Open-access cloud resources contribute to mainstream REDD+: The case of Mozambique

• Catarina Lopes ^[1] ; Ana Leite ^[1] ; Maria José Vasconcelos ^[2]
  1. [1] Remote Sensing, Environment and Technology for Development (RSeT)
  2. [2] Technical University of Lisbon
• Localización: Land use policy: The International Journal Covering All Aspects of Land Use, ISSN 0264-8377, ISSN-e 1873-5754, Nº. 82, 2019, págs. 48-60
• Idioma: inglés
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• Resumen

  • The objective of this work is to investigate how Earth Observation data and processing platforms accessible on the cloud can facilitate the implementation of REDD+ in developing countries. For that, we explore newly available open-access satellite data, cloud processing, and a ready-made land cover map to assess the extent to which such resources can directly respond to monitoring and measuring, reporting, and verification requisites. Mozambique is one of the 47 countries selected to benefit from the Forest Carbon Partnership Facility to implement REDD+ strategies. However, to meet funding agreements, the country needs to periodically produce national land cover and land cover change maps at given resolution and accuracy levels. The work presented here shows that the land cover mapping requisites of REDD+ may be quickly and cost-effectively met through the development and use of newly available cloud resources. The study relies on an experimental design that tests the results of image processing approaches with algorithms available or developed in Google Earth Engine against country-wide reference data collected by a team of national experts. The results show that, in addition to pre-processing advantages, which facilitate multi-temporal compositing and mosaicking of very large and heavy data sets, developments in cloud processing and image classification swiftly produce large extent and high-resolution land cover maps, tailored to a specific objective. The comparison of results between the in-house map obtained using Google Earth Engine, and the pan-African map produced by the European Space Agency (2016) at the same spatial resolution, reveals that both maps meet REDD+ requirements for a binary Forest/Non-forest legend. However, the in-house map is more accurate and reaches considerably better results if a more complex, six class IPCC legend is required. Nevertheless, this study shows that, given adequate reference data, the need to periodically produce high resolution land cover maps for national forest monitoring purposes is no longer an obstacle for mainstreaming the implementation of REDD+.