{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:28:47Z","timestamp":1760236127690,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/CTA-OHR\/32360\/2017"],"award-info":[{"award-number":["PTDC\/CTA-OHR\/32360\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing methodologies could contribute to a more sustainable agriculture, such as monitoring soil preparation for cultivation, which should be done properly, according to the topographic characteristics and the crop\u2019s nature. The objectives of this work are to (1) demonstrate the potential of unmanned aerial vehicle (UAV) technology in the acquisition of 3D data before and after soil tillage, for the quantification of mobilised soil volume; (2) propose a methodology that enables the co-registration of multi-temporal DTMs that were obtained from UAV surveys; and (3) show the relevance of quality control and positional accuracy assessment in processing and results. An unchanged-area-matching method based on multiple linear regression analysis was implemented to reduce the deviation between the Digital Terrain Models (DTMs) to calculate a more reliable mobilised soil volume. The production of DTMs followed the usual photogrammetric-based Structure from Motion (SfM) workflow; the extraction of fill and cut areas was made through raster spatial modelling and statistical tools to support the analysis. Results highlight that the quality of the differential DTM should be ensured for a reliable estimation of areas and mobilised soil volume. This study is a contribution to the use of multi-temporal DTMs produced from different UAV surveys. Furthermore, it demonstrates the potential of UAV data in the understanding of soil variability within precision agriculture.<\/jats:p>","DOI":"10.3390\/rs13214336","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"4336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Measurement of Soil Tillage Using UAV High-Resolution 3D Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Carla","family":"Rebelo","sequence":"first","affiliation":[{"name":"Interdisciplinary Centre of Social Sciences (CICS.NOVA), Faculty of Social Sciences and Humanities, (NOVA FCSH), Universidade NOVA de Lisboa, Avenida de Berna 26-C, 1069-061 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9974-1869","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Nascimento","sequence":"additional","affiliation":[{"name":"CERIS\u2014Civil Engineering Research and Innovation for Sustainability, Department of Civil Engineering, Architecture and Georesources, Instituto Superior T\u00e9cnico, University of Lisbon, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khanal, S., Kc, K., Fulton, J., Shearer, S., and Ozkan, E. (2020). Remote Sensing in Agriculture\u2014Accomplishments, Limitations, and Opportunities. Remote Sens., 12.","DOI":"10.3390\/rs12223783"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sishodia, R.P., Ray, R.L., and Singh, S.K. (2020). Applications of Remote Sensing in Precision Agriculture: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12193136"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hristov, G., Raychev, J., Kinaneva, D., and Zahariev, P. (2018, January 26\u201328). Emerging Methods for Early Detection of Forest Fires Using Unmanned Aerial Vehicles and Lorawan Sensor Networks. 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