TRENDS IN VEGETATION COVER OVER NIGER-BENUE TROUGH FROM USING 1-KM SPOT NDVI DATA

Transcription

1 TRENDS IN VEGETATION COVER OVER NIGER-BENUE TROUGH FROM USING 1-KM SPOT NDVI DATA Nathaniel Bayode ENIOLORUNDA Geography Department, Faculty of Social Sciences, Usmanu Danfodiyo University, Sokoto. & (+234(0) ) and Olalekan Mumin BELLO Geography Department, Faculty of Natural and Applied Sciences, Umaru Musa Yar Adua University, Katsina. (+234(0) ) Abstract The study assessed vegetation trends in the Niger-Benue Trough of Nigeria. 1-km SPOT Normalized Difference Vegetation Index (NDVI) and rainfall monthly time series covering between 1998 and 2007 were obtained and recomposed into annual time series. The data were subjected to time series analysis to determine the slope coefficients of Ordinary Least Square regression (OLS) for the analysis of vegetation change. The normally distributed slope coefficients were categorized into Degraded Vegetation, Unchanged Vegetation and Improved Vegetation based on 2 standard deviation to the mean. Results showed that Mashegu/Mokwa and Wushishi (Niger State) and Kachia (Kaduna State), Ardo-Kola (Taraba State), Alkaleri (Bauchi State) FCT and Amaku, Awe and Gidan Rai (Nasarawa State) were degraded, while places around Ipawo, Adiele (Kogi State) and North-west of Ilorin recorded vegetation improvement. Correlation analysis between NDVI and rainfall showed that anthropogenic influence rather than climate dynamism was responsible for vegetation change in the study area. As vegetation change has serious impact on soil, biodiversity, food security and climate, it was recommended that government should among other things make kerosene available and affordable for domestic consumption, campaign for agroforestry, establish cattle ranches across the region and routinely assess vegetation using finer resolution satellite data such as Nigeria-Sat for the sustainability of the environment. Key words: Vegetation degradation, Niger-Benue Trough, SPOT, NDVI and Time Series. Sokoto Journal of the Social Sciences Vol. 2 No.1 37

2 Introduction Vegetation plays a key role in earth s surface-atmosphere interaction, the alteration of which affects the biogeochemical cycles (Eric, 2003; Cabral et al., 2006; Jaclyn et al., 2012). It provides food, shelter, fuel, pasture, habitat and income; hence it is an important aspect of any landscape environment (Omuto, 2011). A change in vegetation coverage is an indication of ecological environment change (Xiaoxia, et al., 2008). Climate change and anthropogenic pressure on the landscape are mostly responsible for the alteration in vegetation composition and density (Glenn et al., 1998; Hountondji, 2006; Pouliot et al., 2009; Karen et al., 2010), which in most cases (especially in the semi-arid and arid environments) result in degradation of habitats for flora and fauna (Panday and Ghimire, 2012) and affect human livelihood. Shifting rainfall patterns result in change in grazing intensity and pattern, agricultural pattern, soil erosion, drought and flooding that impact on man (Janpeter et al., 2012). In Nigeria, the vast rural majority whose livelihoods are precariously dependent on the environment exert undue pressure on the vegetation as the population increases geometrically (Adeniyi, 1993; Muhammad, 2002; Eric, 2003; Ojo et al, 2009). Hountondji et al. (2006) and Pouliot et al., (2009) reported that direct human activities on the landscape caused substantial amount of vegetation degradation in the past century. The Niger-Benue Trough is the ecotone between the North and South of Nigeria. A number of studies, (Mashi, 1998; Mayowa and Omojola, 2005; Eniolorunda et al, 2008; Yelwa 2008) have reported vegetation degradation and proximate causes wholly or partly covering the country. Such studies have shown that the vegetation in the extreme Northern part of Nigeria is fast turning into Sahel. Odjugbo and Ikhuoria (2003) in Odjugbo (2010) observed desertification of areas North of the Niger-Benue Trough, prompting human migration into areas of less risk. This in turn increases the anthropogenic pressure on the vegetation of the buffer landscape, besides the attendant social problems. The Niger-Benue Trough exhibits some complexities in terms of ethnic multiplicity, cultural diversity and livelihoods. Hence the anthropogenic impacts on the vegetation are likely to vary with the pattern of the complex diversities. However, the pattern of vegetation dynamics in the area has not been given emphasis in the literature, whereas vegetation degradation mapping is one of the basic components of ecological database of any region, as it provides the benchmark data for planning, conservation and assessment of ecological status of the ecosystem (Salami, 1999; Fernando and Adria, 2009). Earth Observation (EO) data allows the detection of vegetation anomalies and trends in biomass change (FAO, 2005). The synoptic coverage, repeat cycle and multispectral nature of satellite instruments provide the advantage for earth trend modelling (Janssen, 2004). Normalized Difference Vegetation Index (NDVI) is an index of choice for generating and providing information on the spatio- Sokoto Journal of the Social Sciences Vol. 2 No.1 38

3 temporal dynamics of vegetation (Panday and Ghimire, 2012). Derived from any instrument that observes the earth in the Near Infrared (NIR) and the Red bands, NDVI calculations are based on the principle that actively growing green plants strongly absorb radiation in the visible region of the spectrum (the PAR, or Photo-synthetically Active Radiation ) while strongly reflecting radiation in the Near Infrared region (Ryan, 1997). Several studies have utilized NDVI (particularly time series) data from different instruments for vegetation cover monitoring. These instruments include Advanced Very High Resolution Radiometer (AVHRR) (Hountondji, 2006; Yelwa, 2008; Pouliot et al., 2009; Omuto 2011; Panday et al., 2012), Moderate Resolution Imaging Spectrometer (MODIS) (Xiaoxia et al., 2008; Omuto 2011), SPOT (Cabral et al., 2006; Jarlan et al., 2008; Li et al. 2008; Eckert et al., 2011; Coban et al.,2010; Yelwa and Eniolorunda, 2012) and Landsat (Jussof and Manaf, 1994; Munyati, 2000; Chen and Rao, 2008). The continuous earth s surface observation and subsequent archiving of SPOT NDVI, and coupled with the fact that it is made free for the research community enable researchers to monitor vegetation degradation at a very high temporal scale. The VEGETATION and VEGETATION2 instruments on board SPOT4 and SPOT5 respectively image the land surface in blue ( μm), Red ( μm), Near Infra-red (NIR: μm) and Mid- Infra-Red (MIR: μm) with a high spectral (absolute calibration accuracy of about 5%) and geometric accuracy (less than 0.3 pixel) (Jarlan et al., 2008). A major environmental challenge of the 21st century is environmental degradation; it adversely affects the sustainable relationship between ecosystems and the livelihoods of people worldwide (Reynolds et al., 2007b). The Niger-Benue Trough is regarded as the food basket of Nigeria. As vegetation degradation can result in soil quality degradation (Abreu et al., 1999) which in turn can affect food security for the teaming population (Narendra et al., 2012), an assessment of the level of vegetation degradation in the Niger-Benue Trough of Nigeria becomes a necessity. The objectives of the study are to determine the hotspots of vegetation change and account for the observed vegetation degradation at some selected hotspots. Study Area The study area is located between latitudes N and 10 0 N and longitudes 4 0 E and 12 0 E, covering approximately 246, 420 km 2 (Figure 1). It is found North of the rainforest belt, largely within the Guinea Savannah, which is made up of mixed deciduous and semi-deciduous woodlands (USAID, 2008). The area contains some relict forest patches with species indicative of the rainforests. Species found in the area are Butyrospermum parki (shear butter), Parkia filicoidea (locust bean tree), Diospyros mespiliformis (African Ebony), Mangifera indica (mango tree), Adansonia digitata (Baobab tree) (Eniolorunda et al., 2008). Sokoto Journal of the Social Sciences Vol. 2 No.1 39

4 14 0'0"N 2 0'0"E 4 0'0"E Sokoto 6 0'0"E 8 0'0"E 10 0'0"E NIGER REPUBLIC 12 0'0"E 14 0'0"E µ C HAD Lake Chad 14 0'0"N 12 0'0"N Kebbi Zamfara Katsina Kano Jigawa Yobe Borno 12 0'0"N 10 0'0"N 8 0'0"N 6 0'0"N C L I B U R EP IN EN B Gombe Kaduna Bauchi New Bussa Mashegu Niger Kachia Alkaleri Wushishi N Mokwa Benue Plateau Adamawa Kwara R FCT, Abuja i ver Riv e r N Ardo-Kola R OO iger Kurmi Ilorin Ndambazau Nassarawa Awe Oyo Amaku Gidan Rai Taraba C AME Ipawo Ekiti Kogi Osun Adiele Benue Legend Ogun Ondo Niger-Benue Trough Lagos Edo Delta Bayelsa Enugu Anambra Ebonyi Cross River Imo Abia Akwa Ibom Cross River Rivers Cross River Notable Settlements State Boundary International Boundary Major River Lake Kilometers 10 0'0"N 8 0'0"N 6 0'0"N 4 0'0"E 6 0'0"E 8 0'0"E 10 0'0"E 12 0'0"E 14 0'0"E 16 0'0"E Figure 1: Map of Nigeria showing Niger-Benue Trough Source: Balogun (2003) Although most trees are not as valuable as those found in the rainforest, they are mostly extracted for fuel-wood or cleared to make room for agriculture (USAID, 2008). The environment is a good habitat for wild animals, most of which are going extinct due to large-scale anthropogenic modifications. Elevation ranges from 100m around the trough to 1500m in Jos plateau, presenting complex basaltic relief features (Balogun, 2003). The area is welldrained by Rivers Niger and Benue and their tributaries, serving as the most important rivers in the country. Rain season is experienced between April and October, while the dry season lasts between November and March. About 1,500mm of rain is recorded around Jos plateau due largely to the orographic effect. Elsewhere, rainfall is between 1,000mm and 1,200mm. Minimum temperature of about 20 0 C and mean annual temperature of 22 0 C are recorded around Jos plateau, while the mean annual temperature elsewhere is around 27 0 C (Balogun, 2003). Population density is sparse in most places but high in Jos, FCT and Minna. Due to the fertile alluvial soil in the basin, agriculture is the main economic activity of the area, making the Sokoto Journal of the Social Sciences Vol. 2 No.1 40

5 area the major producer of tubers and grains in the country. However, activities such as bush burning, overgrazing, fuelwood extraction, among others have rendered the vegetation degraded. Materials and Methods Data used for this study are 1km SPOT NDVI and rainfall data. The NDVI data was freely downloaded via as ten-daily composites from the month of April 1998 to December The data was extracted after which the study area was sub-mapped. Maximum Value Composite (MVC) was used to recompose the ten-daily composites into monthly composites as it has the capability of further correcting the atmospheric effect in the data (Bijay et al., 2009); the annual composites were derived by averaging the 12 monthly composites. The rainfall data, which came in monthly totals, was obtained from the Nigerian Meteorological Agency (NiMets). Monthly rainfall values were later summed up to annual totals, covering the period between 1998 and Time Series Analysis was performed on the NDVI data to determine the slope coefficient of the Ordinary Least Squares (OLS) regression between the values of each pixel overtime. OLS regression is a generalized linear modelling technique that may be used to model a single response variable (vegetation) which has been recorded on at least an interval time scale (Hutcheson, 2011). It is represented mathematically as Y = a + bx, where Y is the continuous response variable (vegetation), X is the continuous explanatory variable (time), a is the value of Y when X is zero, and b is the regression coefficient that describes the change in Y that is associated with a unit change in X (Hutcheson, 2011). The slope coefficient map legend is a continuum of positive, zero and negative values which are normally distributed (Yelwa and Eniolorunda, 2012). Positive slope coefficients represent areas of improved vegetation, while negative values represent areas of vegetation degradation; zero values are areas of nochange in vegetation (Eastman, 2009). For easy interpretation, areas of real change were isolated from those of minor variation based on 2-standard-deviationto-the-mean ( ± 2, where is the mean and the standard deviation) on the assumption of normal distribution. Thus, the slope coefficients were categorized into three classes of Degraded Vegetation, Unchanged Vegetation and Improved Vegetation. Two rain stations were selected for each of the positive and negative areas. This was to test for correlation between NDVI and rainfall time series values at the selected points in order to account for the vegetation degradation or improvement. Image processing and analysis were carried out within ArcGIS and Idrisi Taiga environments. Results The time series analysis performed on the NDVI data produced a slope coefficient map (Figure 2) showing spatial variation in the degrees of vegetation dynamics. The legend shows Sokoto Journal of the Social Sciences Vol. 2 No.1 41

6 Figure 2: Slope Map off Niger-Benue Trough ope values raanging from a continuum of slo 05 to which are normallyy distribu uted (Figure 3). In order to categorizee the slope coefficien nts, the (-0.567) and d (0.8815) where extracted e from Figure 3 and fed d into the eq quation: ± 2, wherre iss the mean n and the standarrd deviation. The recclassified slop pe coefficien nt map is presented in n Figure 4. ogram of Slope Coefficient showing s normal distribution n Figure 3: Histo Sokoto Journal of the Social S Sciences Vol. 2 No.1 42

7 Figure 4: Reclassified Slope CoefficientMap Figure 4 shows that places around Mashegu/Mokwa and Wushishi (Niger State) and Kachiaa (Kaduna State), Ardo- Kola (Taraba State), Alkaleri (Bauchi State) FCT and Amaku, Awe and Gidan Rai (Nasarawa State) have negative slope coefficients, indicating vegetation degradation through time. Places around Ipawo, Adiele (Kogi State) and North-west of Ilorin have positive slope coefficients, indicating vegetation improvement. NDVI values at North-west and Kachia were queried (Table 1) and profiled as shown in Figure 5. of Ilorin, Adiele, Mashegu/Mokwa The result shows upward trends at Ilorin North-west and Adiele, indicating improvement in vegetation while downward trends were observed at Mashegu/Mokwa and Kachia, indicating vegetation degradation. Table 1: Annual rainfall and NDVI values for some selected locations Year Annual Rain (mm) at Ilorin NDVI at Ilorin Northwest Annual Rain (mm) at Lokoja NDVI at Annual Adiele Rainfall (mm) Minna Annual NDVI at Rainfall Mashegu/M (mm) at okwa Kaduna NDVI at Kachia Sokoto Journal of the Social Sciences Vol. 2 No.1 43

8 Source: NiMets a b c d Figure 5: NDVI Profiles at Ilorin North-west (a), Adiele (b), Mashegu/Mokwa (c) and Kachia (d) Time series profile of rainfall (Figure 6) shows slight downward trends for Ilorin and Kaduna, slight upward trends for Lokoja and Minna. a Annual lrainfall (mm) 2000 y = x Year b Annual Rainfall (mm) y = 22.72x Year Sokoto Journal of the Social Sciences Vol. 2 No.1 44

9 Annual Rainfall (mm) 2000 y = 14.56x R² = c Years b Figure 6: Rainfall trends at Ilorin (Kwara) (a), Lokoja (Kogi State) (b), Minna (Niger State) (c) and Kaduna (d) rain stations Spearman rank correlation was used to test for the relationship between NDVI and rainfall at Ilorin north-west (Kwaraa State), Adielee (Kogi State) ), Mashegu/Mokwa (Niger State) and Kachiaa (Kaduna), as both variables are known to have a harmonic relationship (Eastman, 2009; Omuto, 2011). Correlation value for Ilorin north-west is with a P-value of while that of Adiele is with a P-value 0.329, showing weak and insignificant positive relationship between NDVI and rainfall at both stations. In other words, rainfall is not responsible for vegetation increase at the locations. For Mashegu/Mokwa, the correlation is with a P-value of 0.45, showing a weak negative and insignificant correlation between both variables. The slight rise in rainfall trend and downward trend in NDVI values at the station suggest that the vegetation degradation is anthropogenic. For Kachia, correlation is highly positive between NDVI and rainfall with a coefficient of 0.59 and a P-value of However, this correlation is not significant, meaning that the downward trends in both rainfall and NDVI are only similar by coincidence. Discussion and Conclusion Generally, correlation values between NDVI and rainfall at the selected stations are weak and statistically insignificant; hence climate is exempted from being responsible for vegetation change in the study area. Most changes in vegetation are reportedly anthropogenic. Omuto (2011) performed a time series image change detection for vegetation cover and composition using MODIS and AVHRR- NDVI for Kenya. The study modelled a relationship between NDVI and rainfall, and was able to separate each other s effects on vegetation. The author discovered that human factor was responsible for the alteration of vegetation composition and overalll cover loss. Li et al., (2011) explored the relationship between vegetation change trends and climatic and anthropogenic factors in Inner Mongolia, China using AVHRRR data. Increase in Vegetation was found to be positively connected to anthropogenic influence and not to precipitation. Pouliot et al., (2009) assessed the potential causes of vegetation trends in the north and south of Canada using AVHRR NDVI. Climate was found to have stronger effects on Sokoto Journal of the Social Sciences Vol. 2 No.1 45

10 vegetation in the north but anthropogenic influence was stronger in the south. This further buttresses the fact that increasing human pressure on the environment (increase in human population) enhances desertification (Hountondji et al., 2006), as the northern Canada is less populated than the south. Panday and Ghimire (2012) submit that although recent warming trends in climate have prompted assessment of vegetation trends in different parts of the world, the general increase in vegetation greening in most parts of south Asia between 1982 and 2003 were attributable to human factor. Rainfall and NDVI although are positively correlated (Eastman, 2009), variations exist in vegetation responses to rainfall, especially at local scales due to differences in floristic composition, soil types, land use practices, among others (Hountondji et al., 2006). It can be concluded that since the findings of this study are consistent with other studies, the vegetation of the Niger-Benue Trough of Nigeria has been plagued more by human influence than by climatic variation. Such influences are related to fuel-wood extraction, over grazing, continuous cultivation, bush burning, hunting, lumbering, among others (Yelwa, 2008; Eniolorunda and Bello, 2012). Vegetation degradation has serious impact on man in the areas of soil, biodiversity, food security and climate. Therefore, the starting point for correcting the menace is for the government to make kerosene available and affordable for domestic consumption to curtail inordinate fuel-wood extraction. It is also recommended that government should embark on a serious campaign, first to raise the consciousness of people towards positive disposition to tree planting. Also, tree planting should be matched with establishment of cattle ranches to curtail the pervasive and perverse animal grazing. Improved farming practices (e.g. Agroforestry) that are friendly with the environment should be introduced to farmers. Respective state governments should embark on routine assessment of vegetation in their states using finer resolution data such as Nigeria-Sat for effective environmental policy implementation. Acknowledgements Appreciation goes to VITO Belgium for making the SPOT NDVI available on-line for free access. The provision of rainfall data by Mal. A. T. Umar is highly commended. Also, the knowledge shared with the authors of this study by Mal. M. B. Muritala is well appreciated. Sokoto Journal of the Social Sciences Vol. 2 No.1 46

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