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Accueil du site → Doctorat → Allemagne → Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach

Technische Universität Dresden (2015)

Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach

Mahmoud El-Abbas Mustafa, Mustafa

Titre : Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach

Auteur : Mahmoud El-Abbas Mustafa, Mustafa

Université de soutenance : Technische Universität Dresden

Grade : Doctor of Natural Science (Dr. rer. Nat.) 2015

Présentation
Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information.
An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes.

Mots clés  : Fernerkundung, Landnutzung, Landbedeckung, objekt-basierte Bildanalyse, Blauer Nil — remote sensing, land use, land cover, object-based image analysis, Blue Nile

Résumé de la thèse (QUCOSA)

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Page publiée le 10 novembre 2016, mise à jour le 30 novembre 2016