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.
The change detection of land use and land cover has always been the focus of remotely sensed study and application. Based on techniques of image fusion, a new approach of detecting vegetation change according to vector of brightness index (BI) and perpendicular vegetation index (PVI) extracted from multi-temporal remotely sensed imagery is proposed. The procedure is introduced. Firstly, the Landsat eTM+ imagery is geometrically corrected and registered. Secondly, band 2,3,4 and panchromatic images of Landsat eTM+ are fused by a trous wavelet fusion, and bands 1,2,3 of SPOT are registered to the fused images. Thirdly, brightness index and perpendicular vegetation index are respectively extracted from SPOT images and fused images. Finally, change vectors are obtained and used to detect vegetation change. The testing results show that the approach of detecting vegetation change is very efficient.
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