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Article

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

1
Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain
2
Multidisciplinary Institute for Environment Studies “Ramón Margalef”, University of Alicante, San Vicente del Raspeig, 03690 Alicante, Spain
3
Andalusian Center for Assessment and Monitoring of Global Change (CAESCG), University of Almería, 04120 Almería, Spain
4
Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
5
Iecolab. Inter-University Institute for Earth System Research, University of Granada, 18006 Granada, Spain
6
Department of Biology and Geology, University of Almería, 04120 Almería, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 343; https://doi.org/10.3390/rs12030343
Received: 4 December 2019 / Revised: 31 December 2019 / Accepted: 15 January 2020 / Published: 21 January 2020
Accurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management. View Full-Text
Keywords: convolutional neural networks; data augmentation; deep learning; dry forest; forest mapping; large-scale datasets; transfer learning convolutional neural networks; data augmentation; deep learning; dry forest; forest mapping; large-scale datasets; transfer learning
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MDPI and ACS Style

Guirado, E.; Alcaraz-Segura, D.; Cabello, J.; Puertas-Ruíz, S.; Herrera, F.; Tabik, S. Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sens. 2020, 12, 343. https://doi.org/10.3390/rs12030343

AMA Style

Guirado E, Alcaraz-Segura D, Cabello J, Puertas-Ruíz S, Herrera F, Tabik S. Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sensing. 2020; 12(3):343. https://doi.org/10.3390/rs12030343

Chicago/Turabian Style

Guirado, Emilio, Domingo Alcaraz-Segura, Javier Cabello, Sergio Puertas-Ruíz, Francisco Herrera, and Siham Tabik. 2020. "Tree Cover Estimation in Global Drylands from Space Using Deep Learning" Remote Sensing 12, no. 3: 343. https://doi.org/10.3390/rs12030343

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