Shamima Ferdousi Sifa | Remote Sensing | Research Excellence Award

Ms. Shamima Ferdousi Sifa | Remote Sensing | Research Excellence Award

Assistant Professor | University of Dhaka | Bangladesh

Shamima Ferdousi Sifa is an academic in disaster science and climate resilience with expertise in geospatial analysis, remote sensing, and climate risk assessment. Her research focuses on landslide susceptibility, flood dynamics, urban heat islands, and climate change impacts using GIS, satellite data, and statistical modeling. She contributes to interdisciplinary studies addressing hazard vulnerability, disaster risk reduction, and community resilience. Her professional work includes teaching, supervising research, and leading collaborative projects related to environmental hazards and climate adaptation. She has produced scholarly publications in environmental and geoscience fields with publication metrics including h-index: 4, Citations: 68, and Documents: 6, reflecting growing research influence in disaster and climate studies.

Citation Metrics (Scopus)

80

60

40

20

10

0

Citations
68

Documents
6

h-index
4

🟦 Citations    🟥 Documents    🟩 h-index


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Featured Publications

Yongjie Ji | Forests RS technology | Best Researcher Award

Yongjie Ji | Forests RS technology | Best Researcher Award

Assoc Prof Dr Yongjie Ji, Southwest Forestry University, China

Prof. Dr. Yongjie Ji, he appears to be a strong candidate for the Best Researcher Award. Here’s an overview of his achievements formatted with section headings:

Publication profile

Orcid

Educational Background

Dr. Yongjie Ji holds a Master of Applied Science in Cartography and Geographic Information Systems and a Ph.D. in Forest Management, focusing on forestry remote sensing and information technology. His educational foundation equips him with a robust understanding of both geographical information systems and forest management.

Professional Experience

Currently, Dr. Ji serves as an Associate Professor at Southwest Forestry University. He is actively involved in academia as a guest editor for the journal Forests and contributes as a reviewer for multiple reputable journals, including GSIS, RS, IJDE, and JEM. His teaching portfolio includes courses such as Principles of Geographic Information System and Introduction to Remote Sensing, where he shares his expertise with students.

Research Focus

Dr. Ji’s research centers on the application of multispectral, hyperspectral, LiDAR, and SAR remote sensing data for the inversion of forestry parameters. This field is critical for understanding and managing forest resources effectively, making his work highly relevant to current environmental and ecological challenges.

Contributions

Dr. Ji has authored or co-authored over 50 publications, including SCI papers and contributions to Chinese journals. He has also written two textbooks and one monograph, demonstrating his commitment to advancing knowledge in his field. His notable publications include:

Research Projects

Dr. Ji has presided over 16 significant research projects, including those funded by NFC and provincial authorities, contributing to the advancement of forestry science and technology. His leadership in these projects indicates a strong ability to manage and direct impactful research initiatives.

Publication top notes

Forest above-ground biomass estimation using X, C, L, and P band SAR polarimetric observations and different inversion models

Spatial and Temporal Change Characteristics and Climatic Drivers of Vegetation Productivity and Greenness during the 2001–2020 Growing Seasons on the Qinghai–Tibet Plateau

Correction: Wang et al. Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI. Forests 2024, 15, 215\

Assoc. Prof. Dr. Yongjie Ji’s extensive academic background, prolific research contributions, and leadership in significant projects make him a highly suitable candidate for the Best Researcher Award. His work not only enhances our understanding of forestry but also has practical applications that address contemporary environmental challenges.