Deep Learning Assessment of Riparian Vegetation Loss and Stream Fish Diversity

Abstract

Riparian vegetation plays an essential role in maintaining freshwater ecosystem stability by regulating stream conditions, supporting aquatic habitats, and providing important ecological resources for fish communities. However, increasing land-use changes, agricultural expansion, urban development, and human disturbances have resulted in significant loss of riparian vegetation, leading to habitat degradation and reduced aquatic biodiversity. Stream fish diversity is highly influenced by vegetation-related factors such as habitat complexity, water quality regulation, food availability, and shelter conditions. Standard ecological evaluation techniques are predominantly based on field observation and manual observation techniques, which consume time and efforts for extensive environmental assessment activities. This research paper discusses an intelligent evaluation method using deep learning for understanding the impact of loss of riparian vegetation on the diversity of stream fish species in the context of ecological conservation. Various environmental observations, vegetation attributes, stream parameters, and fish diversity parameters have been considered for assessing the health of the aquatic environment. Such an evaluation method is helpful in determining the patterns of vegetation deterioration along with its impact on the variability of fish communities. It can be concluded that areas having less riparian vegetation will have lower quality of habitats and diversity of fishes, while healthy vegetation areas will have greater biodiversity of the aquatic communities