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Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests / M.F.A. Vogels in International journal of applied Earth observation and geoinformation, vol 54 (February 2017)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 54 (February 2017) . - pp 114 - 123 Titre : Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests Type de document : Article/Communication Auteurs : M.F.A. Vogels, Auteur ; S.M. de Jong, Auteur ; G. Sterk, Auteur ; E.A. Addink, Auteur Année de publication : 2017 Article en page(s) : pp 114 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] base de données historiques [Termes IGN] carte agricole [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] cultures [Termes IGN] Ethiopie [Termes IGN] image numérisée [Termes IGN] Pays-Bas [Termes IGN] photographie aérienne [Termes IGN] photographie en noir et blanc [Termes IGN] surface cultivée [Termes IGN] utilisation du sol Résumé : (auteur) Land-use and land-cover (LULC) conversions have an important impact on land degradation, erosion and water availability. Information on historical land cover (change) is crucial for studying and modelling land- and ecosystem degradation. During the past decades major LULC conversions occurred in Africa, Southeast Asia and South America as a consequence of a growing population and economy. Most distinct is the conversion of natural vegetation into cropland. Historical LULC information can be derived from satellite imagery, but these only date back until approximately 1972. Before the emergence of satellite imagery, landscapes were monitored by black-and-white (B&W) aerial photography. This photography is often visually interpreted, which is a very time-consuming approach. This study presents an innovative, semi-automated method to map cropland acreage from B&W photography. Cropland acreage was mapped on two study sites in Ethiopia and in The Netherlands. For this purpose we used Geographic Object-Based Image Analysis (GEOBIA) and a Random Forest classification on a set of variables comprising texture, shape, slope, neighbour and spectral information. Overall mapping accuracies attained are 90% and 96% for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s, which is beneficial for regions where historical land-use statistics are mostly absent. Numéro de notice : A2017-050 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2016.09.003 En ligne : http://dx.doi.org/10.1016/j.jag.2016.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84229 [article]