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Open AccessArticle

Mapping Chestnut Stands Using Bi-Temporal VHR Data

1
Departamento de Física, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
2
Remote Sensing Working Group, Institute of Computer Science, University of Osnabrück, 49090 Osnabrück, Germany
3
Departamento de Informática, Universidad of Almería, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2560; https://doi.org/10.3390/rs11212560
Received: 18 September 2019 / Revised: 24 October 2019 / Accepted: 29 October 2019 / Published: 31 October 2019
This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife. View Full-Text
Keywords: WorldView; bi-temporal image; extended morphological profiles; random forest; Canary Islands WorldView; bi-temporal image; extended morphological profiles; random forest; Canary Islands
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MDPI and ACS Style

Marchetti, F.; Waske, B.; Arbelo, M.; Moreno-Ruíz, J.A.; Alonso-Benito, A. Mapping Chestnut Stands Using Bi-Temporal VHR Data. Remote Sens. 2019, 11, 2560.

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