| Summary: | This research sought to simultaneously measure sedimentation and mangrove root growth rates at the Lasolo River estuary and construct a predictive sedimentation model from the measured rates. The applied methods were descriptive quantitative analyses using cloud-based computation to obtain sea surface temperatures, Landsat 8 and 9 OLI images to identify land cover change, field-derived data, and rain data. The neural network toolbox in MATLAB was used to perform the modeling. Results showed that sediments accumulated at the highest rate of 6.10 cm, and mangrove roots grew at an average of less than 1 cm per two weeks, meaning that the former always surpasses the latter. The forecasting model’s training and validation each produced a regression value (R) of 0.92 and 0.69 for sedimentation rates and R=0.83 and R=0.88 for root growth rates. Sedimentation rates continued to vary until the end of the observation, with a maximum of 3.91 cm. Although the root growth rates had a similar pattern (fluctuating), an upward trend with signs of acceleration to 0.62 cm was observed. This study also detected a conversion of vegetated land into open mining areas, which expanded, tentatively, from 645.8 ha in 2013 to 2,112.6 ha in 2022. © 2023 by ASERS Publishing. All rights reserved.
|
|---|