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Research of Scientia Naturalis
Published by Yayasan Adra Karima Hubbi
ISSN : 30479932     EISSN : 30479940     DOI : 10.70177/scientia
Core Subject : Science, Agriculture,
Agriculture, Biological Sciences & Forestry Chemistry Physics
Research of Scientia Naturalis is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the research fields of Mathematics and Natural Sciences. All publications provide breadth of coverage appropriate to a wide readership in Mathematics and Natural Sciences research depth to inform specialists in that area. We feel that the rapidly growing Research of Scientia Naturalis community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Umum - Umum
Articles 2 Documents
 1 
Search results for , issue "vol. 3 no. 4 (2026)" : 2 Documents clear
MAPPING THE EFFICACY OF BIO-BASED CARBON CAPTURE TECHNOLOGIES IN MITIGATING GREENHOUSE GAS EMISSIONS FOR INDUSTRIAL SUSTAINABILITY Achmad Agus Salim; Muhammad Taswin; Muhammad Yassir; Daiki Nishida
Research of Scientia Naturalis Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i4.4324

Abstract

Industrial decarbonization requires carbon-management technologies that can reduce residual emissions without creating excessive energy, resource, or land burdens. This study aimed to map and compare the efficacy of bio-based carbon capture technologies for mitigating greenhouse gas emissions and supporting industrial sustainability. A systematic review, evidence mapping, and quantitative synthesis were conducted on 84 studies published between 2010 and 2026, covering microalgae, microbial fixation, carbonic-anhydrase-assisted capture, biomass-derived sorbents, biochar, and bioenergy with carbon capture and storage. Data were evaluated across technical performance, environmental effectiveness, economic feasibility, industrial scalability, and storage permanence. The findings showed that carbonic-anhydrase-assisted systems and bioenergy with carbon capture and storage achieved the strongest gross capture performance, while biochar and bioenergy with carbon capture and storage provided greater long-term storage potential. Microalgae and microbial systems offered valuable co-benefits through wastewater treatment, nutrient recovery, and biomass production, although energy-intensive cultivation and downstream processing reduced their net climate benefits. The study concludes that capture efficiency alone is insufficient for evaluating technological efficacy. Credible industrial deployment requires life-cycle greenhouse gas assessment, durable carbon storage, sustainable feedstock sourcing, realistic scale-up evidence, and integration with broader decarbonization strategies across diverse industrial sectors and operating conditions under transparent monitoring, reporting, and verification frameworks worldwide.
DEEP LEARNING ARCHITECTURES FOR AUTOMATED CLASSIFICATION OF SATELLITE IMAGERY TO MONITOR GLOBAL DEFORESTATION TRENDS AND LAND USE CHANGES Sa’dianoor Sa’dianoor; Chen Mei; Yang Xiang
Research of Scientia Naturalis Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i4.4327

Abstract

Global deforestation and land-use change require timely, accurate, and transferable monitoring systems capable of processing extensive satellite archives. This study aimed to evaluate deep learning architectures for automated classification of satellite imagery, detection of forest change, and identification of post-deforestation land uses across diverse ecological regions. A comparative experimental design was applied to 120,000 labelled image patches derived from Landsat, Sentinel-1, and Sentinel-2 data representing South America, Southeast Asia, Central Africa, North America, Europe, and Oceania. Nine architectures, including convolutional, recurrent, Siamese, Transformer-based, and hybrid models, were assessed using spatial, temporal, and cross-regional validation. The hybrid CNN–Transformer achieved the strongest overall accuracy, calibration, and transfer performance, while radar–optical fusion improved classification in persistently cloudy tropical environments. Multitemporal models reduced confusion between permanent deforestation, seasonal disturbance, harvesting, and regeneration. Geographic holdout testing produced substantial performance declines across all architectures, demonstrating that random partitioning overestimated operational reliability. The study concludes that effective global deforestation monitoring requires multimodal data, temporal context, uncertainty estimation, active learning, and geographically independent evaluation. Automated classification can strengthen forest surveillance, yet expert verification and statistically adjusted area estimation remain essential for responsible environmental decision-making and policy implementation under rapidly changing climatic, technological, and land-management conditions worldwide over time.

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