Mapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery

TitleMapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery
Publication TypeJournal Article
Year of Publication2010
AuthorsMitri, GH, Ioannis Z. Gitas
JournalGeoscience and Remote Sensing, IEEE Transactions on
Volume48
Pagination1613-1618
ISSN0196-2892
Keywordsbrutia mature, Earth Observing-1, EO-1 Hyperion imagery, field-spectroradiometry measurement, fires, forest regeneration, forestry, geophysical image processing, homogenous revegetated areas, Hyperspectral remote sensing, image classification, image segmentation, Kappa Index of Agreement, Mediterranean island, nigra mature, object detection, object training, object-based classification, object-oriented model, pine trees, Pinus brutia regeneration, Pinus nigra regeneration, postfire vegetation recovery mapping, satellite image, Thasos, vegetation mapping, vegetation recovery
Abstract

The aim of this paper is to investigate whether it is possible to accurately map postfire vegetation recovery on the Mediterranean island of Thasos by employing Earth Observing-1 (EO-1) Hyperion imagery and object-based classification. Specific objectives include the following: 1) locating and mapping areas of forest regeneration and other vegetation recovery and distinguishing among them; 2) distinguishing between Pinus brutia regeneration and Pinus nigra regeneration within the area of forest regeneration; and 3) examining whether it is possible to distinguish between areas of forest regeneration (Pinus brutia, Pinus nigra) and mature forest. The data used in this study consist of satellite images, field-spectroradiometry measurements, and field observations of the homogenous revegetated areas. The methodology comprised four consecutive steps. The first step involved preprocessing of the Hyperion image and field data. Subsequently, an object-oriented model was developed, which involved three steps, namely, image segmentation, object training, and object classification. The process resulted in the separation of five classes (??brutia mature,?? ?? nigramature,?? ??brutia regeneration,?? ??nigra regeneration,?? and ??other vegetation??). The accuracy assessment revealed very promising results (approximately 75.81% overall accuracy, with a Kappa Index of Agreement of 0.689). Some classification confusion involving the classes of Pinus brutiaregeneration and Pinus nigra regeneration was recorded. This could be attributed to the absence of large homogenous areas of regenerated pine trees. The main conclusion drawn in this paper was that object-based classification can be used to accurately map postfire vegetation recovery using EO-1 Hyperion imagery.

 

URLhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5290013
DOI10.1109/TGRS.2009.2031557