Degradation of Urban Green Spaces in Lagos, Nigeria: Evidence from Satellite and Demographic Data
Advances in Remote Sensing
The study aimed to assess the potential of using Remote Sensing (RS) data to evaluate the changes of urban green spaces in Lagos, Nigeria. Landsat Thematic Mapper and Landsat 8 (Operational Land Imager) data pair of May 4, 1986, December 12, 2002 and January 1, 2019 covering Lagos Government Authority (LGA) were used for this study. Supervised image classification technique using Maximum Likelihood Classifier (MLC) was used to create base map which was then used for ground truthing. Random Forest (RF) classification technique using RF classifier was utilized in this study to generate the final land use land cover map. RF is an ensemble learning method for classification that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification). Lagos census population data was also used in this study to model population projection. Extrapolation of the model was used to predict data for the years, 2020 and 2040. Results of the study revealed a reduction of urban green spaces due to agriculture and settlement. While the remote mapping revealed the gradual dispersion of ecosystem degradation indicators spread across the state, there exists clusters of areas vulnerable to environmental hazards across Lagos. To mitigate these risks, the paper offered recommendations ranging from the need for effective policy to green planning education for city managers, developers and risk assessment. These measures will go a long way in helping sustainability and management of land resources in Lagos.
Twumasi, Yaw A.; Merem, Edmund C.; Namwamba, John B.; and Mwakimi, Olipa S., "Degradation of Urban Green Spaces in Lagos, Nigeria: Evidence from Satellite and Demographic Data" (2020). Urban Forestry. 12.
Scientific Research Publishing