remote sensing Article Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping Ignacio Borlaf-Mena 1, * , Ovidiu Badea 2,3 and Mihai Andrei Tanase 1,2   Citation: Borlaf-Mena, I.; Badea, O.; Tanase, M.A. Assessing the Utility of Sentinel-1 Coherence Time Series for Temperate and Tropical Forest Mapping. Remote Sens. 2021, 13, 4814. https://doi.org/10.3390/rs13234814 Academic Editors: Michele Martone and Armando Marino Received: 14 September 2021 Accepted: 23 November 2021 Published: 27 November 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Geology, Geography and Environment, University of Alcalá, Calle Colegios 2, 28801 Alcalá de Henares, Spain; mihai.tanase@uah.es 2 Department of Forest Monitoring, Romanian National Institute for Research and Development in Forestry, INCDS “Marin Drăcea”, Bulevardul Eroilor 128, 077190 Voluntari, Romania; obadea@icas.ro 3 Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, Ludwig van Beethoven Str. 1, 500123 Bra¸ sov, Romania * Correspondence: ignacio.borlaf@edu.uah.es Abstract: This study tested the ability of Sentinel-1 C-band to separate forest from other common land use classes (i.e., urban, low vegetation and water) at two different sites. The first site is characterized by temperate forests and rough terrain while the second by tropical forest and near-flat terrain. We trained a support vector machine classifier using increasing feature sets starting from annual backscatter statistics (average, standard deviation) and adding long-term coherence (i.e., coherence estimate for two acquisitions with a large time difference), as well as short-term (six to twelve days) coherence statistics from annual time series. Classification accuracies using all feature sets was high (>92% overall accuracy). For temperate forests the overall accuracy improved by up to 5% when coherence features were added: long-term coherence reduced misclassification of forest as urban, whereas short-term coherence statistics reduced the misclassification of low vegetation as forest. Classification accuracy for tropical forests showed little differences across feature sets, as the annual backscatter statistics sufficed to separate forest from low vegetation, the other dominant land cover. Our results show the importance of coherence for forest classification over rough terrain, where forest omission error was reduced up to 11%. Keywords: SAR; Sentinel-1; C-band; forest cover; radar; LULUCF 1. Introduction Forest ecosystems host a large portion of terrestrial biodiversity, and provide many ecosystem services, such as timber and food production, risk mitigation (i.e., flood, erosion), and climate regulation, as forests hold a large portion of terrestrial biomass, and its growth and degradation play an essential role on climate and atmospheric CO 2 dynamics. This has prompted several international agreements to preserve forest services and biodiversity, along with specific procedures to track forest cover and status. One of the earliest interna- tional efforts for tracking forest status was undertaken under the Food and Agriculture Organization (FAO) through the global Forest Resources Assessment (FRA), whose first report was published in 1948. FRA defines forest as areas with tree canopy cover above 10%, 5 m minimum tree height, and a minimum extent of 0.5 Ha [1]. Forests’ increasing importance is reflected by subsequent conventions such as the United Nations (UN) Rio Convention on Biological Diversity [2], and the UN Framework Convention on Climate Change [3], UNFCC. The UNFCC was extended by the Kyoto protocol and the Paris agreements [4,5] with the commitment of the signatory countries to reduce their greenhouse gasses emissions through, among other, reforestation programs. Further forest-related agreements include the Bonn Challenge [6], a global effort for forest restoration, and the New York declaration of forests [7], aimed at reducing the rate of defor- estation. Agreements under the UNFCC use indicators considered critical to characterize Remote Sens. 2021, 13, 4814. https://doi.org/10.3390/rs13234814 https://www.mdpi.com/journal/remotesensing