首页|Gansu Agricultural University Researcher Publishes New Study Findings on Machine Learning (A Comparative Analysis of Remote Sensing Estimation of Aboveground Bi omass in Boreal Forests Using Machine Learning Modeling and Environmental Data)
Gansu Agricultural University Researcher Publishes New Study Findings on Machine Learning (A Comparative Analysis of Remote Sensing Estimation of Aboveground Bi omass in Boreal Forests Using Machine Learning Modeling and Environmental Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Lanzhou, People’s Republic of China, by NewsRx correspondents, research state d, “It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change.” Financial supporters for this research include Nature Science Foundation of Gans u Province. Our news reporters obtained a quote from the research from Gansu Agricultural Un iversity: “Remote sensing as a cost-effective tool offers the potential to updat e AGB maps for boreal forests in real time. This study evaluates different machi ne learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extre me Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regressio n (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains , northwest China, the study integrated field measurements, space-borne LiDAR, o ptical remote sensing, and environmental data to develop a training dataset. Amo ng 34 variables, 22 were selected for AGB estimation modeling. Our findings reve aled that the LightGBM AGB model had the highest level of accuracy (R2 = 0.84, R MSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overe stimation.”
Gansu Agricultural UniversityLanzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learni ngRemote Sensing
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