Analyse spatio-temporelle de l’influence du climat sur l’abondance des plantes dans une partie de la ceinture de la forêt pluviale du sud-ouest du Nigéria
Folasade Olubunmi Oderinde
Abstract : This study examines the relationship between vegetation and climatic parameters in Osun State, Nigeria between 1985 and 2015. Researcher uses Landsat imageries of the wet and dry season and climate imageries of temperature and precipitation for the study. The Normalized Difference Vegetation Index (NDVI) data was extracted using red and infrared bands in each season to ascertain the vegetation health. Linear regression analysis was used to determine the relationship between vegetation health, temperature and precipitation. The NDVI values were projected to 2030 using Markov’s projection. Results show a reduction of vegetation cover from 81.08% in 1985 to 70.52% in 2015. During wet season, temperature and precipitation accounted for 80% and 40% of vegetation health respectively while in dry season, temperature and precipitation accounted for 39% and 36% respectively. Except for medium class, a reduction of vegetation health is forecast by 2030. The study recommends a periodic monitoring of vegetation for natural resource management.
Keywords: Normalized Difference Vegetation Index, Temperature, Precipitation, Landsat imageries, Markov’s projection
Résumé : Cette étude examine la relation entre la végétation et les paramètres climatiques dans l’État d’Osun, au Nigéria, entre 1985 et 2015. Des images des saisons humide et sèche ainsi que les données thermiques et pluviométriques ont été utilisées à des fins d’analyse de l’indice de végétation normalisé par différence (NDVI) extrait des bandes rouges et infrarouges. L’analyse de régression linéaire a permis de déterminer la relation entre la santé de la végétation, la température et les précipitations. Les valeurs du NDVI ont été projetées jusqu’en 2030 à l’aide de la projection de Markov. Les résultats montrent une diminution de la couverture végétale de 81,08% en 1985 et 70,52% en 2015. Pendant la saison des pluies, la combinaison températures et précipitations contribuent à la santé de la végétation à 80% et 40% alors que celle des précipitations et de la température représentent 39% et 36% respectivement. Exception faite de santé moyenne de la végétation son état général devrait diminuer en 2030. En matière de gestion des ressources naturelles, les résultats suggèrent une surveillance périodique de la végétation.
Mots clés : Indice de végétation par différence normalisée, température, précipitations, images Landsat, projection de Markov
Plan
Introduction
Materials and methods
Acquisition and image processing
Results and discussions
Vegetation cover of the study area
NDVI in the area during the wet season
Dry season values of NDVI in the area
Temperature and precipitation pattern during the study period
Relationship between climate and NDVI
Conclusion
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INTRODUCTION
Vegetation cover forms an intrinsic part of the ecosystem, and provides the basis for the survival of virtually all living organisms (Hansen, DeFries et al., 2000; Osunmadewa, Wessollek et al., 2015). It is an important component of the physical environment, which plays a crucial role in maintaining balance in ecosystems by moderating surface temperatures, reducing noise pollution along roadsides and removing pollutants such as carbon dioxide, nitrogen oxides and particulate matter from the atmosphere (Trammel and Sluss, 2012). Vegetation also has significant effect on soil erosion and flood (Li, Niu et al., 2014; Brookhuis and Hein, 2016) and trees are important sinks for carbon dioxide (Lu, Yin et al., 2010; Oderinde, 2014). A very significant factor of vegetation cover is climate. Climate influences the growth, distribution, composition and structure of plant species and communities. The vegetation of an area is thus one of the most sensitive indicators of climate especially in terms of recuperation after a period of disturbance. Changes in climate have impacts on ecosystems, vegetation pattern and livelihoods among others. Climate change influences the growth, distribution and abundance of vegetation (Kelly and Gould, 2008).The global climate change that is ongoing all over the globe has brought some major changes in the global vegetation pattern.
Remote sensing helps monitoring the impact of climate variation on vegetation (Kelly and Gould, 2008 Kelly and Gould, 2008). As such, Vegetation Index contributes to capture spatial patterns of plant communities (Yengoh, Dent et al., 2014). For example, the Normalized Difference Vegetation Index (NDVI) has been used to disseminate information about global food production and availability (Yengoh, Dent et al., 2014).The relationship between the NDVI and climatic variables have been analysed by Ichii, Kawabata et al. (2002). Consequently, there is a strong relationship between NDVI and climatic parameters (Fashae, Olusola et al., 2017; Yelwa and Isah, 2006). There is a long tradition in Nigeria using remote sensing for analysis of vegetation anomalies (Aweda and Adeyewa, 2011; Osunmadewa and Wessollek, 2014; Yengoh, Dent et al., 2014). Most of these studies focuses on North East, Middle belt, and northwestern part of Nigeria. The present study analyses the influence of climate on vegetation abundance in a part of the rainforest belt of Nigeria, which is in the southeast.
MATERIALS AND METHODS
The study was conducted in Osun State, Southwest Nigeria. The area lies between latitude 70 00’ and 80 02’ N and longitude 30 45’ E and 50 02’ E (Fig 1) and covers an area of 9,238.09 sq. km. The area enjoys the humid tropical climate with distinct wet and dry season. The mean annual temperature is between 280C and 350C while the mean annual precipitation ranges between 1,125mm in the derived savanna and 1,475mm in the rainforest belt (Sofoluwe, Tijani et al., 2011). The area is located within the crystalline basement complex rocks of Nigeria and has an undulating topography. It is drained by several rivers, which include Rivers Osun, Sasa and Owena. The soils belong to the highly ferruginous tropical red soils associated with the basement complex rocks. Crops cultivated include yam (Discorea), Cassava (Manihot esculenta), Maize (Zea maïs). The original vegetation of the area is lowland tropical rainforest, which has been largely replaced by secondary forest due to population growth and the associated human activities including agriculture, lumbering, road construction, fuelwood extraction among others (ESMP, 2014). Cultivated trees such as coco tree (Theobroma cacao), palm oil tree (Elaies guineensis), beech wood or white teak (Gmelina arborea), citrus tree (Citrus spp) now dominate the vegetation cover in many places. However, well-developed rainforests still exist in forest reserves especially in the southern part of Osun state.
Figure 1: Map of the study area
ACQUISITION AND IMAGE PROCESSING
Satellite imageries for the study area are from the archives of the United States Geological service (USGS). Landsat Multispectral Scanner (MSS) image of 1985, Enhanced Thematic Mapper (ETM+) image of 2000 and Operational Land Imager of 2015 with a spatial resolution of 30 m were used for the study. The MSS images of 1985 were acquired on 17th June and 9th November, the images in 2000 were acquired on 6th June and 7th November while the images of 2015 were acquired on 5th June and 5th November. Because the study area experiences two seasons in a year, the imageries were obtained in June and November to represent the wet and dry season respectively during the study period. The satellite imageries were imported into Erdas Imagine 14.0 software. Images enhancement including haze reduction and layer stacking helped improve their qualities. Geometric and radiometric corrections performed on all images, and false colour composite images for creating a single raster dataset from multiple bands were completed as well. In addition stacking band 4, 3, 2 and 1 of the Landsat images in each epoch helped generating this dataset. Therefore, the colour layer-stacking tool in Erdas Imagine 14.0 was used for this process. Masking was done on each of the composite imageries to create a subset of the imagery covering only the study area. This reduced the run time of the analysis at every stage. The land use and land cover for the area was evaluated using supervised classification. Training data was developed using composite image of band 4, 3 and 2. The dominant classes of land cover were identified and later confirmed using ground truthing method. The different NDVI classes were also extracted using red and infrared bands in each epoch to ascertain the vegetation characteristics (Ochege, George et al., 2017).
Climate data for the study were obtained from Climate Research Unit, University of East Anglia, and Norwich, United Kingdom. The datasets were downloaded in zipped formats (.tar) before they were unzipped into Network Common Data Form (NetCDF) formats readable by ArcGIS 10.5 software (Nawajish & Ding, 2016) using the multidimensional tools. These data are raster data. The “make NetCDF feature layer” tool was specifically used for this operation. The precipitation and temperature values of the grid points that covers the study area were extracted from the raster data. The study made use of descriptive statistics including percentages and frequencies to summarize the data set. Trend analysis was thereafter performed on the obtained values to examine the changes that have occurred over time (Ochege, George et al., 2017), using the SPSS version 20.0. Linear regression analysis was used to determine the relationship between vegetation characteristics and temperature; and precipitation. The Z test was conducted to ascertain the relationship between the weather parameters and NDVI. With Markov projection, the NDVI values of 2000 and 2015 were used to project the NDVI to 2030, (Ozah, Dami et al., 2012; Ahmad, Adeyewa et al., 2013). The results were presented using graphs and tables.
RESULTS AND DISCUSSIONS
Vegetation cover of the study area
The land use/land cover types identified in Osun State between 1985 and 2015 were bare land, built-up area, rock outcrop, vegetation and water body (Table 1).
Table 1: Land Use Pattern of Osun State between 1985 and 2015
The area covered by bare land, built up area and water body increased between 1985 and 2015. The area covered by rock outcrop and vegetation were observed to have reduced during the study period. The reduction observed in the vegetation cover may be due to the expansion in the built-up area as noted by Igbawua, Zhang, et al., (2016) and Okeke and Enoh, (2016).
NDVI in the area during the wet season
The values of the NDVI during the wet season were ranked and grouped into three classes as well as the area covered by each class (Table 2, Figures 2,3,4, and 5).
Table 2: NDVI (ha) class in the wet season during the study period. NDVI (ha) during the wet season (June)
Figure 2: NDVI Analysis for June 1985
Figure 3: NDVI analysis for June 2000
Figure 4: NDVI analysis for June 2015
Figure 5: The projected NDVI analysis for June 2030
0 represents values which range between -1.0 to values below or equal to -0.5 which can be described as low NDVI. 1 represents values which are between -0.5 to values below or equal to 0.5 which can be described as medium NDVI while 2 represents values from 0.5 to values below or equal to 1.0 which can be described as high NDVI. The three classes were subsequently reclassified to be 0 to represent “No vegetation”, 1 to represent “Poor vegetation” and 2 to represent “Healthy vegetation”. It was observed that the area where there is little or no NDVI values increased from 9% in 1985 to about 11.35% in 2000 to 17.66% in 2015 and is also projected to cover 21.5% of the area in 2030. This reveals that vegetation health reduced during the wet season and will further reduce in future. The area where there is poor NDVI values covered 10.23% of the area in 1985, it increased to 14.80% in 2000 and further increased to 60.32% in 2015. It is projected that the area covered by the NDVI class will increase to 73.20% in 2030. This also points to the fact that vegetation health reduced during the study period with an abundance of dry leaves mixed with sandy patch in fields expected to have green leaves, this will also increase in future. The area covered by rich NDVI was observed to be 80.77% in 1985. This reduced to 73.85% in 2000 and further reduced to 22.02% in 2015.It is projected that in 2030, the NDVI class will cover 5.30% of the area. This means that the area covered by green leaves reduced during the study period and is projected to reduce in 2030. It can be observed that vegetation health reduced during the wet season. The NDVI results shows a general decrease in the chlorophyll needed to absorb enough sunlight needed for photosynthesis by plants, hence reducing vegetation health over the years. This is in line with the findings of Samson et al., (2017), Okeke, and Enoh, (2016). In 2030, NDVI values are expected to reduce across the bands if the present conditions remain the same. The change in NDVI values in the wet season during the study period shows a general depreciation in vegetation health (Table 3).
Table 3: Change in NDVI (ha) for wet season during the study period
Dry season values of NDVI in the area
Figures 6, 7, 8 and 9 show the NDVI values in the area for the month of November, which represents the dry season in 1985, 2000, 2015 and 2030. Table 4 presents the area covered by each NDVI class in the dry season during the study period.
Figure 6: NDVI analysis for November
Figure 7: NDVI analysis for November
Figure 8: NDVI analysis for November 2015
Fig 9: Projected NDVI analysis for November 2030
Table 4: NDVI (ha) during the dry season (November)
The table shows that the area where there is little or no vegetation mixed with bare sand covered an area of 9.24% in 1985, 20.58% in 2000 and later increased to about 25.9% in 2015 and is projected to cover about 31.50% of the area in 2030. It was also observed that areas where there is poor vegetation covered an area of 23.30% in 1985, increased to about 25.65% in 2000, increased to 44.6% in 2015 and is projected to be about 53.3% in 2030. The area covered by healthy vegetation covered an area of 67.46% in 1985, reduced to 53.77% in 2000 and reduced to about 29.5% in 2015. This area is projected to decrease to about 15.20% in 2030. According to Musa, Jiva et al., (2011), the NDVI values being higher at low band indicated that the chlorophyll responsible to absorb sunlight needed for photosynthesis is no more present, meaning the chlorophyll is only present at regions having high values at the high band. The results of the NDVI analysis showed a general decrease in green vegetation cover during the study period. It is projected that by 2030, there will be a general decrease of NDVI across the three (3) bands of observation. This means there would be a further decrease in vegetation health within the study area by 2030 if the current conditions remains, as they are (Fashae, Olusola et al., 2017). Table 5 shows the change in the NDVI values at the different bands in the dry season during the study period. The table reveals that the NDVI value with little or no vegetation increased between 2000 and 2015 and is projected to increase in 2030. The NDVI values with poor vegetation were observed to have increased between 1985 and 2000.The values increased between 2000 and 2015 and is projected to increase in 2030.The NDVI value with healthy vegetation were observed to have reduced between 1985 and 2000 and between 2000 and 2015 and is projected to reduce in 2030.
Table 5: Change in NDVI values during the dry season
Temperature and precipitation pattern during the study period
Figures 10 and 11 shows the temperature and precipitation pattern in the study area during the period under study. The mean monthly temperature and precipitation as well as the standard deviation are presented in tables 6 and 7.
Figure 10: Trend of Precipitation and Temperature in the wet season (June)
Figure 11: Precipitation and temperature trend during the dry season (November)
Table 6: Climate parameters during the wet season (June)
Table 7: Climate parameters during the dry season (November)
The average temperature of the study area remained steady at about 200C during the dry season and about 250C during the wet season. Adesina, Odekunle et al., (2008) reported that monthly temperature in the southern part of the country range between 220 C and 320 C while Odjugo, (2011) also observed that the area would be having temperatures as hot as about 260C.
Meanwhile, the rainfall for wet season over the years was observed to be 206 mm for 1985, 266 mm for 2000 and 202 mm for 2015. While the rainfall for the dry season as expected was recorded as 49 mm for 1985, 6 mm for 2000 and 29 mm for 2015. The results show that there was an increase in rainfall between 1985 and 2000 while there was a reduction between 2000 and 2015. During the dry season, there was a decrease in rainfall between 1985 and 2000 and an increase in rainfall between 2000 and 2015.
The decreasing rainfall and increasing temperatures observed is an evidence of the prevailing effects of the global climate change in the country (Odjugo, 2011).
Relationship between climate and NDVI
Table 8 presents the result of the regression analysis between the climatic parameters and NDVI during the wet season.
Table 8: Regression Coefficient table explaining the relationship between NDVI (dependent variable) and precipitation and temperature for dry season
The table shows that there was positive correlation between precipitation and NDVI during this period. This is an indication that a positive change in precipitation will lead to a positive change in NDVI, provided that the other conditions remain the same. The correlation value of 0.98 indicates that the abundance of healthy vegetation or the reverse is greatly dependent on precipitation. It was also observed that there is a positive correlation between NDVI and temperature. The implication of this is that a positive change in temperature will result to a positive change in NDVI provided that the other conditions remain the same. These observations support the result of earlier research carried out by Rodríguez-Iturbe and Porporato (2004) as well as D’odorico and Porporato (2006). They reported that the vegetation of semiarid environments is well adapted to highly variable conditions of rainfall and temperature. It was also noted that seasonal precipitation and temperature fluctuations are the primary factors that affect vegetation dynamics and plant growth in these ecosystems. The correlation results largely attest to the fact that NDVI depends on temperature and precipitation together with other environmental and climatic factors as noted by Igbawua, Zhang et al, 2016.
Table 9 presents the result of the regression analysis between the climatic parameters and NDVI during the dry season.
Table 9: Regression Coefficient table explaining the relationship between NDVI (dependent variable) and precipitation and temperature for dry season
The table shows that there was negative correlation between precipitation and NDVI during this period. This is an indication that a negative change in precipitation will lead to a positive change in NDVI, provided that the other conditions remain the same. It was observed that there is a positive correlation between NDVI and temperature. The implication of this is that a positive change in temperature will result to a positive change in NDVI provided that the other conditions remain the same. The correlation results largely attest to the fact that NDVI depends on temperature and precipitation together with other environmental and climatic factors like humidity, sunshine hours as noted by Bounoua, Collatz et al., (2000) and Igbawua, Zhang et al., (2016). The correlations were observed to be significant at 5 % significance level.
CONCLUSION
The study revealed that the climatic parameters significantly affected the NDVI during the wet season than during the dry season. A general decline was observed in the vegetation health during the period and was projected to reduce in 2030. The capacity of geo-information technologies has been demonstrated in assessing the land use pattern and vegetation health in the study area. This will enable adequate provision to be made for effective strategies in order to forestall the adverse impacts of climate change in a developing country where the consequences are likely to be very severe.
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To cite this article
Electronic reference
Folasade Olubunmi Oderinde (2021). « Spatio-temporal analysis of the influence of climate on plant abundance in a part of the Rainforest Belt of Southwestern Nigeria ». Canadian journal of tropical geography/Revue canadienne de géographie tropicale [Online], Vol. (8) 1. Online August 15, 2021, pp. 1-6. URL: http://laurentian.ca/cjtg
Author
Folasade Olubunmi Oderinde
Department of Geography and Environmental Management
Tai Solarin University of Education
Ijebu Ode, Nigeria
Email: sadeoderinde@gmail.com
