Tree Cover Estimation in Global Drylands from Space Using Deep Learning

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ID: 86693
2020
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Abstract
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.
Reference Key
guirado2020treeremote Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Guirado, Emilio;Alcaraz-Segura, Domingo;Cabello, Javier;Puertas-Ruíz, Sergio;Herrera, Francisco;Tabik, Siham;
Journal remote sensing
Year 2020
DOI
DOI not found
URL
https://www.mdpi.com/2072-4292/12/3/343
Keywords
Microbiology Engineering (General). Civil engineering (General) Technology Science Agriculture (General) economics as a science nutrition. foods and food supply nutritional diseases. deficiency diseases agriculture plant culture applied optics. photonics

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