Dr. Nadeem Fareed | Artificial Intelligence | Research Excellence Award

Dr. Nadeem Fareed | University of Florida | United States

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.

Citation Metrics (Scopus)

150

120

Citations
138

Documents
11

h-index
7

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Featured Publications

Interdisciplinary Applications of LiDAR in Forest Studies: Advances in Sensors, Methods, and Cross-Domain Metrics – Remote Sensing, 2026

Evaluating the Impact of Field-Measured Tree Height Errors Correction on Aboveground Biomass Modeling Using Airborne Laser Scanning and GEDI Datasets in Brazilian Amazonia – Trees, Forests and People, 2025

Aboveground Biomass Modeling Using Simulated GEDI Waveform LiDAR and Forest Inventories in Amazonian Rainforests – Forest Ecology and Management, 2025

UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data – Remote Sensing, 2024

Quality Control and Crop Characterization Framework Using UAS LiDAR Point Clouds over Agricultural Fields – ASABE Annual International Meeting, 2023

NASA ICESat-2: Space-Borne LiDAR for Geological Education and Field Mapping of Aeolian Sand Dune Environments – Remote Sensing, 2023

Dr. Suraj Yadav | Data-Driven Engineering | Research Excellence Award

Dr. Suraj Yadav | Mississippi State University | United States

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.

Citation Metrics (Scopus)

200

150

100

Citations
173

Documents
21

h-index
6