Vegetation change detection for urban areas based on extended change vector analysis

Hui Yu; Yonghong Jia

doi:10.1117/12.712719

28 October 2006 Vegetation change detection for urban areas based on extended change vector analysis

Hui Yu, Yonghong Jia

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190E (2006) https://doi.org/10.1117/12.712719
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

This study sought to develop a modified change vector analysis(CVA) using normalized multi-temporal data to detect urban vegetation change. Because of complex change in urban areas, modified CVA application based on NDVI and mask techniques can minify the effect of non-vegetation changes and improve upon efficiency to a great extent. Moreover, drawing from methods in Polar plots, the extended CVA technique measures absolute angular changes and total magnitude of perpendicular vegetation index (PVI) and two of Tasseled Cap indices (greenness and wetness). Polar plots summarized change vectors to quantify and visualize both magnitude and direction of change, and magnitude is applied to determine change pixels through threshold segmentation while direction is applied as pixel's feature to classifying change pixels through supervised classification. Then this application is performed with Landsat ETM+ imageries of Wuhan in 2002 and 2005, and assessed by error matrix, which finds that it could detect change pixels 95.10% correct, and could classify change pixels 91.96% correct in seven change classes through performing supervised classification with direction angles. The technique demonstrates the ability of change vectors in multiple biophysical dimensions to vegetation change detection, and the application can be trended as an efficient alternative to urban vegetation change detection and classification.

Citation Download Citation

Hui Yu and Yonghong Jia "Vegetation change detection for urban areas based on extended change vector analysis", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190E (28 October 2006); https://doi.org/10.1117/12.712719

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KEYWORDS

Vegetation

Statistical analysis

Earth observing sensors

Image segmentation

Landsat

Near infrared

Remote sensing

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