Forest land cover changes and its socio-economic consequences on south-eastern part of Bangladesh

Hill forest vegetation cover in the southeastern part of Bangladesh has been facing degradation and depletion over a few decades. The forest estimation and mapping of this country are well documented and mainly restricted to the mangrove extent. However, the monitoring of the hill forest vegetation of Bangladesh is limited till now. This study monitor and analyzes the forest vegetation cover changes using Landsat imagery from 1974 to 2020, specifically in Khagrachari and Rangamati hill district, Bangladesh. We preprocess the satellite imagery and then perform a decision tree classification algorithm based on the spectral indexes derived from the imagery. The initial assessment indicates the negative change of dense vegetation/forest vegetation cover (FVC) in most parts of the study area since 1974. Further results show that ~57.17%, ~39.3%, ~31.27%, and ~24.97% of the total area were classified as FVC type in 1989,1999, 2010 and 2020, respectively. Besides, this study briefly discusses how the change of FVC impacts the life of the local indigenous community living around the area. This preliminary investigation highlights deforestation over the ~46 years around the study area, which could be beneficial for planning to manage and conserve forest resources, and protect the local indigenous community

A rapid non-destructive hyperspectral imaging data model for the prediction of pungent constituents in dried ginger

Ginger is best known for its aromatic odour, spicy flavour and health-benefiting properties. Its flavour is derived primarily from two compound classes (gingerols and shogaols), with the overall quality of the product depending on the interaction between these compounds. Consequently, a robust method for determining the ratio of these compounds would be beneficial for quality control purposes. This study investigated the feasibility of using hyperspectral imaging to rapidly determine the ratio of 6-gingerol to 6-shogoal in dried ginger powder. Furthermore, the performance of several pre-processing methods and two multivariate models was explored. The best-performing models used partial least squares regression (PSLR) and least absolute shrinkage and selection operator (LASSO), using multiplicative scatter correction (MSC) and second derivative Savitzky–Golay (2D-SG) pre-processing. Using the full range of wavelengths (~400–1000 nm), the performance was similar for PLSR (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.92) and LASSO models (R2 ≥ 0.73, RMSE ≤ 0.29, and RPD ≥ 1.94). These results suggest that hyperspectral imaging combined with chemometric modelling may potentially be used as a rapid, non-destructive method for the prediction of gingerol-to-shogaol ratios in powdered ginger samples

Smart farming through responsible leadership in Bangladesh: Possibilities, opportunities, and beyond

Smart farming has the potential to overcome the challenge of 2050 to feed 10 billion people. Both artificial intelligence (AI) and the internet of things (IoT) have become critical prerequisites to smart farming due to their high interoperability, sensors, and cutting-edge technologies. Extending the role of responsible leadership, this paper proposes an AI and IoT based smart farming system in Bangladesh. With a comprehensive literature review, this paper counsels the need to go beyond the simple application of traditional farming and irrigation practices and recommends implementing smart farming enabling responsible leadership to uphold sustainable agriculture. It contributes to the current literature of smart farming in several ways. First, this paper helps to understand the prospect and challenges of both AI and IoT and the requirement of smart farming in a nonwestern context. Second, it clarifies the interventions of responsible leadership into Bangladesh’s agriculture sector and justifies the demand for sustainable smart farming. Third, this paper is a step forward to explore future empirical studies for the effective and efficient use of AI and IoT to adopt smart farming. Finally, this paper will help policymakers to take responsible initiatives to plan and apply smart farming in a developing economy like Bangladesh

Upper mantle viscosity underneath northern Marguerite Bay, Antarctic Peninsula constrained by bedrock uplift and ice mass variability

We constrain viscoelastic Earth rheology and recent ice-mass change in the northern Marguerite Bay region of the Antarctic Peninsula. Global Positioning System (GPS) time series from Rothera and San Martin stations show bedrock uplift range of ∼−0.8–1.8 mm/year over 1999–2005 and 2016–2020 but ∼3.5–6.0 mm/year over ∼2005–2016. Digital elevation models reveal substantial surface lowering, but at a lower rate since ∼2009. Using these data, we show that an elastic-only model cannot explain the non-linear uplift of the GPS sites but that a layered viscoelastic model can. We show close agreement between GPS uplift changes and viscoelastic models with effective elastic lithosphere thickness and upper-mantle viscosity ∼10–95 km and ∼0.1−9 × 1018 Pa s, respectively. Our viscosity estimate is consistent with a north-south gradient in viscosity suggested by previous studies focused on specific regions within the Antarctic Peninsula and adds further evidence of the low viscosity upper mantle in the northern Antarctic Peninsula