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Dr. Nadeem Fareed Over the past two decades, Light Detection and Ranging (LiDAR) has evolved from early NASA-led airborne laser altimetry into a mature and widely adopted technology for vegetation remote sensing. Advances in laser engineering, signal processing, IMU/GNSS integration, and carrier platforms such as UAS, MLS, and SLAM systems have enabled multiscale observations of vegetation structure and dynamics. This review synthesizes developments in ALS discrete-return and full-waveform systems, photon-counting, multispectral, and hyperspectral LiDAR, alongside modern data fusion and AI-driven processing approaches. The body of work comprises 11 documents, receiving 138 citations from 135 publications, with an h-index of 7.
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Assoc. Prof. Dr. Wubin Shan is a researcher whose work focuses on energy-absorbing materials and structures, micro/nano 3D and 4D printing, nonlinear system dynamics, flexible electronics, and mechatronic equipment design with mechanical analysis. His research combines advanced manufacturing and multidisciplinary engineering approaches to solve complex technical challenges. He has led several competitive research projects and published 18 scholarly documents, achieving 411 citations from 407 citing documents with an h-index of 7. In addition, he holds numerous authorized invention patents and actively contributes to academic service and student mentorship, with teams receiving recognition in national and provincial engineering, innovation, and entrepreneurship competitions.
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Assist. Prof. Dr. Sameh Attia-Essaies is an Assistant Professor and researcher specializing in inorganic chemistry, composite materials, and clay mineral science. He holds a PhD in Inorganic Chemistry with excellent distinction and possesses strong expertise in structural, thermal, mechanical, and physicochemical characterization techniques, including X-ray diffraction, FT-IR spectroscopy, and thermal analysis. His research interests focus on mineral systems, composite materials, brines, phase diagram studies, and organic synthesis. His scholarly contributions include 10 scientific documents, receiving 100 citations across 90 citing documents, with an h-index of 5, demonstrating consistent research impact.
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Dr. Suraj Yadav is a researcher specializing in radar remote sensing, precision agriculture, and AI-driven geospatial analytics. His work integrates UAV-based multispectral, hyperspectral, LiDAR, and microwave sensing with physics-informed modeling and deep learning to enable robust crop monitoring, nitrogen estimation, and yield prediction. He has led the calibration and validation of advanced imaging spectroscopy platforms across diverse crop types and land surface conditions. His scholarly contributions include 21 research documents, receiving 173 citations across 141 citing publications, with an h-index of 6, reflecting sustained research impact.