Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R 2 = 0.977, p < 0.001) and OLI (R 2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803˘0.47 kg in 2009, while it was 752.3˘0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB.

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The comfort and productivity of workers may be affected differently by the indoor air quality (IAQ) and related risk factors at the office and at home. Sick Building Syndrome (SBS) is one of the health issues usually faced by workers. SBS is generally associated with the time spent in a building, IAQ, and other related risk factors. The study reviewed papers published in journal articles and conferences regarding IAQ, environmental risk factors and SBS in the last ten years. The review employed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 items from two significant databases, Scopus and Web of Science. The review steps involved identification, screening, eligibility, data extraction and analysis. The study found that air quality in a building significantly influences work productivity and may contribute to SBS. Findings show that SBS symptoms are linked to various personal characteristics, sociodemographic, working environment and IAQ factors. The physical contaminants, chemical contaminants and ventilation rate have established relations with SBS symptoms. These findings can help to form interventions aiming to improve IAQ and the productivity of occupants.