Anubhava Srivastava | Ecosystem | Best Researcher Award

Dr. Anubhava Srivastava | Ecosystem | Best Researcher Award

Assistant Professor at Sharda University, India

Dr. Anubhava Srivastava is an accomplished academic and researcher in Computer Science, currently serving as Assistant Professor at Sharda University. With a Ph.D. from RGIPT, Jais, his research bridges artificial intelligence, machine learning, geoinformatics, and remote sensing. His innovative doctoral thesis focused on LU/LC mapping and classification using AI/ML on Google Earth Engine. Dr. Srivastava’s academic journey reflects a strong commitment to interdisciplinary applications of data science, from precision agriculture to environmental monitoring. He has published extensively in SCI/Scopus journals and international conferences, authored book chapters with Springer and Elsevier, and holds patents related to IoT applications. A GATE qualifier, he has also completed numerous professional certifications through Coursera. With an H-index of 9 (Google Scholar), his work is well-cited and widely recognized. Passionate about sustainability and digital innovation, Dr. Srivastava’s contributions make him a leading voice in AI-driven geospatial solutions for real-world problems.

Professional Profiles📖

Scopus 

ORCID 

Google Scholar

🎓 Education 

Dr. Srivastava holds a Ph.D. in Computer Science from Rajiv Gandhi Institute of Petroleum Technology (RGIPT), Jais, completed in 2023 with a CPI of 8.67. His doctoral thesis focused on LU/LC classification using AI/ML techniques and Google Earth Engine. He earned his M.Tech in Computer Science and Engineering from Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, in 2015,  Prior to that, he completed his B.Tech in Computer Science in 2012 with 71.84%. His early education includes a Higher Secondary Certificate (57.60%) and a Secondary Certificate (71.32%) from GIC Sultanpur. His academic training spans critical subjects such as database systems, machine learning, artificial intelligence, and remote sensing algorithms. Dr. Srivastava’s educational background is a blend of foundational computer science and advanced AI-geoinformatics, equipping him to tackle complex, data-driven challenges in environmental science, spatial computing, and smart systems.

🏗 Experience 

Dr. Srivastava has over 9 years of academic experience. He currently serves as an Assistant Professor at Sharda University, Greater Noida (since May 2023), in the Department of Computer Science. Prior to that, he was with Noida Institute of Engineering and Technology (2022–2023) and Rajarshi Rananjay Sinh Institute of Management & Technology, Amethi (2015–2022). His responsibilities have included mentoring postgraduate and undergraduate students, supervising research projects, and teaching cutting-edge courses in AI, machine learning, remote sensing, and databases. Dr. Srivastava has also guided students in developing projects using Google Earth Engine, satellite data, and real-time geospatial analytics. His teaching philosophy emphasizes practical application, data literacy, and sustainability. His contributions extend beyond academics through research collaborations, IEEE conference participation, and involvement in AI-based solutions for socio-economic development and smart agriculture. His interdisciplinary experience bridges academia, technology, and field-based environmental intelligence.

🏆 Awards & Honors 

Dr. Srivastava has made significant strides in both academia and applied research. His design patents—on RFID-based animal tracking and irrigation water supply management—highlight his innovation in IoT for environmental and agricultural applications. He has earned repeated recognition through his publications in high-impact SCI and Scopus journals such as Science of the Total Environment and Applied System Innovation. With an H-index of 9 on Google Scholar and 8 on Scopus, his scholarly work on AI, remote sensing, and GIS is widely cited. He has qualified the prestigious GATE exam thrice (2013, 2016, 2017). Dr. Srivastava’s work has been presented in multiple IEEE and Springer conferences, showcasing his leadership in AI-powered geospatial research. He also contributed chapters to international volumes with Elsevier, CRC Press, and Springer. His recognition as a thought leader in environmental computation, digital literacy, and rural innovation places him among promising young researchers in India.

🔬 Research Focus

Dr. Anubhava Srivastava’s research lies at the intersection of Artificial Intelligence, remote sensing, environmental monitoring, and geospatial data analytics. His Ph.D. work involved LU/LC classification and environmental change detection using AI/ML techniques over Google Earth Engine. He focuses on developing intelligent systems that analyze temporal and spatial patterns of land, forest, and urban landscapes. His recent publications address critical challenges such as forest degradation, fire detection, and digital literacy through geoinformatics. His research integrates NDVI, vegetation indices, satellite image classification, and climate change modeling using Sentinel-2 and Landsat datasets. Beyond environmental domains, his contributions also include cybersecurity in P2P networks, real-time earthquake monitoring, and structural health analysis using 3D point clouds. Through interdisciplinary collaboration, he aims to support SDGs by designing AI-based frameworks that enable informed policymaking, sustainable agriculture, and urban resilience. His long-term goal is to expand geospatial intelligence applications through ethical and scalable AI innovations.

🛠 Skills 

Dr. Srivastava brings a comprehensive skillset in artificial intelligence, machine learning, deep learning, and remote sensing. He is proficient in using Google Earth Engine, Python, MATLAB, and GIS tools for environmental data modeling and visualization. His technical expertise includes supervised and unsupervised classification algorithms, NDVI-based analysis, satellite data processing, API integration, and cloud-native development. He is also adept in computer networks, particularly P2P security protocols, demonstrated in his M.Tech thesis and conference papers. He has authored secure overlays like Heal Gossip and FCCC for environmental and communication systems. Dr. Srivastava’s coursework and certifications from Coursera further validate his mastery in web development, AI, GIS mapping, and cloud computing. His interpersonal and academic skills include research mentorship, technical writing, and effective presentation in reputed conferences. He actively collaborates across disciplines, enhancing his role as a researcher, mentor, and digital innovation advocate.

Publications Top Notes

Review of structural health monitoring techniques in pipeline and wind turbine industries

Authors: VB Sharma, K Singh, R Gupta, A Joshi, R Dubey, V Gupta, S Bharadwaj, A Srivastava

Citations: 44

Year: 2021

Mapping vegetation and measuring the performance of machine learning algorithm in LULC classification in the large area using Sentinel-2 and Landsat-8 datasets of Dehradun

Authors: A Srivastava, S Bharadwaj, R Dubey, VB Sharma, S Biswas

Citations: 35

Year: 2022

Exploring forest transformation by analyzing spatial-temporal attributes of vegetation using vegetation indices

Authors: A Srivastava, S Umrao, S Biswas

Citations: 18

Year: 2023

A probabilistic Gossip-based secure protocol for unstructured P2P networks

Authors: A Srivastava, P Ahmad

Citations: 18

Year: 2016

Determination of optimal location for setting up cell phone tower in city environment using LiDAR data

Authors: S Bharadwaj, R Dubey, MI Zafar, A Srivastava, VB Sharma, V Bhushan

Citations: 16

Year: 2020

Analyzing land cover changes over Landsat-7 data using Google Earth Engine

Authors: A Srivastava, S Biswas

Citations: 14

Year: 2023

GIS mapping of short-term noisy event of Diwali night in Lucknow city

Authors: R Dubey, S Bharadwaj, MI Zafar, V Mahajan, A Srivastava, S Biswas

Citations: 14

Year: 2022

Comparison of Sentinel and Landsat datasets over Lucknow region using gradient tree boost supervised classifier

Authors: A Srivastava, R Dubey, S Biswas

Citations: 13

Year: 2023

FCCC: Forest cover change calculator user interface for identifying fire incidents in forest region using satellite data

Authors: A Srivastava, S Umrao, S Biswas, R Dubey, MI Zafar

Citations: 11

Year: 2023

GIS based road traffic noise mapping and assessment of health hazards for a developing urban intersection

Authors: MI Zafar, R Dubey, S Bharadwaj, A Kumar, KK Paswan, A Srivastava

Citations: 9

Year: 2023

AI-driven environmental monitoring using Google Earth Engine

Authors: A Srivastava, H Sharma

Citations: 5

Year: 2024

A method for extracting deformation features from terrestrial laser scanner 3D point clouds data in RGIPT building

Authors: VB Sharma, R Dubey, A Bhatt, S Bharadwaj, A Srivastava, S Biswas

Citations: 4

Year: 2022

Conclusion✅

Maryam Fazlollahi Mohammadi | Soil and Macro Fauna | Zoology Honour Awards

Dr. Maryam Fazlollahi Mohammadi | Soil and Macro Fauna | Zoology Honour Awards

Lecturer at South East Technological University, Ireland

Dr. Maryam Fazlollahi Mohammadi is an accomplished forestry researcher and lecturer with a strong background in ecosystem ecology and biodiversity. She earned her BSc and MSc from Tehran University and completed her PhD in Forestry at Tarbiat Modares University in 2016, graduating first in her class. Her doctoral research focused on topographic impacts on forest ecosystems, particularly in the Hyrcanian forests. Maryam has since held academic positions in Iran and Ireland, including postdoctoral research at University College Dublin (UCD) and a current lecturer role at South East Technological University (SETU). Her work contributes significantly to understanding forest dynamics, soil-vegetation interactions, and carbon modeling. Dr. Fazlollahi is known for her collaborative projects, teaching excellence, and contributions to environmental sustainability. 🌍🌲📚

Professional Profiles📖

Education 🎓📚

Maryam obtained her Bachelor’s and Master’s degrees in Forestry from Tehran University, Iran, with a strong emphasis on forest ecology and land management. Her Master’s studies explored forest biodiversity, while her PhD research at Tarbiat Modares University (2016) investigated how topography and landscape features influence forest ecosystems—particularly tree regeneration, herbaceous layers, and soil quality—in the Hyrcanian forest region. Her research was pioneering in identifying natural regeneration patterns linked to soil traits and topography. She ranked 1st in her PhD cohort, a testament to her academic excellence and dedication. She also undertook part of her PhD research at the University of Torino, Italy, collaborating on soil carbon research with Dr. Daniel Said-Pullicino. Recently, she has expanded her skills through the QQI-accredited project management course for postdocs. 📖🌿🌎

Experience 🏥🔍

Maryam has extensive teaching and research experience in both Iranian and Irish academic settings. She began her career lecturing at Iranian universities, teaching ecology, soil science, and agricultural systems. As an occasional lecturer at UCD, she contributed to modules like Forest Climate and Carbon, supporting undergraduate learning through lectures, quizzes, and lab design. Her postdoctoral work at UCD focused on forest carbon dynamics as part of the Terrain-AI project, funded by SFI and Microsoft, involving collaboration with multiple universities. Since December 2022, she has been a Forestry Lecturer at SETU, engaging in undergraduate and postgraduate teaching, fieldwork, and student supervision. She maintains active collaborations across UCD, UL, UCC, and industry stakeholders. Her background bridges research and practice in forest management and climate mitigation. 🌲🧪📊

Awards & Honors 🏆🎖️

Dr. Maryam Fazlollahi Mohammadi has received several notable recognitions throughout her academic career. She ranked first in her PhD cohort at Tarbiat Modares University, a testament to her academic excellence. She was awarded an international research mobility grant by Iran’s Ministry of Science and Technology, facilitating collaborative research on soil carbon dynamics at the University of Torino, Italy. As a postdoctoral researcher on the prestigious Terrain-AI project—funded by Science Foundation Ireland (SFI) and Microsoft—she contributed to cutting-edge forest carbon modeling. At South East Technological University (SETU), she supervises multiple research theses, supporting student-led inquiry and applied learning. She also completed a QQI-accredited project management course for postdoctoral researchers. Dr. Fazlollahi has consistently been recognized for the timely and high-quality delivery of academic and administrative responsibilities. She serves as a reviewer for leading journals such as Catena, Ecological Research, and Scientific Reports, and has developed forest management training materials used across Northern Iran.

Research Focus 🔬🧪

Dr. Fazlollahi’s research spans forest ecology, biodiversity, and environmental modeling. Her work explores how topography, soil conditions, and landform shape influence forest regeneration and species distribution, particularly in mixed-species ecosystems like the Hyrcanian forests. She is an expert in spatial ecology and has contributed to innovative soil-carbon mapping and topographic modeling. As a core researcher on the Terrain-AI project, she focuses on linking management interventions with forest carbon fluxes to support climate mitigation. Her research outcomes support practical forestry decisions, from silviculture planning to policy advising. Her interdisciplinary collaborations extend into agroforestry, biodiversity conservation, and remote sensing integration. She actively works with stakeholders like Coillte, DAFM, and private forestry bodies to apply scientific insights to policy and practice. 🌳📈🌎

Skills 🛠️📊

Dr. Maryam Fazlollahi Mohammadi possesses a diverse and robust skill set that supports her academic, research, and professional endeavors. She is proficient in analytical tools such as MATLAB, R, and SPSS 📊, enabling her to conduct complex statistical analyses and data modeling. Her expertise in GIS and spatial analysis includes hands-on experience with ArcGIS and remote sensing technologies 🗺️. She has completed QQI-certified postdoctoral training in project management 📋, enhancing her ability to lead and coordinate interdisciplinary research projects. Dr. Fazlollahi is also adept in using research and academic tools like EndNote, GS+, and EXPERT CHOICE 🔬. Her teaching skillset includes integrating digital platforms such as Brightspace and Moodle and automating assessments via Google Sheets 💻. In the field and laboratory, she excels in soil sampling, vegetation surveys, and developing lab protocols 🌱. Her strong verbal and written communication 📢, multilingual fluency in English and Farsi 🗣️, and excellent organizational and collaborative abilities 🤝 ensure the timely and high-quality execution of both academic and outreach responsibilities.

Publications Top Notes📚

 

Impact of Management practices on stand-level carbon balance in Irish forests
Source: Irish Forestry
Year: 2022
Citations: Not available

Fine-scale topographic influence on the spatial distribution of tree species diameter in old-growth beech (Fagus orientalis Lipsky.) forests, northern Iran
Source: Scientific Reports, Nature
Year: 2022
Citations: Not available

The effect of landform on soil microbial activity and biomass in a Hyrcanian oriental beech stand
Source: CATENA
Year: 2017
DOI: 10.1016/j.catena.2016.10.006
Citations: 40+

Tree species composition, biodiversity and regeneration in response to catena shape and position in a mountain forest
Source: Scandinavian Journal of Forest Research
Year: 2017
DOI: 10.1080/02827581.2016.1193624
Citations: 30+

Slope gradient and shape effects on soil profiles in the northern mountainous forests of Iran
Source: Eurasian Soil Science
Year: 2016
DOI: 10.1134/s1064229316120061
Citations: 20+

Using the Analytical Network Process (ANP) based on BOCR Model to select the most suitable region for forestation with almond species
Source: Nusantara Bioscience
Year: 2015
DOI: 10.13057/nusbiosci/n070210
Citations: 15+

The influence of landform on the understory plant community in a temperate Beech forest in northern Iran
Source: Ecological Research
Year: 2015
DOI: 10.1007/s11284-014-1233-3
Citations: 25+

Adaptive Strip Sampling in Forest Inventory of Scattered Species of Ulmus glabrain Hyrcanian Forests Northern Iran
Source: Nusantara Bioscience
Year: 2015
Citations: 10+

Cost Price and Quality Evaluation of Seedlings in Public and Private Nurseries (Case Study: Pistacia Atlantic Avarmutica and Populus nigra in Zaghe and Kalantari Nurseries of Lorestan Province)
Source: Iranian Journal of Forest
Year: 2014
Citations: 5+

Qualitative and Financial Evaluation of Public and Private Forest Nurseries; Case study of Southern Zagros forests, Iran
Source: Bioscience
Year: 2014
Citations: 5+

Selection of the Most Suitable Species for Forestation in Southern Zagros Forests Using AHP & TOPSIS Techniques
Source: Ecology of Iranian Forest Journal
Year: 2014
Citations: 10+