Spatio-Temporal Modelling of Vegetation Change Dynamics in the Guinea Savannah Region of Nigeria Using Remote Sensing and GIS Techniques

Remote Sensing and Applied Geoinformatics, Volume 15

Babatunde Adeniyi Osunmadewa

Kurzübersicht

The study thus fill the gap of inadequate information on environmental condition and human induced change in land cover types which is essential for proper land use management, vegetation monitoring and early warning measure for desertification.
Osunmadewa Babatunde Adeniyi (born in 1976) obtained his BSc at the Department of Forestry and Wildlife, Federal University of Agriculture, Abeokuta, Nigeria, in 2004 where he also worked as graduate fellow in 2006. He completed his MSc in 2010 and earned a doctoral degree (Dr. rer. nat) in 2017 at the Faculty of Environmental Sciences, Technische Universität Dresden (TUD), Germany. Currently, he is working on a project as research associate under the research group of remote sensing at TUD. He has professional experience in the fields of remote sensing and GIS, environmental impact assessment, desertification monitoring and data mining. He has actively participated in international conferences and has represented the International Forestry Students‘ Association at high-level fora in Europe.
ISBN: 978-3-944101-89-7
Veröffentlicht: 31.12.2019, Volume 15 . Auflage, Einband: Broschur, Abbildung und Tabellen: zahlr., Seiten 164, Format 176 x 250 , Gewicht 1 kg
Lieferzeit: 2-3 Tage
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Babatunde Adeniyi Osunmadewa

Spatio-Temporal Modelling of Vegetation Change Dynamics in the Guinea Savannah Region of Nigeria Using Remote Sensing and GIS Techniques

Volume 15 of the series "Remote Sensing and Applied Geoinformatics" / Band 13 der Reihe „Fernerkundung und angewandte Geoinformatik“
Published by Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden
(Herausgegeben von Univ. Prof. Dr. habil. Elmar Csaplovics, Lehrstuhl Remote Sensing, FR Geowissenschaften, TU Dresden)

164 pages, format DIN B5 (176 x 250 mm), weight 0.50 kg, cover: paperback, numerous illustrations, many of them colored. Language: English. Price: 40,00 Euro. ISBN 978-3-944101-89-7. Publishing house: Rhombos Verlag, Berlin 2019

264 Seiten, Format DIN B5 (176 x 250 mm), Gewicht 0,55 kg, Einband: Broschur, zahlreiche Abbildungen, viele davon farbig. Sprache: Englisch. Preis: 40 Euro. ISBN 9783941216860. Rhombos-Verlag, Berlin 2018

About this book

Increased anthropogenic activities across the Guinea savannah region of Nigeria have led to change in the physiognomic characteristic of vegetation cover over the last few decades. In the context of the Guinea savannah region of Nigeria where agriculture still plays a major role in people’s economy, it is important to identify the relationship between climatic variables, vegetation productivity and human activities which can be used to understand the on-going transition processes. Therefore the use of remotely sensed datasets at varying temporal and spatial resolution to better understand the dynamics of change at regional to local scale is imperative. This study examines spatial and temporal relationship between NDVI and climate parameters (remote sensing datasets), land use land cover change (LULCC) and the perspective of local people on vegetation change dynamics using NDVI3g time series datasets from Global Inventory Modeling and Mapping Studies (GIMMS), rainfall datasets from Tropical Applications of Meteorology Satellite (TAMSAT), temperature datasets from Climate Research Unit (CRU), national land use land cover (LULC) data from Forestry Management Evaluation & Coordination Unit (FORMECU), MERIS global land cover datasets from European Space Agency, Landsat imagery and socio-economic data to better understand greening trend phenomena across the Guinea savannah region of Nigeria. Both parametric and non-parametric statistical models were employed for the assessment of long-term inter-annual trend of NDVI and climate time series datasets while harmonic regression analysis was used to examine change in seasonality and phenological characteristics of vegetation. Significant change in LULC was revealed over the study period which is evidenced in loss of savannah woodland to agricultural land. The study thus fill the gap of inadequate information on environmental condition and human induced change in land cover types which is essential for proper land use management, vegetation monitoring and early warning measure for desertification.

The author

Osunmadewa  Babatunde  Adeniyi  (born  in  1976)  obtained  his  BSc  at  the  Department  of  Forestry  and  Wildlife,  Federal  University  of  Agriculture,  Abeokuta,  Nigeria, in  2004  where  he  also  worked  as  graduate fellow in 2006. He completed his MSc in 2010 and earned a doctoral degree (Dr. rer. nat) in 2017 at the Faculty of Environmental Sciences, Technische Universität Dresden (TUD), Germany. Currently,  he is working on a project as research associate under the research group of remote sensing at TUD. He has professional experience in the fields of remote sensing and GIS, environmental impact assessment, desertification monitoring and data mining. He has  actively participated in international conferences and has represented the International Forestry Students‘Association at high-level fora in Europe.

The editor

Prof. Dr. Elmar CsaplovicsProf. Dr. Elmar Csaplovics obtained a doctorate in remote sensing   at TU Wien in 1982. He was a post-doc research fellow at  the French National Institute for Agricultural Research (INRA) in Montpellier and at the Department of Geology, Geophysics and Geoinformatics, FU Berlin 1988-1992. After habilitation in remote sensing at TU Wien he is professor of Remote Sensing at TU Dresden since 1993. He was visiting professor at University College London (2007), at Université Paris VII Denis Diderot (2014) and at TU Wien (2015). His research focus is on remote sensing and applied geoinformation analysis for monitoring and assessment of land use land cover with emphasis on wetlands, arid lands, landscape history and natural heritage.


Table of Contents

1    Background    1
1.1    Introduction    1
1.2    Problem statement    3
1.3    Justification    3
1.4    Research question    4
1.5    Objectives    4
1.6    Thesis organization    4
2    Remote sensing and its applications for monitoring egetation and land use change    7
2.1    The concept of the ecosystem    7
2.2    Application of remote sensing for monitoring vegetation and land use land cover    8
2.2.1    Remote sensing and vegetation analysis    10
2.3    Time series analysis    11
2.4    Combined application of remote sensing and social science    12
2.5    Review of some studies on vegetation and land use land cover change in Nigeria    12
3    Study area    13
3.1    Description of the study area    13
3.2    Climate    14
3.3    Rainfall    15
3.4    Temperature    17
3.5    Relief    19
3.6    Drainage system    19
3.7    Geology of Nigeria    21
3.8    Overview of soil types in Nigeria and the study area    22
3.9    Overview of vegetation types in Nigeria and in the study areas    22
4    Methodology    25
4.1    Description of data collection/acquisition    25
4.1.1    Primary data collection    25
4.1.2    Secondary data collection    25
4.2    Description of datasets    26
4.2.1    Normalized Difference Vegetation Index (NDVI) dataset    26
4.2.2    Data pre-processing    29
4.3    Inter-annual trend analysis for selected locations    29
4.3.1    Determination of seasonality frequency time series    30
4.3.2    Decomposition method    31
4.3.3    Normality test    31
4.4    Trend analysis    32
4.4.1    Linear regression model    32
4.4.2    Theil-Sen median slope    32
4.4.3    Mann-Kendall trend analysis    33
4.5    Correlation coefficient between NDVI and climate parameters    33
4.6    Comparision between GIMMS and MODIS dataset    34
4.7    Seasonal trend analysis (STA)    34
4.7.1    Data and method    34
4.7.2    Phenological metrics extraction    36
4.7.3    Temporal metrics of NDVI and their values    37
4.7.4    Methodology for qualitative analysis    37
4.7.5    Survey method    38
4.7.6    Questionnaire    38
4.7.7    Key informants interview    38
4.7.8    Sample size and selection of respondents    38
4.7.9    Data analysis    38
4.8    Land use land cover (LULC)    39
5    Results and discussion    43
5.1    Inter-annual trend analysis    43
5.1.1    Abstract    43
5.1.2    Inter-annual trends in vegetation and climatic parameters    43
5.1.3    Linear NDVI trends    44
5.1.4    Monotonic trends in NDVI    45
5.2    Spatio-temporal analysis of trends in NDVI and
climatic time series datasets for selected locations    46
5.2.1    NDVI    46
5.2.2    Rainfall analysis for Niger state    48
5.2.3    Decomposition of NDVI time series dataset for Niger state    49
5.2.4    Temperature analysis for Niger state    49
5.3    Frequency of seasonality    50
5.3.1    Determination of seasonal frequency in NDVI    50
5.3.2    Decomposition of NDVI time series dataset for Niger state    52
5.3.3    Decomposition of rainfall time series for Niger state    53
5.3.4    Decomposition of temperature time series
dataset for Niger state    54
5.4    Trend analysis    55
5.4.1    NDVI trend analysis in Niger state    55
5.4.2    Inter-annual trend analysis for climatic datasets for
selected locations in Niger state    61
5.4.3    Correlation analysis between NDVI and climatic data    64
5.5    Comparison of GIMMS and MODIS datasets    67
5.6    Discussion    68
5.6.1    Overall trends in NDVI and climatic data    68
5.6.2    Correlation between NDVI and Climatic drivers    69
6        Assessment of seasonal trends and variation in
     vegetation cover through phytophenological characteristics    71
6.1    Abstract    71
6.2    Seasonal Trend Analysis for the study areas    71
6.3    Interpretation of seasonal NDVI curves for the study regions    74
6.3.1    Seasonal NDVI curve for Kogi state    75
6.3.2    Seasonal NDVI curve for Kwara state    76
6.3.3    Seasonal NDVI curve for Niger state    77
6.4    Phenological metrics    79
6.5    Discussion    82
7    Assessment of land use land-cover change using er change in the study areas
(Kogi, Kwara and Niger state)    84
7.3    Land use land cover assessment in Kwara state    87
7.4    Discussion    99
7.5    Assessment of land use land cover change (LULCC)
using Landsat imagery    105
8    Assessment of explanatory variables influencing vegetal-cover transition in the study area    112
8.1    Socio economics/demographic characteristics of respondents    112
8.2    Respondents sources of Livelihood    113
8.3    Land-use pattern    114
8.4    Effect of land use on soil    115
8.5    Impact of land use on vegetation change dynamics    115
8.6    Agricultural production    116
8.7    Livestock production    118
8.8    Discussion    118
9    Synthesis of results    122
9.1    Conclusion    125
9.2    Limitation of the study    126
9.3    Recommendation    127
10    Reference    128

List of Acronyms and Abbrevations

ABS    Annual Abstract of Statistics
ACF    Autocorrelation Function
AOI    Area of Interest
AutoMCU    Automated Monte Carlo Unmixing
AVHRR    Advanced Very High Resolution Radiometer
AVI    Ashburn Vegetation Index
BGS    Beginning of Growing Season
BOKU    University of Natural Resources and Life Sciences
BS    Bare Substrate
CC    Cross-Correlation
CIS    Carnegie Institution for Science
CLASlite    Carnegie Landsat Analysis System-lite
CMK    Contextual Mann-Kendall
CO2     Carbon Dioxide
CRU    Climatic Research Unit
CTVI    Corrected Transformed Vegetation Index
cT     Continental Tropical
DVI    Difference Vegetation Index
EGS    End of the growing season
EMD    Empirical Mode Decomposition
ENVI    ENvironment for Visualizing Images
EOS    End of Season
ESA    European Space Agency
ETM    Enhanced thematic mapper
FAO    Food and Agricultural Organization
FC    Fractional Cover
FORMECU    Forestry Monitoring and Evaluation Coordinating Unit
GCOS    Global Climate Observation System
GIMMS    Global Inventory Modeling and Mapping Studies
GLC    Global Land Cover
GloVis    Global Visualization Viewer
GSR    Guinea Savannah Region
GTS    Global Telecommunication System
GVI    Global Vegetation Index
IITA    International Institute of Tropical Agriculture
ITCZ    Inter-Tropical Convergence Zone
ITD    Inter-Tropical Discontinuity
KRC    Kendall’s Rank Correlation
LULC    Land Use Land Cover
LULCC    Land Use Land Cover Change
MERIS    MEdium Resolution Imaging Spectrometer
MODIS    Moderate Resolution Imaging Spectroradiometer
MK    Mann-Kendall
MSAVI    Modified Soil-Adjusted Vegetation Index
NDVI    Normalized Difference Vegetation Index
NDVImax     Maximum Normalized Differenced Vegetation Index
NIR    Near Infrared
NOAA    National Oceanic and Atmospheric Administration
NPV    Non-Photosynthetic Vegetation
NRVI    Normalized Ratio Vegetation Index
OLI    Operational Land Imager
OLS    Ordinary Least-Square
PACF    Partial Autocorrelation Function
POES    Polar Operational Environmental Satellite
PPMC    Pearson Product Moment Correlation
PVI    Perpendicular Vegetation Index
PV    Photosynthetic vegetation
QQ    Quantile-quantile
RVI    Ratio Vegetation Index
SAVI    Soil-Adjusted Vegetation Index
SOS    Start of Season
SPOT    Satellite Pour l'Observation de la Terre
STA    Seasonal Trend Analysis
TAMSAT    Tropical Applications of Meteorology using Satellite data
Tm    Tropical maritime
TM    Thematic Mapper
TIR    Thermal Infrared
TIRS    Thermal Infrared Sensor
TVI    Transformed Vegetation Index
TSAVI    Transformed Soil-Adjusted Vegetation Index
TS    Theil-Sen
USGS    United States Geological Survey
Vis    Vegetation Indices
WA    West Africa
WMO     World Meteorological Organization
°C    Degree Celsius

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