Ecological Connectivity Index Mapping for Green Infrastructure Development in Kaduna State, Northern Nigeria

Infrastructural development is considered one of the key steps towards economic advancement and prosperity, although the ecological basis of such intent is often not enshrined in the policies of governments, especially in most developing nations. Thus, infrastructural development has further increased fragmentation of ecologically-rich landscapes, causing biodiversity losses, reduced specie richness and diversity and climate change. Hence, the need for green infrastructure development. This study made use of Geographic Information System (GIS) tool to explore areas with high Ecological Connectivity Index (ECI) that could (easily) support green infrastructure development in Kaduna State in the face of climate change impacts which is already ravaging the region. Kaduna State land use/cover map and Aster GDTM elevation data were acquired, processed and analyzed using ArcGIS 10.5 software. A spatial model was developed that made use of the combination of elevation, slope and land use/cover data of the State via Multi-Criteria Evaluation (MCE) to reveal potentially viable and ecologically-rich areas of the State and the most efficient links between such areas. A total distance of 878 kilometers of potential ecological corridors in the study area were mapped and therefore, serve as a base to be considered in embarking on a state-wide green wall infrastructure development program by Kaduna State government. It is however recommended that Kaduna State as well as other state governments, particularly in northern Nigeria, ought to roll-out phased green infrastructure investment program(s), adopt the use of shrubs and trees in building green walls and to localize all the gains of the continental-wide Green Wall initiative as this will further reduce the threats of climate change in the region. The study also recommended the inclusion of other landscape variables/descriptors (e.g. road proximity, soil properties, cultural setting of local communities, etc.) into building a more comprehensive model to strengthen the accuracy of the map and the model presented in this study.