remote sensing Article A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps Mario Padial-Iglesias 1, *, Pere Serra 1 , Miquel Ninyerola 2 and Xavier Pons 1   Citation: Padial-Iglesias, M.; Serra, P.; Ninyerola, M.; Pons, X. A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps. Remote Sens. 2021, 13, 2662. https://doi.org/10.3390/rs13142662 Academic Editor: Ioannis Gitas Received: 21 May 2021 Accepted: 3 July 2021 Published: 6 July 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 Grumets Research Group, Departament de Geografia, Edifici B. Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; pere.serra@uab.cat (P.S.); xavier.pons@uab.cat (X.P.) 2 Departament de Biologia Animal, Biologia Vegetal i Ecologia, Edifici C. Universitat Autònoma de Barcelona, 08193 Bellaterra, Catalonia, Spain; miquel.ninyerola@uab.cat * Correspondence: mario.padial@uab.cat Abstract: Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered “true” per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10.9, a decrease in the omission error of 16.8, and a decrease in the commission error of 14.0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential. Keywords: land-cover change mapping; Landsat; digital image classification; ground truth samples; filtering rules 1. Introduction Historically, land cover mapping (LCM) has provided specific information for mon- itoring environmental impacts related, for instance, to soil degradation, deforestation, water quality and biodiversity loss [1–5]. It also plays a decisive role in local, national and international management policies [6–8]. Aerial photography [9] was key for the start of a new era of LCM, a field nowadays strongly related to remote sensing (RS) in the task of providing land cover information for human needs [10,11]. In 1994, the “Land Use and Cover Change” project was launched to address how biophysical and anthropogenic fac- tors impact land dynamics that produce environmental and social impacts [12]. Since then, LCM has substantially expanded due to the increasing demand for land cover information due to concern about global change and sustainability management, which are currently hot research topics within the scientific community. Studies based on LCM have been carried out practically all over the world (e.g., in Australia [13], Canada [14], China [15], Remote Sens. 2021, 13, 2662. https://doi.org/10.3390/rs13142662 https://www.mdpi.com/journal/remotesensing