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Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia

Author

Listed:
  • Zerihun Asrat

    (Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

  • Habitamu Taddese

    (Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

  • Hans Ole Ørka

    (Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

  • Terje Gobakken

    (Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

  • Ingunn Burud

    (Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

  • Erik Næsset

    (Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway)

Abstract

Forests, particularly in the tropics, are suffering from deforestation and forest degradations. The estimation of forest area and canopy cover is an essential part of the establishment of a measurement, reporting, and verification (MRV) system that is needed for monitoring carbon stocks and the associated greenhouse gas emissions and removals. Information about forest area and canopy cover might be obtained by visual image interpretation as an alternative to expensive fieldwork. The objectives of this study were to evaluate different types of satellite images for forest area and canopy cover estimation though visual image interpretation, and assess the influence of sample sizes on the estimates. Seven sites in Ethiopia with different vegetation systems were subjectively identified, and visual interpretations were carried out in a systematical design. Bootstrapping was applied to evaluate the effects of sample sizes. The results showed that high-resolution satellite images (≤5 m) (PlanetScope and RapidEye) images produced very similar estimates, while coarser resolution imagery (10 m, Sentinel-2) estimates were dependent on forest conditions. Estimates based on Sentinel-2 images varied significantly from the two other types of images in sites with denser forest cover. The estimates from PlanetScope and RapidEye were less sensitive to changes in sample size.

Suggested Citation

  • Zerihun Asrat & Habitamu Taddese & Hans Ole Ørka & Terje Gobakken & Ingunn Burud & Erik Næsset, 2018. "Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia," Land, MDPI, vol. 7(3), pages 1-17, July.
    • Handle: RePEc:gam:jlands:v:7:y:2018:i:3:p:92-:d:160769
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    References listed on IDEAS

    as
    1. Sisay Nune Hailemariam & Teshome Soromessa & Demel Teketay, 2016. "Land Use and Land Cover Change in the Bale Mountain Eco-Region of Ethiopia during 1985 to 2015," Land, MDPI, vol. 5(4), pages 1-22, November.
    2. Nigel Dudley & Sue Stolton, 2003. "Running Pure : The Importance of Forest Protected Areas to Drinking Water," World Bank Publications - Books, The World Bank Group, number 15006, December.
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