Journal of Environmental & Earth Sciences

Prediction and Modelling of Land Use Change in Pesawaran District Lampung Using ANN and Cellular Automata

Irma lusi Nugraheni, Mustofa Usman, Sutarto Sutarto — Tue, 27 May 2025 00:00:00 +0800

The simultaneous increase in development in Pesawaran Regency is closely correlated with the intense competition for land use. However, low policy implementation effectiveness has led to construction beyond designated spatial plan. The study used a quantitative survey using Landsat images in 2016, 2019, and 2022. The data analysis techniques used geographic information systems integrated with Artificial Neural Network (ANN) and Cellular Automata (CA) models. This study aims to predict land-use change in 2031, evaluate its alignment with spatial planning, and provide guidance for controlling land-use change. The results showed that there has been an increase in land use. In 2019, built-up land reached 7,069.65 Ha. The model shows its ability to predict land simulation and transformation, where it is predicted that built-up land in 2031 will experience an increase of up to 40.10%, so development and change cannot be avoided every year. This study also suggests that decision-makers and local governments should reconsider spatial planning strategies. This study shows that there have been many land use changes from 2016 to 2022. The model shows its ability to predict simulation and land transformation. When using the model, there are many changes in the land use area in 2031. This is due to wet agricultural land turning into built-up land by almost 70%. This study shows that road network influence land-use change. The cellular automata model managed to capture the complexity with simple rules. Predictions for future research should focus on conserving wetlands and primary forests.

SIF: Satellite Image Fusion for Deforestation Analysis in the Amazon Using S-1 and S-2 Data for LULC Applications

Priyanka Darbari, Ankush Agarwal , Manoj Kumar — Mon, 26 May 2025 00:00:00 +0800

Deforestation is the purpose of converting forest into land and reforestation compared to deforestation is very low. That’s why closely and accurately deforestation monitoring using Sentinel-1 and Sentinel-2 satellite images for better vision is required. This paper proposes an effective image fusion technique that combines S-1/2 data to improve the deforested areas. Based on review, Optical and SAR image fusion produces high-resolution images for better deforestation monitoring. To enhance the S-1/2 images, preprocessing is needed as per requirements and then, collocation between the two different types of images to mitigate the image registration problem, and after that, apply an image fusion machine learning approach, PCA-Wavelet. As per analysis, PCA helps to maintain spatial resolution, and Wavelet helps to preserve spectral resolution, gives better-fused images compared to other techniques. As per results, 2019 S-2 preprocessed collocated image enhances 42.2508 km² deforested area, S-1 preprocessed collocated image enhances 23.7918 km² deforested area, and after fusion of the 2019 S-1/2 images, it enhances 16.5335 km²deforested area. Similarly, the 2023 S-2 preprocessed collocated image enhances 49.2216 km²deforested area, S-1 preprocessed collocated image enhances 23.8459 km²deforested area after fusion of the 2023 S-1/2 images, enhancing 35.9185 km²deforested area. These improvements show that combining data sources gives a clearer and more reliable picture of forest loss over time. The overall paper objective is to apply effective techniques for image fusion of Brazil's Amazon Forest and analyze the difference between collocated image pixels and fused image pixels for accurate analysis of deforested area.

High-Performance Supercapacitor Electrodes from Optimized Single-Step Carbonized Michelia Champaca Biomass

Dibyashree Shrestha — Fri, 16 May 2025 00:00:00 +0800

This study explores the potential of Michelia champaca wood as a sustainable and locally available precursor for the fabrication of high-performance supercapacitor electrodes. Activated carbons were synthesized through single-step carbonization at 400 °C and 500 °C (SSC-400 °C and SSC-500 °C) and double-step carbonization at 400 °C (DSC-400 °C), with all samples activated using H₃PO₄. The effects of carbonization stratergy on the structural, morphological, and electrochemical characteristics of the resulting carbon materials were systematically evaluated, using techniques such as BET, SEM, TEM, XRD, Raman scattering, FTIR, CV, GCD and EIS. Among the samples, SSC-400 °C exhibited the best electrochemical performance, achieving a specific capacitance of 292.2 Fg⁻¹, an energy density of 6.4 Wh kg⁻¹, and a power density of 198.4 W kg⁻¹. This superior performance is attributed to its optimized pore structure, improved surface functionality and enhanced conductivity. SSC-500 °C showed marginally lower performance, whereas, DSC-400 °C displayed the least favorable results, indicating that double-step carbonization process may negatively affect material quality by disrupting the pore network. This work highlights a strong correlation between synthesis methodology and electrochemical efficiency, directly reinforcing the importance of process optimization in electrode material development. The findings contribute to the broader goal of developing cost-effective, renewable and environmentally friendly energy storage systems. By valorizing biomass waste, the study supports global movements toward green energy technologies and circular carbon economies, offering a viable pathway for sustainable supercapacitor development and practical applications in energy storage devices.

Characterization and Management of Sewage Sludge in Abomey Calavi: Pathways to Sustainable Treatment Solutions

Wed, 28 May 2025 00:00:00 +0800

In the Republic of Benin, as in many other West African countries, urban areas have experienced rapid population growth in recent years. This situation has led to an increasing demand for sanitation facilities, necessitating regular emptying of these systems. In a bid to reduce health risks and protect the surrounding natural environment, the management of the by-products from these systems has become a significant concern for decision-makers at various levels. This study aims to characterize fecal sludge at the Abomey-Calavi treatment station and suggest a mixed biological treatment approach. Fifteen sewage sludge samples were collected in 1,500 ml plastic bottles from Adjagbo's Sewage Treatment Station, operated by SGDS-SA, a Waste Management and Sanitation company. Physico-chemical parameters were determined using spectrophotometric analysis. Colonies were enumerated using membrane filtration and inoculation. Correlation analysis was performed on sewage sludge samples. The main results indicate an alkaline character (pH > 7) and a high organic pollutant load in the fecal sludge, with average concentrations of Chemical Oxygen Demand (COD) and Biochemical Oxygen Demand over 5 days (BOD₅) at 18,730 mg O₂.L⁻¹ and 6,612 mg O₂.L⁻¹, respectively. The COD/BOD₅ ratio of 2.83 suggests that the material is partially biodegradable. Furthermore, the nutrients exhibited high concentrations of nitrates, with an average value of 4,786 mg.L⁻¹, while nitrites, ammoniacal nitrogen, and orthophosphates had average concentrations of 22.48 mg.L⁻¹, 119.74 mg.L⁻¹, and 239.0 mg.L⁻¹, respectively. This study characterized fecal sludge at the Abomey-Calavi treatment station and suggests a mixed biological treatment approach.