Detecting and prediting changes in mangrove forest in West and Central Africa using Landsat satellite data

Tropical mangrove are located in the tropical and subtropical regions. They connect lands and people with the sea, providing various ecological and socioeconomic services for humans. At the same time, mangrove in many parts of the world are declining at an alarming rate-possibly. Monitoring a spatiotemporal distribution of mangrove is thus critical for natural resources management of mangrove systems. Therefore, this research objective are: (i) to map the current extent of mangrove in West and Central Africa, (ii) identify mangrove change (gain and loss) from 1988 to 2014 using Landsat data, and (iii) to predict mangrove change in the future. The data were processed through five main steps: (1) data pre-processing including atmospheric corrections and image normalization; (2) image classification using supervised classification approach; (3) accuracy assessment; (4) change detection analysis; and (5) change prediction. The result shows that mangrove areas have changed significantly. In the West and Central Africa loss of mangrove from 1988 to 2014 was approximately 16.9%, only 2.5% was recovered or newly planted at the same time. Mangroves declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. For mangroves change projection, this research was projected changes until 2027 within the in-situ area that was selected by using Probabilistic Landscape Modelling and Simulation Tool with probabilistic simulation approach. Total area of mangrove forests increased a little bit comparing with classification results in 2001 and 2014. Mangrove area was remain unchanged or slightly decreased in the future. Mangrove prediction result effected by the input variables as well as the parameters used within the model. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove. Hence, the results achieved from this study could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in the region.