Search

Mangrove forests are crucial for carbon sequestration and biodiversity conservation but are threatened by anthropogenic effects and climate change. Although restoration efforts have been initiated, their effectiveness remains uncertain due to the absence of robust monitoring and evaluation mechanisms. This study investigates machine learning algorithms for modelling aboveground biomass (AGB) in mangrove forests across Peninsular Malaysia. Data on tree diameter at breast height (DBH) and species were collected in Sungai Pulai, Sungai Johor, and Sungai Merbok. Combined with remote sensing data, the performance of Extreme Gradient Boosting (XGBoost), Random Forest (RF), Boosted Tree (BT), and Support Vector Machine (SVM) was compared, chosen for their ability to analyze complex patterns and predict accurately. The results indicated that XGBoost outperformed the others by achieving higher R² values of 0.97, lower mean absolute error (MAE) of 6.80 Mg ha-¹, and root mean squared error (RMSE) of 26.74 Mg ha-¹, demonstrating superior accuracy and predictive performance. This study also shows similar above-ground carbon (AGC) values across the study areas and in comparison with previous studies. XGBoost’s robust capacity for estimating AGB and AGC highlights its potential to significantly improve mangrove forest management and conservation efforts on a broader scale.

Solar power plays a key role in sustainable energy development. However, tropical climates pose challenges such as high temperatures and humidity, requiring a deep understanding of PV system defects. This study uses unmanned aerial vehicle thermographic inspection techniques to assess defects in solar farms in Marang, Terengganu, aiming to improve solar energy reliability in tropical environments. Results show that Bypass Diode issues are the most common defect, making up 75.70% of occurrences, indicating a critical area for remediation. Clusters of Thermal Anomalies (CTA) defects follow at 13.20%, necessitating detailed examination of abnormal thermal patterns. Faulty Interconnections are less frequent at 1.90%, highlighting the need for proper electrical connections. Hot spots and Overheating Component defects account for 8.70% and 0.40% respectively, pointing to localized overheating and potential malfunctions. The study also examines the correlation between solar irradiance patterns and PV defects, with irradiance levels ranging from 700.6 W/m² to 1073.1 W/m², showing how sunlight intensity fluctuations impact PV system performance. This analysis helps prioritize resources and develop targeted maintenance strategies, enhancing the reliability and performance of solar energy systems in tropical climates. The findings offer practical insights for advancing renewable energy infrastructure in tropical regions, promoting the sustainable adoption of solar energy worldwide.

Blue carbon habitats, including mangrove forests, salt marshes, and seagrass beds, play a crucial role in sequestering organic carbon, mitigating global warming, and protecting coastlines and local livelihoods. Traditional monitoring methods for these ecosystems are limited and expensive, leading to the adoption of remote sensing, particularly via unmanned aerial vehicles (UAVs). UAVs enhance monitoring efforts through high-resolution data collection, bolstering conservation and management strategies. This paper explores the utility of UAVs for monitoring mangrove-based blue carbon ecosystems, emphasizing their role in addressing emerging challenges. The review highlights the efficacy of UAVs in guiding conservation and restoration initiatives globally, especially in nations rich in mangrove ecosystems. Integration of artificial intelligence and machine learning further enhances UAV capabilities, facilitating data analysis and interdisciplinary collaboration. Advancements in UAV technology promise to revolutionize surveillance of mangrove-based blue carbon ecosystems, ensuring the protection of these vital habitats and their contribution to climate change mitigation. [ABSTRACT FROM AUTHOR]