{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T14:28:04Z","timestamp":1772980084525,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["GCP\/LES\/052\/GER"],"award-info":[{"award-number":["GCP\/LES\/052\/GER"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Food and Agriculture Organization of the United Nations (FAO) is building a land cover monitoring system in Lesotho in support of ReNOKA (\u2018we are a river\u2019), the national program for integrated catchment management led by the Government of Lesotho. The aim of the system is to deliver land cover products at a national level on an annual basis that can be used for global reporting of official land cover statistics and to inform appropriate land restoration policies. This paper presents an innovative methodology that has allowed the production of five standardized annual land cover maps (2017\u20132021) using only a single in situ dataset gathered in the field for the reference year, 2021. A total of 10 land cover classes are represented in the maps, including specific features, such as gullies, which are under close monitoring. The mapping approach developed includes the following: (i) the automatic generation of training and validation datasets for each reporting year from a single in situ dataset; (ii) the use of a Random Forest Classifier combined with postprocessing and harmonization steps to produce the five standardized annual land cover maps; (iii) the construction of confusion matrixes to assess the classification accuracy of the estimates and their stability over time to ensure estimates\u2019 consistency. Results show that the error-adjusted overall accuracy of the five maps ranges from 87% (2021) to 83% (2017). The aim of this work is to demonstrate a suitable solution for operational land cover mapping that can cope with the scarcity of in situ data, which is a common challenge in almost every developing country.<\/jats:p>","DOI":"10.3390\/rs14143294","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Operational Use of EO Data for National Land Cover Official Statistics in Lesotho"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9911-7510","authenticated-orcid":false,"given":"Lorenzo","family":"De Simone","sequence":"first","affiliation":[{"name":"Office of the Chief Statistician, Food and Agriculture Organization of United Nations, 00153 Rome, Italy"}]},{"given":"William","family":"Ouellette","sequence":"additional","affiliation":[{"name":"FAOLS, Maseru 7588, Lesotho"}]},{"given":"Pietro","family":"Gennari","sequence":"additional","affiliation":[{"name":"Office of the Chief Statistician, Food and Agriculture Organization of United Nations, 00153 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. 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