Periodical publishing
Current research from the National Institute of Forestry, Mexico, has shown that machine learning techniques can significantly improve the accuracy of aboveground biomass estimation in temperate forests. The study modelled AGB in central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost)
Periodical publishing
Researchers at the Graduate University of Advanced Technology have developed an innovative approach to monitor water quality and detect dead zones using satellite imagery and machine learning algorithms. The study employed seven AI models to estimate dissolved oxygen levels in Long Island Sound, New York, and found that the Extreme
Machine learning
Research conducted by the Chinese Academy of Sciences in Changchun, People's Republic of China, has highlighted the critical need for improved water quality monitoring and prediction as the world grapples with the intensifying challenges of climate change and human activities. The study emphasizes the importance of integrating remote
Machine learning
Coffee demand continues to rise globally, and producing countries face challenges due to climate change, resulting in yield losses. Researchers from the State University of Campinas (UNICAMP) have developed a novel approach to identify coffee cultivation using machine learning. This innovative method proposes to identify coffee cultivation considering four phenological
Machine learning
A recent study published in the Journal of Hydrology and Hydromechanics has revealed that land-use change is a significant driver of environmental degradation and increasing drought risk. The research, conducted by investigators from Slovak University of Technology, aimed to assess drought dynamics in the South Malang Plateau, East Java, by
Machine learning
Researchers at the University of Groningen have developed a novel recursive Bayesian estimation framework using B-splines for continuous-time 6-DoF dynamic motion estimation. This framework, known as RESPLE, improves upon existing systems by achieving comparable or superior estimation accuracy and robustness while attaining real-time efficiency. The study presents extensive real-world evaluations
Machine learning
Researchers from Yili Normal University in Xinjiang, China, have employed a novel methodological framework integrating morphological spatial pattern analysis, circuit theory, and machine learning models to investigate the spatiotemporal evolution and optimization of ecological networks in arid areas. The study, published in Environmental and Sustainability Indicators, reveals significant improvements in
Machine learning
Punjab, India's agricultural sector is a crucial aspect of the country's economy, with a significant portion of the population dependent on it. To ensure sustainable development and effective management of agricultural lands, researchers have turned to remote sensing and machine learning techniques to classify and monitor
Machine learning
Researchers from the Chinese Academy of Sciences have utilized artificial intelligence to improve the interpretation of ecological drivers in grassland ecosystems in arid regions. A new study combines machine learning with temporal autocorrelation metrics to assess resilience in these ecosystems. Key Takeaways: * The study employed Random Forest and SHAP analysis
Machine learning
Researchers at Hong Kong Polytechnic University have proposed an innovative framework for estimating tree above-ground biomass, a crucial step towards achieving carbon neutrality. The existing global allometric models face challenges in accurately modeling local trees' biomass. The new framework involves collecting local tree felling data, implementing Light Detection and
Economic development
Pakistan's second remote sensing satellite, PRSS-2, has become operational following its successful deployment in space after being launched by China's Xichang Satellite Launch Centre last month. The satellite has established stable contact with ground stations and has begun capturing and transmitting high-resolution imagery, greatly enhancing data
Economic development
The Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) has confirmed the successful deployment and operational readiness of the country's latest Remote Sensing Satellite, launched from Xichang Satellite Launch Center in China on 31 July. This satellite has established stable contact with ground stations and begun capturing and
