Reference : Developing a web-based system to visualize vegetation trends by a nonlinear regressio...
Dissertations and theses : Bachelor/master dissertation
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
Developing a web-based system to visualize vegetation trends by a nonlinear regression algorithm
Wei, Yufei mailto [University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Geography and Spatial Planning (DGEO) >]
Lund University, ​Lund, ​​Sweden
Master of Science in Geomatics
Eklundh, Lars
Jamali, Sadegh
Groth, Roger
Mansourian, Ali
[en] Physical Geography and Ecosystem Analysis ; NDVI ; Vegetation Trends ; Nonlinear Regression Algorithm ; PolyTrend ; Web Development ; Django ; MATLAB ; Python
[en] Comparing with traditional linear regression methods that used to monitor vegetation trends, a nonlinear regression algorithm (PolyTrend) developed by Jamali et al. (2014) can provide more accurate information of vegetation trends by fitting a polynomial line with a degree of up to three for ASCII-formed time-series NDVI (Normalized Difference Vegetation Index) dataset of a single pixel. To extend the ability of the PolyTrend algorithm for processing time-series NDVI satellite imagery and to increase its accessibility, a web-based system for visualizing vegetation trends by the PolyTrend algorithm has been developed. The PolyTrend web-based system allows users to define the value of statistical significance of the PolyTrend algorithm, the nominal range of the input data, and the range of desired NDVI input to be processed. It applies the PolyTrend algorithm to each pixel of the uploaded time-series NDVI satellite imagery dataset. It returns the types of vegetation changes, the slope of the changes of NDVI values in the whole time span, and whether the net change of NDVI increases or decreases during this period, in the forms of ASCII files (i.e. text files) and binary files (i.e. images). By refining the existing PolyTrend algorithm written in MATLAB and embedding it in a web environment, the PolyTrend web-based system has proved its ability in monitoring global vegetation trends using raw time-series NDVI satellite imagery.
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