Estimation of Mediterranean forest attributes by the application of k-NN procedures to multitemporal Landsat ETM+ images

• Published in: International journal of remote sensing Vol. 26; no. 17; pp. 3781 - 3796
• Main Authors: Maselli, F., Chirici, G., Bottai, L., Corona, P., Marchetti, M.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 10.09.2005; Taylor and Francis
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; Southern Europe; Italy; Nearest neighbour; Thematic Mapper; maps; Mediterranean vegetation; Estimation; Europe; simulation; Space remote sensing; Landsat; Growing season; vegetation; inventory; forests; imagery; Procedure; growth; Mediterranean region; Multidate observation; seasonal variations; Forest inventory; Leaf area index
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160500166433

Routine applications of nonparametric estimation methods to satellite data for assisting the creation of forest inventories in Northern European countries are stimulating interest in the possible extension of these methods to more complex Mediterranean areas. This is the subject of the current work, which presents an experiment based on the integration of remotely sensed images and sample field measurements aimed at producing forest attribute maps in central Italy. Testing was carried out in an area where 370 geocoded field plots, sampled on a single-stage cluster design, were collected to characterize wood and non-wood forest attributes. These ground data served to apply various k-Nearest Neighbour (k-NN) estimation procedures to multitemporal Landsat 7 ETM+ images in order to map major forest attributes (basal area and simulated leaf area index, LAI). More specifically, the investigation focused on evaluating the effects of using satellite images from different periods of the growing season and spectral metrics of increasing complexity. The results achieved by the examined methods are finally discussed in order to provide guidelines for possible operational utilization.