Impacts of meander migration on the Amazon riverine communities using Landsat time series and cloud computing


River meander migration is a process that maintains biodiverse riparian ecosystems by producing highly sinuous rivers, and oxbow lakes. However, although the floodplains support communities with fish and other practices in the region, meandering rivers can directly affect the life of local communities. For example, erosion of river banks promotes the loss of land on community shores, while sedimentation increases the distance from house to the river. Therefore, communities living along the Juruá River, one of the most sinuous rivers on Earth, are vulnerable to long-term meander migration. In this study, the river meander migration was detected by using Landsat 5-8 data from 1984 to 2020. A per-pixel Water Surface Change Detection Algorithm (WSCDA) was developed to classify regions subject to erosion and sedimentation processes by applying temporal regressions on the water index, called Modified Normalized Difference Water Index (mNDWI). The WSCDA classified the meander migration with omission and commission errors lower than 13.44% and 7.08%, respectively. Then, the number of riparian communities was mapped using high spatial resolution SPOT images. A total of 369 communities with no road access were identified, the majority of which living in stable regions (58.8%), followed by sedimentation (26.02%) and erosion (15.18%) areas. Furthermore, we identified that larger communities (>20 houses) tend to live in more stable locations (70%) compared to smaller communities (1-10 houses) with 55.6%. A theoretical model was proposed to illustrate the main impacts of meander migration on the communities, related to Inundation, Mobility Change, and Food Security. This is the first study exploring the relationship between meander migration and riverine communities at watershed-level, and the results support the identification of vulnerable communities to improve local planning and floodplain conservation.

International Journal of Remote Sensing