<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>. Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.</p>
</abstract>

. Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively low cost of their acquisition make them very attractive data source for Geographical Information Systems (GIS). The main goal of this paper is to prepare, run and evaluate image classification using a block of raw aerial images obtained from Digital Mapping Camera (DMC). Classification was preceded by preparation of raw images. It contained geometric and radiometric correction of every image in block. Initial images processing lead to compensate their brightness differences. It was obtained by calculating two vegetation indices: Normalized Difference Vegetation Index (NDVI) and Green Normalized Vegetation Index (gNDVI). These vegetation indices were the foundation of image classification. PCI Geomatics Geomatica 10.2 and Microimages TNT Mips software platforms were used for this purpose.

Digital aerial images land cover classification based on vegetation indices

Jerzy Cierniewsk i Adam Mickiewicz University, Institute of Physical Geography and Environmental Planning

Department of Soil Science and Remote Sensing of Soils

Quaestiones Geographicae

{"article-title":"Digital aerial images land cover classification based on vegetation indices"}

. Knowledge of how land cover has changed over time improve assessments of the changes in the future. Wide availability of remote sensed data and relatively...

Digital aerial images land cover classification based on vegetation indices

Publié en ligne: 21 oct. 2012

Pages: 5 - 23

DOI: https://doi.org/10.2478/v10117-012-0026-4

This content is open access.