Spatiotemporal analysis of mangroves using median composites and convolutional neural network.

ABSTRACT: Mangroves play an important ecological role, but these are commonly misunderstood and undervalued. Climatic change and increase in sea level cause risk to these ecosystems. Hence, tracking the extent of mangroves leads to proper management and restoring the damaged ones. Existing index methods for mapping the mangrove extent often struggle with cloud cover, while the studies that feed direct image inputs to models face spatial resolution misalignment, limiting accurate analysis. To overcome these challenges this paper proposes a novel cloud-masked feature extraction (CMFE) approach, integrating bit masking, median compositing, and multi-band spectral sampling for precise mangrove analysis. Our approach utilizes multispectral imagery from United States Geological Survey (USGS) for Landsat 8

SUBMITTER: R K 

PROVIDER: S-EPMC12289926 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature