strobl P, Vogt P. “Towards a pan-European burnt scar mapping methodology based on single date medium resolution optical remote sensing data.” Int J Appl Earth Obs Geoinf. 2013;20:52–9. The authors present a two-stage approach for operational burnt scar mapping with medium resolution remote sensing data in Mediterranean Europe with an increased capability for detection of smaller burnt scars
S. Violini, “Deforestation: change detection in forest cover using remote sensing,” in Seminary Master in Emergency Early Warning and Response Space Applications (Mario Gulich Institute, CONAE. Argentina), pp. 1–28, 2013.
• Banskota A, Kayastha N, Falkowski M, Wulder M, Froese R, White J. Forest monitoring using Landsat time series data: a review. Can J Remote Sens. 2014; 40(5):362–84. Comprehensive review of time series approaches using Landsat data including preprocessing steps and verification methods
C. Kuenzer, S. Dech, and W. Wagner, Remote Sensing Time Series: Revealing Land Surface Dynamics, ch. 1: “Remote sensing time series revealing land surface dynamics: status quo and the pathway ahead,” pp. 1–24. Springer International Publishing, 2015.
L. Eklundh and P. Jönsson, Remote Sensing Time Series: Revealing Land Surface Dynamics, ch. 7: “TIMESAT: a software package for time-series processing and assessment of vegetation dynamics,” pp. 141–158. Springer International Publishing, 2015.
K. Gutjahr, R. Perko, H. Raggam, and M. Schardt, “The Epipolarity constraint in stereo-Radargrammetric DEM generation,” Geoscience and Remote Sensing, IEEE Transactions on, vol. Volume: 52, Issue: 8, pp. 5014–5022, Aug 2014.
Chen W, Chen W, Li J. “Comparison of surface reflectance derived by relative radiometric normalization versus atmospheric correction for generating large-scale landsat mosaics.” Remote Sensing Letters. 2010;1(2):103–9.CrossRefGoogle Scholar
Schlaepfer D, Borel CC, Keller J, Itten KI. “Atmospheric precorrected differential absorption technique to retrieve columnar water vapour.” Remote Sens Environ. 1998;65:353–66.CrossRefGoogle Scholar
U. Mueller-Wilm, Sentinel-2 MSI—Level-2A prototype processor installation and user manual, 2016. Last accessed 5 Oct 2016.
Hagolle O, Huc M, Villa Pascual D, Dedieu G. A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of FormoSat-2, LandSat, VENuS and sentinel-2 images. Remote Sens. 2015;7(3):2668.CrossRefGoogle Scholar
C. Chance, T. Hermosilla, N. Coops, M. Wulder, and J. White, “Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites,” vol. 44, pp. 186–194, 2016.
C. Huang, N. Thomas, S. N. Goward, J. G. Masek, Z. Zhu, J. R. G. Townshend, and J. E. Vogelmann, “Automated masking of cloud and cloud shadow for forest change analysis using Landsat images,” Int Journal of Remote Sensing, vol. 31, pp. 5449–5464, October 2010.
Zhu Z, Woodcock C. “Object-based cloud and cloud shadow detection in Landsat imagery.” Remote Sens Environ. 2012;118:83–94.CrossRefGoogle Scholar
Zhu Z, Wang S, Woodcock CE. “Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images.” Remote Sens Environ. 2015;159:269–77.CrossRefGoogle Scholar
Morton DC, Shimabukuro RSDYE, Anderson LO, Espírito-Santo FDB, Hansen M, Carroll M. “Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data.” Earth Interactions. 2005;9(8):1–22.CrossRefGoogle Scholar
C. G. Diniz, A. A. de Almeida Souza, D. C. Santos, M. C. Dias, N. C. da Luz, D. R. V. de Moraes, J. S. Maia, A. R. Gomes, I. da Silva Narvaes, D. M. Valeriano, L. E. P. Maurano, and M. Adami, “DETER-B: the New Amazon near real-time deforestation detection system,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 7, 2015.
Miettinen J, Stibig H-J, Achard F, Langner A, Carboni S. “Remote sensing of forest degradation in Southeast Asia—regional review.” Asian Journal of Geoinformation. 2015;15:23–30.Google Scholar
•• Potapov P, Turubanova S, Tyukavina A, Krylov A, McCarty J, Radeloff V, Hansen M. “Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive.” Remote Sens Environ. 2015; 159:28–43. The authors developed an algorithm to simultaneously process data from different Landsat platforms and sensors (TM and ETM+) to map annual forest cover loss and decadal forest cover gain and applied it on 59,539 Landsat images across Eastern Europe and European Russia with accuracies >75%
•• Zhu Z, Woodcock CE, Olofsson P. “Continuous monitoring of forest disturbance using all available landsat imagery.” Remote Sens Environ. 2012; 122:75–91. The Continuous Monitoring of Forest Disturbance Algorithm (CMFDA) presented in this paper flags forest disturbance by differencing the predicted and observed Landsat images with both producer's and user's accuracies higher than 95% in the spatial domain and temporal accuracy of approximately 94%
de Beurs KM, Henebry GM. “A statistical framework for the analysis of long image time series.” Int J Remote Sens. 2005;26(8):1551–73.CrossRefGoogle Scholar
M. Kuhn, “Building predictive models in R using the caret package,” Journal of Statistical Software, vol. 28, no. 5, 2008.
A. Ghosh, F. E. Fassnacht, P. Joshi, and B. Koch, “A framework for mapping tree species combining hyperspectral and LiDAR data: role of selected classifiers and sensor across three spatial scales,” Int J Appl Earth Obs Geoinf, vol. 26, pp. 49–63, Feb. 2014.