The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVIII-M-3-2023
https://doi.org/10.5194/isprs-archives-XLVIII-M-3-2023-57-2023
https://doi.org/10.5194/isprs-archives-XLVIII-M-3-2023-57-2023
05 Sep 2023
 | 05 Sep 2023

MAXIMUM LIKELIHOOD ALGORITHM DETECTS COASTAL WETLAND CHANGES IN TWO CONTRASTING COASTAL WETLANDS IN LOUISIANA

T. H. Dauda, Z. Ning, Y. Twumasi, and O. Oladigbolu

Keywords: Remote sensing, GIS, Maximum likelihood algorithm, Land use land cover change, Barataria Bay, Wax Lake delta, coastal wetlands

Abstract. Louisiana coastal wetlands contain about 37 percent of the estuarine herbaceous marshes in the conterminous United States. However, the combined effect of sea level rise and other anthropogenic factors have altered land use land cover over the last few years. This is true for two wetlands in coastal Louisiana, Barataria bay and Wax Lake delta. Barataria Bay, Louisiana, USA has experienced significant land loss. Updated information on the dynamics of change in these wetlands is limited and poorly documented. This information is necessary to develop strategies that will contribute to reversing and halting degradation. Thus, this study employed the Maximum Likelihood classifier on Landsat satellite imagery to assess land use and land cover changes in Barataria Bay and Wax Lake Delta, southeastern Louisiana, USA. The analysis revealed notable alterations in the land cover patterns over the study period. In Barataria Bay, there was a decrease in salt marsh areas with a corresponding increase in open water and Built-up area. In contrast, Wax Lake Delta demonstrated substantial land/wetland growth, with significant expansion of vegetation cover. The Maximum Likelihood classifier demonstrated high accuracy in classifying the land cover types, with an overall accuracy of 86% for Barataria Bay and 92% for Wax Lake Delta. These results highlight the effectiveness of the classifier in accurately identifying and mapping land cover changes in coastal environments. The findings contribute valuable insights for understanding the dynamics of coastal ecosystems and can inform decision-making processes for coastal management and conservation efforts.