{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:09:48Z","timestamp":1773385788079,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI\/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Servi\u00e7os Distritais de Actividades Econ\u00f3micas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.<\/jats:p>","DOI":"10.3390\/rs12081279","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique"],"prefix":"10.3390","volume":"12","author":[{"given":"Sosdito","family":"Mananze","sequence":"first","affiliation":[{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, Rua do Campo Alegre s.n., 4169-007 Porto, Portugal"},{"name":"Escola Superior de Desenvolvimento Rural, Universidade Eduardo Mondlane, Vilankulo PC-257, Mozambique"},{"name":"Centro de Investiga\u00e7\u00e3o em Ci\u00eancias Geo-Espaciais, Observat\u00f3rio Astron\u00f3mico Prof. Manuel de Barros, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8280-0110","authenticated-orcid":false,"given":"Isabel","family":"P\u00f4\u00e7as","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, Rua do Campo Alegre s.n., 4169-007 Porto, Portugal"},{"name":"Centro de Investiga\u00e7\u00e3o em Ci\u00eancias Geo-Espaciais, Observat\u00f3rio Astron\u00f3mico Prof. Manuel de Barros, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-324X","authenticated-orcid":false,"given":"M\u00e1rio","family":"Cunha","sequence":"additional","affiliation":[{"name":"Faculdade de Ci\u00eancias, Universidade do Porto, Rua do Campo Alegre s.n., 4169-007 Porto, Portugal"},{"name":"Centro de Investiga\u00e7\u00e3o em Ci\u00eancias Geo-Espaciais, Observat\u00f3rio Astron\u00f3mico Prof. Manuel de Barros, Alameda do Monte da Virgem, 4430-146 Vila Nova de Gaia, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,17]]},"reference":[{"key":"ref_1","unstructured":"FAO (2016). 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