Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions.

Mangrove forests provide essential climate regulation and coastal protection, yet fine‐scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating high-resolution remote sensing with a light use efficiency modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data. Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation indices and MODIS fPAR with temperature and water availability constraints. Solar-induced chlorophyll fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI observations to assess correspondence with photosynthetic activity. LAI and GPP exhibited pronounced seasonal and interannual variability, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling of canopy structure and function. These results provide a fine-scale assessment of mangrove carbon dynamics relevant for conservation and climate mitigation planning.

The tension test of rebar is vital for Reinforced Cement Concrete (RCC) structural analysis. Traditionally, deformation is measured with human intervention using an extensometer and an elongation scale, while a Universal Testing Machine (UTM) applies load to the test specimen. To enhance the experiment, digital UTM with built-in software to generate stress-strain diagrams is used worldwide, which in turn requires a more financial and energy-hungry effort. This research aims to develop a MATLAB application to generate stress-strain diagram by processing smartphone-recorded experimental video at a low cost. The developed application can be considered a sustainable alternative with efficient energy consumption, complying with the 12^th Sustainable Development Goal (SDG) – Responsible Consumption and Production. To achieve the objective, digital image processing-based algorithm is utilized for the detection of color-marked endpoints of rebar gauge length obtaining instantaneous strain vs time data without using the costly extensometer. It is combined with the load vs time data obtained leveraging Optical Character Recognition (OCR) in MATLAB to plot engineering stress-strain diagram. Key techniques include RGB thresholding and Kanade-Lucas-Tomasi (KLT) point tracking. The research shows that the computer-vision-based application can generate stress-strain diagram with required accuracy of 70% to 95% at a low cost. Despite integrating computer vision in structural health monitoring and geotechnical experiments, a specific algorithm for rebar tension testing has gained little attention. This research may potentially fill this gap with a further scope of enhancement using Convolutional Neural Network (CNN)-based point tracking methods for future researchers.