Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review
Purpose of Review
This paper presents a review of the current state of the art in remote sensing-based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview and tabular description of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing mapping approaches: first, classical image-to-image change detection and second, time series analysis.
With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa.
The review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high-resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real-time disturbance mapping in support of operational reactive measures.
KeywordsForest degradation Disturbance mapping Remote sensing Time series Forest monitoring
This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 685761 (EOMonDIS) as well as under grant agreement no. 633464 (DIABOLO). The study in the Republic of Congo was performed within the project GSE-Forest Monitoring REDD Extension Services, financed by ESA. The study in Democratic Republic of Congo was funded by the 7th Framework Programme of the European Commission under grant agreement no. 263075 (ReCover). The study in Cameroon and Central African Republic was funded by the 7th Framework Programme of the European Commission under grant agreement no. 262775 (REDDAf).
Compliance with Ethical Standards
Conflict of Interest
Drs Hirschmugl, Gallaun, Dees, Datta, Deutscher, Koutsias, Schardt declare no conflicts of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 1.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.Google Scholar
- 2.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.
- 3.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.
- 4.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.
- 5.A. Singh, “Review article digital change detection techniques using remotely-sensed data,” Int J Remote Sens, vol. 10, pp. 989–1003, jun 1989.Google Scholar
- 6.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.Google Scholar
- 9.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.
- 11.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.Google Scholar
- 12.• 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 6 m 3 of timber per year until 2030. Google Scholar
- 13.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.
- 14.“Full State of Europe’s Forests 2015.” http://foresteurope.org/state-europes-forests-2015-report/, 2015, last accessed 27 Jan 2017.Google Scholar
- 15.International Sustainability Unit, “Tropical forests—a review.” http://www.pcfisu.org/wp-content/uploads/2015/04/Princes-Charities-International-Sustainability-Unit-Tropical-Forests-A-Review.pdf, 2015, last accessed 27 Jan 2017.
- 16.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.
- 17.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.Google Scholar
- 19.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.Google Scholar
- 20.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.Google Scholar
- 21.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.Google Scholar
- 22.J.-P. Lanly, “Deforestation and forest degradation factors,” in Proceedings of XII World Forestry Congress, 2003.Google Scholar
- 23.• 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. Google Scholar
- 25.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.Google Scholar
- 27.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.Google Scholar
- 31.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.Google Scholar
- 36.•• 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.Google Scholar
- 39.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.Google Scholar
- 42.• 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. Google Scholar
- 43.K. Granica and M. Schardt, “User utility synthesis report.” https://www.eufodos.info/sites/default/files/reports/EF-REP-JR-2012-07-26_D510_1-synth_report_v1.pdf, 2012, last accessed 27 Jan 2017.
- 46.•• 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.CrossRefGoogle Scholar
- 48.• 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.CrossRefGoogle Scholar
- 51.•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.CrossRefGoogle Scholar
- 52.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.Google Scholar
- 53.• 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.CrossRefGoogle Scholar
- 54.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.Google Scholar
- 55.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.Google Scholar
- 56.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.Google Scholar
- 59.U. Mueller-Wilm, Sentinel-2 MSI—Level-2A prototype processor installation and user manual, 2016. Last accessed 5 Oct 2016.Google Scholar
- 61.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.Google Scholar
- 62.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.Google Scholar
- 66.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.Google Scholar
- 67.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
- 68.•• 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%.CrossRefGoogle Scholar
- 69.•• 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%.CrossRefGoogle Scholar
- 71.M. Kuhn, “Building predictive models in R using the caret package,” Journal of Statistical Software, vol. 28, no. 5, 2008.Google Scholar
- 72.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.Google Scholar