Regressive vegetation cover status in river Kaduna catchment area Kaduna, Nigeria

Regressive vegetation cover status in river Kaduna catchment area Kaduna, Nigeria

Zaharaddeen Isa Bala Danjuma 

Department of Geography, Ahmadu Bello University, Zaria 810107, Nigeria

Department of Geography, Umaru Musa Yaradua University, Katsina 820241, Nigeria

Corresponding Author Email:
22 July 2018
28 August 2018
30 September 2018
| Citation



It is very important to conserve the nature and composition of vegetation resources especially in an area were climate change and human activities are threatening their existence, especially Nigeria and in Particular River Kaduna Catchment Area might be one of those areas. Therefore this research aims to assess the vegetation cover status in river Kaduna catchment area. Remote sensing data and geographical information system were used to analyse the condition of vegetation in this region.  Landsat imageries from 2000-2014 were used to extract NDVI, SAVI and KC was computed based on the relationship with the NDVI, to assess the vigour condition of vegetation. Linear regression analysis was computed for all the indices to determine their trend. The results of the result revealed that all the indices exhibited similar pattern of vegetation variation. It also indicate that the built up area have high NDVI and SAVI value more that the water body. The Afaka forest reserve have the highest NDVI, SAVI and KC value. Result indicated that the Afaka forest reserve had a healthy vegetation even though it is being threated by human explosion. The trend analysis shows a negative trend of NDVI, SAVI and KC in Catchment Area respectively. It can be conclude that NDVI, SAVI and Kc showed a spatial and temporal variability in Middle Kaduna River Catchment area. The vegetation condition varied from one region to another This result shows a depletion of the vegetation as a consequence of human activities particularly fuel wood, cultivation for agricultural purposes and deforestation. It also has implication for global carbon dioxide loading and temperature.


vegetation, depletion, remote sensing, NDVI, KC, river Kaduna, regression

1. Introduction
2. Methodology
3. Results and Discussion
4. Conclusion

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