Paper Info
Title
The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data
Abstract
This paper investigates the contribution of multi-temporal enhanced vegetation index (EVI) data to the improvement of object-based classification accuracy using multi-spectral moderate resolution imaging spectral-radiometer (MODIS) imagery. In object-oriented classification, similar pixels are firstly grouped together and then classified; the produced result does not suffer the speckled appearance and closer to human vision. EVI data are from the MODIS sensor aboard Terra spacecraft. 69 EVI data (scenes) were collected during the period of three years (2001-2003) in a mountainous vegetated area. These data sets were used to study the phenology of the land cover types. Different land cover types show distinct fluctuations over time in EVI values and this information might be used to improve object-oriented land cover classification. Two experiments were carried out: one was only with single date MODIS multispectral data, and the other one including also the 69 EVI images. Eight classes were distinguished: temperate forest, tropical dry forest, grassland, irrigated agriculture, rain-fed agriculture, orchards, lava flows and human settlement. The two classifications were evaluated with independent verification data, and the results showed that with multitemporal EVI data, the classification accuracy was improved 5.2%. Evaluated by McNemar's test, this improved was significant, with significance level p = 0.01.
Year
DOI
Venue
2009
10.1080/17538940902818311
INTERNATIONAL JOURNAL OF DIGITAL EARTH
Keywords
DocType
Volume
enhanced vegetation index (EVI),moderate resolution imaging spectral-radiometer (MODIS),phenology,object-based image classification
Journal
2
Issue
ISSN
Citations 
3.0
1753-8947
5
PageRank 
References 
Authors
0.96
2
3
Name
Order
Citations
PageRank
Yanyan Gao1319.12
J.F. Mas213613.09
A. Navarrete350.96