Monitoring Changes and Soil Characterization in Mangrove Forests of the United Arab Emirates Using the Canonical Correlation Forest Model by Multitemporal of Landsat Data - Elmahdy, Samy I.; Ali, Tarig A. (Frontiers in Remote Sensing, 2022)
Mangrove forests are an important indicator of blue carbon storage and biodiversity and provide several benefits to the environment. This study showed the first attempt to apply the canonical correlation forests (CCF) model to classify mangroves, monitor changes in the mangrove forests of the entire region. The CCF model obtained a satisfactory accuracy with an F1 score of more than 0.90. Compared to Sentinel-2, Landsat 8 exhibited good temporal resolution with relatively little mangrove details. The resultant mangroves maps (1990-2020) were used to monitor changes in mangrove forests applying a threshold value ranges from +1 to -1. The results showed a significant increase in the UAE mangroves over the period from 1990 to 2020. To characterize soil in mangrove forests, a set of interpolated maps for the calcium carbonate, salinity concentration, nitrogen and organic matter content was constructed. The results showed there is a positive relationship between mangroves distribution and the calcium carbonate, nitrogen, salinity and organic matter concentrations in the soil of the mangrove forests. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization.