| dc.description.abstract | This study assessed mangrove forest cover change and their associated drivers on the Kenyan
coast. Geographic Information Systems and Remote Sensing including Machine Learning
techniques were used to map the extent and change of mangrove forests for the years 2001, 2011
and 2021 in Mombasa, Tana River and Lamu Counties of Kenya. Binary Logistic Regression was
applied together with the results of mangrove forest dynamics to model the drivers of mangrove
dynamics in the three respective counties. The results showed that there was a significant
difference in mangrove forest coverage in the three respective counties (P < 0.05). Mombasa
County had the least average mangrove coverage (1,138 ha) and the highest mangrove loss (65%)
which was attributed to the influence of anthropogenic activities. Lamu County had the highest
mean mangrove coverage (32,119 ha) in addition to being more stable in terms of mangrove
dynamics whereas Tana River County exhibited a tremendous gain in mangrove coverage (28%)
from 2001 to 2021. The drivers of mangrove dynamics in Kenya included population density,
coastal developments, proximity to protected areas, proximity to major roads and surface
temperature although these factors varied from one county to the other. It is therefore
recommended that mangrove conservation and protection efforts be tailor-made to specific areas
as the drivers of the dynamics are site-specific for example in Mombasa County where
anthropogenic factors are responsible for mangrove loss, the community should be involved in the
conservation and restoration of mangroves, whereas in Tana River County, more research should
be carried out to identify how the erosion of riverbanks can be addressed to reduce mortality of
mangroves. | en_US |
