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Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Author

Listed:
  • Manuela Hirschmugl

    (Joanneum Research, Institute for Information and Communication Technologies, Steyrergasse 17, 8010 Graz, Austria)

  • Carina Sobe

    (Joanneum Research, Institute for Information and Communication Technologies, Steyrergasse 17, 8010 Graz, Austria)

  • Janik Deutscher

    (Joanneum Research, Institute for Information and Communication Technologies, Steyrergasse 17, 8010 Graz, Austria)

  • Mathias Schardt

    (Joanneum Research, Institute for Information and Communication Technologies, Steyrergasse 17, 8010 Graz, Austria)

Abstract

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

Suggested Citation

  • Manuela Hirschmugl & Carina Sobe & Janik Deutscher & Mathias Schardt, 2018. "Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi," Land, MDPI, vol. 7(4), pages 1-17, October.
    • Handle: RePEc:gam:jlands:v:7:y:2018:i:4:p:116-:d:174755
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    References listed on IDEAS

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    1. Ram C. Sharma & Keitarou Hara & Ryutaro Tateishi, 2017. "High-Resolution Vegetation Mapping in Japan by Combining Sentinel-2 and Landsat 8 Based Multi-Temporal Datasets through Machine Learning and Cross-Validation Approach," Land, MDPI, vol. 6(3), pages 1-11, July.
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    Cited by:

    1. Ioannis Manakos & Garik Gutman & Chariton Kalaitzidis, 2021. "Monitoring Land Cover Change: Towards Sustainability," Land, MDPI, vol. 10(12), pages 1-2, December.

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