Detection and Prediction of Water Body and Aquatic Plants Cover Changes of Choghakhor International Wetland, Using Landsat Imagery and the Cellular Automata–Markov Model

Zefrehei, Ahmad Reza Pirali; Aliakba, Hedayati; Pourmanafi, Saeid; Omid, Beyraghdar Kashkooli; Ghorbani, Rasoul

doi:10.1134/s1995425520050091

Cited by 3 publications

(1 citation statement)

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“…Other studies focused on the impacts of hurricanes on coastal wetlands: Carle et Sasser [103] observed a significant decrease in vegetation productivity in Wax Lake Delta (LA, USA), and Zhang et al [104] observed that mangroves took 2-6 years to reach their full extent in the Biscayne National Park (FL, USA). Sedimentation is recognized as a key factor in increasing the area of wetlands in floodplains [33,105]. In addition, many studies highlighted erosion and accretion in coastal wetlands, such as in mangroves in Vietnam [106], as well as in lagoons in Namibia [107], China [108], and Turkey [109].…”

Section: Natural Hazardsmentioning

confidence: 99%

Long-Term Wetland Monitoring Using the Landsat Archive: A Review

et al. 2023

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Wetlands, which provide multiple functions and ecosystem services, have decreased and been degraded worldwide for several decades due to human activities and climate change. Managers and scientists need tools to characterize and monitor wetland areas, structure, and functions in the long term and at regional and global scales and assess the effects of planning policies on their conservation status. The Landsat earth observation program has collected satellite images since 1972, which makes it the longest global earth observation record with respect to remote sensing. In this review, we describe how Landsat data have been used for long-term (≥20 years) wetland monitoring. A total of 351 articles were analyzed based on 5 topics and 22 attributes that address long-term wetland monitoring and Landsat data analysis issues. Results showed that (1) the open access Landsat archive successfully highlights changes in wetland areas, structure, and functions worldwide; (2) recent progress in artificial intelligence (AI) and machine learning opens new prospects for analyzing the Landsat archive; (3) most unexplored wetlands can be investigated using the Landsat archive; (4) new cloud-computing tools enable dense Landsat times-series to be processed over large areas. We recommend that future studies focus on changes in wetland functions using AI methods along with cloud computing. This review did not include reports and articles that do not mention the use of Landsat imagery.

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