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Automatic Change Detection of MODIS NDVI using Artificial Neural Networks  

Jung, Myung-Hee (Dept. of Digital Media Eng., Anyang University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.49, no.2, 2012 , pp. 83-89 More about this Journal
Abstract
Natural Vegetation cover, which is very important earth resource, has been significantly altered by humans in some manner. Since this has currently resulted in a significant effect on global climate, various studies on vegetation environment including forest have been performed and the results are utilized in policy decision making. Remotely sensed data can detect, identify and map vegetation cover change based on the analysis of spectral characteristics and thus are vigorously utilized for monitoring vegetation resources. Among various vegetation indices extracted from spectral reponses of remotely sensed data, NDVI is the most popular index which provides a measure of how much photosynthetically active vegetation is present in the scene. In this study, for change detection in vegetation cover, a Multi-layer Perceptron Network (MLPN) as a nonparametric approach has been designed and applied to MODIS/Aqua vegetation indices 16-day L3 global 250m SIN Grid(v005) (MYD13Q1) data. The feature vector for change detection is constructed with the direct NDVI diffenrence at a pixel as well as the differences in some subset of NDVI series data. The research covered 5 years (2006-20110) over Korean peninsular.
Keywords
Remotely Sensed Data; Vegetation index; NDVI; Neural Network; MODIS;
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