Current Forestry Reports

, Volume 3, Issue 1, pp 32–45 | Cite as

Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

  • Manuela HirschmuglEmail author
  • Heinz Gallaun
  • Matthias Dees
  • Pawan Datta
  • Janik Deutscher
  • Nikos Koutsias
  • Mathias Schardt
Remote Sensing (P Bunting, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Remote Sensing


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.

Recent Findings

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.


Forest 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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Manuela Hirschmugl
    • 1
    Email author
  • Heinz Gallaun
    • 1
  • Matthias Dees
    • 2
  • Pawan Datta
    • 2
  • Janik Deutscher
    • 1
  • Nikos Koutsias
    • 3
  • Mathias Schardt
    • 1
  1. 1.Remote Sensing and Geoinformation, JOANNEUM RESEARCHGrazAustria
  2. 2.Remote Sensing and Forest Information SystemsAlbert-Ludwigs-University FreiburgFreiburgGermany
  3. 3.Department of Environmental and Natural Resources ManagementUniversity of PatrasAgrinioGreece

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