Early detection of vegetation stress in Nairobi national park: structural change analysis from 2005 to 2025
Résumé
Nairobi National Park (NNP), a rare urban wildlife sanctuary bordering Kenya’s capital, is experiencing accelerating habitat destruction due to urban expansion, infrastructure development, and shifting land tenure. This study applies a two-decade satellite-based spatial analysis (2005–2025) to assess vegetation disturbance and ecological thresholds across the park. Using monthly MODIS NDVI data and the BFAST framework, the present study detected abrupt structural changes in vegetation dynamics that traditional linear based trend-based vegetation indices analysis methods failed to capture. The study further compares inflection points of commonly used vegetation indices such as kNDVI, NIRV, and LAI with breakpoint markers to show time lag before change signals are recorded when the indices are used alone. The seasonal-trend model used the split sample technique where part of the data was used for training, with break detection applied to the other end of the timeseries data. Results show that nearly one-third of the park’s vegetation pixels (about 30%) experienced sudden changes in condition between 2005 and 2025. The year 2020 marked the most active period, with 201 pixels showing abrupt shifts and the highest rate of vegetation greening at 76.1%. In contrast, 2018 and 2023 recorded the most intense vegetation decline, with browning rates of 92.7% and 97.2%, respectively. These fluctuations reflect alternating cycles of ecological stress and recovery, with annual change intensity ranging from −0.107 in 2014 (severe decline) to +0.047 in 2016 (moderate recovery). The BFAST method consistently detected short-term vegetation shocks that were missed by standard statistical tools like Mann-Kendall tests and linear regression. In many cases, BFAST identified structural breakpoints up to 3 weeks before visible changes appeared in conventional vegetation indices. This early detection capability highlights BFAST’s value as a diagnostic tool for monitoring rapid ecological shifts and informing early action conservation responses.
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