Mr. Babatunde Adeniyi Osunmadewa Profile

Babatunde Adeniyi Osunmadewa

Student at TU Dresden

SPIE Involvement:

Author

Publications (7)

Proceedings Article | 25 October 2016 Paper

Remote sensing-based vegetation indices for monitoring vegetation change in the semi-arid region of Sudan

Majdaldin R. A., B. Osunmadewa, E. Csaplovics, D. Aralova

Proceedings Volume 9998, 99981X (2016) https://doi.org/10.1117/12.2242002

KEYWORDS: Vegetation, Agriculture, Climatology, Remote sensing, Landsat, Climate change, Earth observing sensors, Image enhancement, Near infrared, Environmental sensing

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Proceedings Article | 25 October 2016 Paper

Regional assessment of trends in vegetation change dynamics using principal component analysis

B. Osunmadewa, E. Csaplovics, Majdaldin R. A., C. Adeofun, D. Aralova

Proceedings Volume 9998, 99981Y (2016) https://doi.org/10.1117/12.2242011

KEYWORDS: Vegetation, Principal component analysis, Remote sensing, Radiometry, Ecosystems, Composites, Agriculture, Climatology, Spatial resolution, Climate change

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Proceedings Article | 18 October 2016 Paper

Environmental resilience of rangeland ecosystems: assessment drought indices and vegetation trends on arid and semi-arid zones of Central Asia

Dildora Aralova, Kristina Toderich, Ben Jarihani, Dilshod Gafurov, Liliya Gismatulina, Babatunde Osunmadewa, Majdaldin Rahamtallah Abualgasim

Proceedings Volume 10005, 100050R (2016) https://doi.org/10.1117/12.2242563

KEYWORDS: Vegetation, Soil science, Ecosystems, Remote sensing, Climatology, Positron emission tomography, Agriculture, Temperature metrology, Climate change, Data modeling

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Proceedings Article | 20 October 2015 Paper

Introducing a rain-adjusted vegetation index (RAVI) for improvement of long-term trend analyses in vegetation dynamics

Christine Wessollek, Pierre Karrasch, Babatunde Osunmadewa

Proceedings Volume 9644, 96440M (2015) https://doi.org/10.1117/12.2192821

KEYWORDS: Remote sensing, Vegetation, Modeling, Statistical analysis, Near infrared, Mathematical modeling, Meteorology, Statistical modeling, Data modeling, Spatial resolution

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SPIE Journal Paper | 8 July 2015

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

Babatunde Osunmadewa, Christine Wessollek, Pierre Karrasch

JARS, Vol. 9, Issue 01, 096029, (July 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096029

KEYWORDS: Vegetation, Statistical analysis, Agriculture, Statistical modeling, Data modeling, Analytical research, Time series analysis, Image segmentation, Visualization, Process modeling

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Proceedings Article | 11 October 2014 Paper

Identification of long-term trends in vegetation dynamics in the Guinea savannah region of Nigeria

Babatunde Osunmadewa, Christine Wessollek, Pierre Karrasch

Proceedings Volume 9239, 92390F (2014) https://doi.org/10.1117/12.2066876

KEYWORDS: Vegetation, Statistical analysis, Data modeling, Climatology, Climate change, Analytical research, Error analysis, Visualization, Satellites, Modeling

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The availability of newly generated data from Advanced Very High Resolution Radiometer (AVHRR) covering the last three decades has broaden our understanding of vegetation dynamics (greening) from global to regional scale through quantitative analysis of seasonal trends in vegetation time series and climatic variability especially in the Guinea savannah region of Nigeria where greening trend is inconsistent. Due to the impact of changes in global climate and sustainability of means of human livelihood, increasing interest on vegetation productivity has become important. The aim of this study is to examine association between NDVI and rainfall using remotely sensed data, since vegetation dynamics (greening) has a high degree of association with weather parameters. This study therefore analyses trends in regional vegetation dynamics in Kogi state, Nigeria using bi-monthly AVHRR GIMMS 3g (Global Inventory Modelling and Mapping Studies) data and TAMSAT (Tropical Applications of Meteorology Satellite) monthly data both from 1983 to 2011 to identify changes in vegetation greenness over time. Analysis of changes in the seasonal variation of vegetation greenness and climatic drivers was conducted for selected locations to further understand the causes of observed interannual changes in vegetation dynamics. For this study, Mann-Kendall (MK) monotonic method was used to analyse long-term inter-annual trends of NDVI and climatic variable. The Theil-Sen median slope was used to calculate the rate of change in slopes between all pair wise combination and then assessing the median over time. Trends were also analysed using a linear model method, after seasonality had been removed from the original NDVI and rainfall data. The result of the linear model are statistically significant (p <0.01) in all the study location which can be interpreted as increase in vegetation trend over time (greening). Also the result of the NDVI trend analysis using Mann-Kendall test shows an increasing (i.e. positive) trend in the time series. The significance of the result was tested using Kendall's tau rank correlation coefficient and the results were significant. Finally the NDVI data and TAMSAT data were analysed together in order to describe the relationship between both values. Although, increase in rainfall over the last decades enhances vegetation greenness, other factors such as land use change and population density need to be investigated in order to better explain changing trends of vegetation greening for the study area in the future.

Proceedings Article | 16 October 2013 Paper

Mapping and monitoring land-cover/land-use change in the gash agricultural scheme (Eastern Sudan) using remote sensing

Majdaldin Rahamtallah Abualgasim Mohammed, Elmar Csaplovics, Osunmadewa Babatunde

Proceedings Volume 8887, 88871E (2013) https://doi.org/10.1117/12.2029186

KEYWORDS: Agriculture, Remote sensing, Geographic information systems, Landsat, Ecosystems, Associative arrays, Climate change, Vegetation, Databases, Earth observing sensors

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Showing 5 of 7 publications

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