首页|Researchers from Wuhan University Describe Findings in Machine Learning (Spatio- temporal Dynamics of Rangeland Transformation Using Machine Learning Algorithms and Remote Sensing Data)
Researchers from Wuhan University Describe Findings in Machine Learning (Spatio- temporal Dynamics of Rangeland Transformation Using Machine Learning Algorithms and Remote Sensing Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting out of Wuhan, People’s Republic of Chin a, by NewsRx editors, research stated, “Rangelands globally face escalating thre ats from overgrazing, land conversion, and climate change. This study investigat es spatiotemporal rangeland degradation patterns in Pakistan’s Bhakkar District , a semiarid region dependent on fragile pastoral ecosystems, over the past four decades at 10-yr intervals (1990, 20 0 0, 2010, 2020).” Financial support for this research came from King Saud University. Our news journalists obtained a quote from the research from Wuhan University, “ Remote sensing offers a valuable tool for monitoring these vast yet understudied dryland environments. We employed Landsat satellite data and machine learning a lgorithms to map land cover change and analyze vegetation health indicators. The random forest classifier achieved high accuracy (94%) in delineati ng six land cover categories-water, built-up, forest, cropland, rangeland, and b arren land. Classified rangeland area declined by over 25%, largely due to agricultural expansion. Vegetation indices showed mixed trends, with dec reases in enhanced vegetation index but marginal improvement in normalized diffe rence vegetation index. Meanwhile, rising land surface temperatures pointed to i ncreased aridity. These concerning changes underscore the urgent need for conser vation policies tailored to community needs through participatory engagement. Ra ngeland degradation threatens the livelihoods and welfare of pastoral communitie s reliant on these ecosystems. Integrated solutions centered on adaptation and r esilience can promote sustainability in Bhakkar’s marginal dryland environments. ”
WuhanPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningRemote SensingW uhan University
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