Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems

Costa, Hugo; Machado, Ines; Moreira, Francisco D.; Benevides, Pedro; Moraes, Daniel; Caetano, Mario

http://hdl.handle.net/10362/127429

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Autores

Costa, Hugo

Machado, Ines

Moreira, Francisco D.

Benevides, Pedro

Moraes, Daniel

Caetano, Mario

Resumo(s)

Southern Portugal is characterized by disperse tree cover of Cork and Holm oaks in an agro-forestry system known as montado. Mapping these trees has been historically very difficult as they occur in isolation or in groups with different understory vegetation, including grass and shrubland. Automatic classification for binary tree/non-tree map production has been used elsewhere, but with limited success in the context of montado. Here, the potential of Sentinel-2 data was explored to map oaks using pure and mixed pixels to train a random forest. The output depicts a gradient of tree cover that can be transformed into a crisp map. The accuracy assessment of the latter shows commission and omission errors of 17% and 18%.

Descrição

Costa, H., Machado, I., Moreira, F. D., Benevides, P., Moraes, D., & Caetano, M. (2021). Exploring the Potential of Sentinel-2 Data for Tree Crown Mapping in Oak Agro-Forestry Systems. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5807-5810). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553780 ----------- The work has been supported by projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPE FIRE (PCIF/MOS/0046/2017), and by Centro de Investigação em Gestão de Informação (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The Fig. 3: Tree crown map of Cork and Holm oaks with three levels of crown cover. Levels 100, 80 and 20 correspond to the classes of the same oak crown cover (e.g. level 100 are classes 1, 3 and 5 together in Table 1). Insets show contrasting examples of classification success with ortophotomaps of 2018 as background.