Bangladesh is an extremely climate-exposed country, with erosion, accretion, tidal surges, and cyclones continuously modifying coastal districts. Shoreline change in Bangladesh is crucial for sustainable coastal management and disaster resilience. Therefore, the objectives of this research are as follows: (i) to assess accretion- and erosion-based shoreline changes of the Bangladesh delta adjacent to the Bay of Bengal for 2021–2025 using a fixed 2021 reference shoreline and a 2025 shoreline proxy extracted from Landsat 8/9 imagery, and (ii) to explore onshore change dynamics from satellite-derived NDVI, NDBI, and NDWI for 2022–2025. The study covers 14 coastal districts and integrates the 2021 baseline shoreline, Survey of Bangladesh geospatial datasets, and 17,055 Ground Reference Points (GRPs) to support geometric consistency and spatially explicit reporting at the delta scale. Three spectral indices—Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI)—were applied to assess vegetation health, surface water distribution, and built-up/exposed land characteristics. Results indicate spatial variability in coastal change, with 383.49 km² of land gained through accretion and 124.12 km² lost to erosion, resulting in a neat accretion of 259.37 km² between 2021 and 2025; 8747.91 km² remained geomorphologically stable. Spectral index trends show minimal inter-annual NDVI and NDWI variability, suggesting stable vegetation cover and no long-term expansion of surface water. In contrast, a slight increase in NDBI indicates localized exposure of new sediments or small-scale land-use transitions along emerging coastal zones. Spearman correlation analysis highlights consistent negative relationships between NDVI and NDWI and moderate contrasts between NDVI and NDBI, reinforcing the coexistence of vegetation recovery, water withdrawal, and sediment-driven land emergence. The novelty of this study lies in the provision of consistent, near-real-time coastal change inventory for the full ~710 km Bangladesh delta coastline by combining a common 2021 baseline shoreline with harmonized Landsat 8/9 OLI surface reflectance (2022–2025) and linked onshore spectral-index dynamics over the same period. Overall, this short-term assessment reveals a sedimentary system that is active but balanced, with accretion surpassing erosion despite cyclone-affected disturbances, underscoring the value of operational satellite monitoring for coastal management, hazard preparedness, and climate-adaptive planning.

Sediment management is defined as the strategic monitoring and control of erosion, transport, and deposition processes to maintain environmental and infrastructural stability. Terrestrial laser scanning (TLS) has emerged as a critical high-precision technology for monitoring sediment dynamics, erosion processes, and geomorphic change detection across diverse environments, including riverine, coastal, watershed, and infrastructure-related landscapes. While the field of TLS technology has seen significant advancements in recent years, including improvements in data accuracy, enhanced operational performance, artificial intelligence (AI), machine learning-based processing, and integration with other remote sensing tools such as unmanned aerial vehicles (UAVs) and satellite light detection and ranging (LiDAR), the study has focused on these developments. These advancements have further extended the application prospects of TLS technology. Despite these advancements, there remains a crucial need to systematically identify global research trends to identify the effectiveness, limitations, and knowledge gaps of TLS in sediment management. The methodological advantages and challenges of TLS applications provide insights into its gradual development role in enhancing sediment monitoring and environmental resilience. The objective of this study is to synthesize the current state of sediment management by conducting a systematic review of 108 peer-reviewed research papers retrieved from academic databases, including Google Scholar, ResearchGate, ScienceDirect, Scopus, and Web of Science, from 28 countries, published between 2000 and 2025. The study will evaluate the effectiveness of TLS methodologies in comparison to conventional techniques and management procedures, following the PRISMA 2020 guidelines. It will examine their capacity to enhance measurement accuracy, reduce error margins, and improve structural guidelines, particularly by advancing TLS technology through the integration of AI and machine learning (ML) algorithms. The findings of the study indicate that TLS and Iterative Closest Point (ICP) techniques can enhance the analysis of 3D models of dam deformation, ensuring improved structural monitoring and safety. The findings offer insights into the evolving role of TLS in sediment monitoring, emphasizing its potential for enhancing environmental management and climate resilience strategies. Furthermore, this review identifies future research directions to optimize TLS applications in sediment management through interdisciplinary approaches.