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Journal of Applied Remote Sensing

Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria
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Paper Abstract

Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.

Paper Details

Date Published: 8 July 2015
PDF: 23 pages
J. Appl. Remote Sens. 9(1) 096029 doi: 10.1117/1.JRS.9.096029
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Babatunde Adeniyi Osunmadewa, Technische Univ. Dresden (Germany)
Christine Wessollek, Technische Univ. Dresden (Germany)
Pierre Karrasch, Technische Univ. Dresden (Germany)


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