{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:42:02Z","timestamp":1770972122233,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2017,2,25]],"date-time":"2017-02-25T00:00:00Z","timestamp":1487980800000},"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>Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (&lt;1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies\u2014if swimming pools are not taken into account\u2014and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas.<\/jats:p>","DOI":"10.3390\/rs9030211","type":"journal-article","created":{"date-parts":[[2017,2,27]],"date-time":"2017-02-27T11:00:20Z","timestamp":1488193220000},"page":"211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-7684","authenticated-orcid":false,"given":"Rapha\u00ebl","family":"D\u2019Andrimont","sequence":"first","affiliation":[{"name":"Earth and Life Institute\u2014Environment, Universit\u00e9 Catholique de Louvain, Croix du Sud 2, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6105-5705","authenticated-orcid":false,"given":"Catherine","family":"Marlier","sequence":"additional","affiliation":[{"name":"Earth and Life Institute\u2014Environment, Universit\u00e9 Catholique de Louvain, Croix du Sud 2, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5355-4988","authenticated-orcid":false,"given":"Pierre","family":"Defourny","sequence":"additional","affiliation":[{"name":"Earth and Life Institute\u2014Environment, Universit\u00e9 Catholique de Louvain, Croix du Sud 2, 1348 Louvain-la-Neuve, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2005.02.015","article-title":"Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis","volume":"97","author":"Greenberg","year":"2005","journal-title":"Remote Sens. 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