Mrs. Dinara Talgarbaeva | Remote Sensing | Top Researcher Award

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Mrs. Dinara Talgarbaeva | Remote Sensing | Top Researcher Award

Mrs. Dinara Talgarbaeva | Remote Sensing | Institute of Ionosphere | Kazakhstan

Mrs. Dinara Talgarbayeva is an accomplished Senior Researcher at the Institute of Ionosphere, Almaty, Kazakhstan, whose expertise lies in satellite-based geodynamic monitoring, InSAR technology, Sarscape data analysis, and GIS analytics. She holds both a Bachelor’s and a Master’s degree in Geology from Satbayev University, Kazakhstan, where she developed her foundational understanding of geological processes and earth observation systems. Over the course of her career, Mrs. Talgarbayeva has built a solid professional portfolio focused on applying remote sensing techniques to study geological deformations, land subsidence, and mineral exploration. Her research integrates Sentinel-1 SAR data, digital elevation models, and lineament analysis to provide accurate insights into seismic hazards and geodynamic changes in Kazakhstan and other Central Asian regions. As a dedicated scientist, she actively collaborates with multidisciplinary teams and international researchers, contributing to innovative solutions in geodesy, environmental monitoring, and mineral mapping. Her research interests are centered around earth observation, geodynamic zoning, natural hazard detection, and data-driven modeling for sustainable resource management. Mrs. Talgarbayeva possesses advanced research skills in SAR interferometry, GIS processing, multispectral analysis, and automation of geological data interpretation using satellite imagery, enabling her to produce reliable and scalable models for terrain deformation and subsidence assessment. She has demonstrated consistent excellence through her participation in numerous high-impact studies and has published multiple research papers in prestigious peer-reviewed journals such as Minerals, Geomatics, Engineered Science, and Reliability Theory and Applications, all indexed in Scopus and IEEE. These publications reflect her growing academic influence and her ability to translate complex scientific data into actionable insights.

Professional Profile: ORCID | Scopus

Selected Publications 

  1. Talgarbayeva, D., Satbergenova, A., Vilayev, A., Urazaliyev, A., & Yelisseyeva, A. (2025). InSAR-based assessment of land subsidence induced by coal mining in Karaganda, Kazakhstan. Geomatics, 5(4). [Cited by 12]

  2. Talgarbayeva, D., Serikbayeva, E., Orynbassarova, E., & Sydyk, N. (2025). Application of multispectral data in detecting porphyry copper deposits: The case of Aidarly Deposit, Eastern Kazakhstan. Minerals, 15(9). [Cited by 9]

  3. Talgarbayeva, D., Vilayev, A., Serikbayeva, E., & Ahmadi, H. (2025). Integrated prospectivity mapping for copper mineralization in the Koldar Massif, Kazakhstan. Minerals, 15(8). [Cited by 11]

  4. Talgarbayeva, D., Kairanbayeva, A., Nurakynov, S., & Mitkov, A. (2024). Predictive system for road condition monitoring based on open climate and remote sensing data – A case study with mountain roads. Engineered Science, 8(2). [Cited by 7]

  5. Talgarbayeva, D., Fremd, A., & Gaipova, A. (2023). Possibilities of lineament analysis of DEM SRTM during geodynamic zoning of seismic hazardous territories (on the example of the North-Tien-Shan region). Reliability Theory and Applications, 5(75), 96–110. [Cited by 5]

Dr. Arif UR Rehman | Remote Sensing Awards | Best Researcher Award

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Dr. Arif UR Rehman | Remote Sensing Awards | Best Researcher Award

Dr. Arif UR Rehman, Aerospace Information Research Institute, CAS, Pakistan

Arif UR Rehman, is a dedicated researcher specializing in Remote Sensing, GIS, and Forestry. He is currently a Research Assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences, in Beijing, China, where he focuses on spatio-temporal remote sensing data acquisition, processing, and developing machine learning-based tools for vegetation mapping. Previously, he worked as a Remote Sensing Analyst at CABI International under an ADB project, contributing to food security by enhancing crop classification techniques. Arif holds a Master’s degree in Forestry from Beijing Forestry University, an MPhil in Remote Sensing and GIS from the University of the Punjab, an MSc in GIS, a PGD in Remote Sensing & GIS, and an MSc in Electronics from the University of Peshawar. His academic research spans diverse topics, including forest classification, afforestation impact assessment, and land surface temperature analysis. With a strong background in scientific publications and GIS-based spatial analysis, he continues to contribute to advancements in remote sensing and environmental monitoring.

Professional Profile:

SCOPUS

Suitability for the Best Researcher Award

Based on the provided information, Arif UR Rehman has a strong academic and research background in Remote Sensing, GIS, and Machine Learning Applications in Forestry and Agriculture. His qualifications and achievements make him a potential candidate for the Best Researcher Award, but there are some aspects to consider:

🎓 Education

📍 Master in Forestry (Professional Degree) (2019 – 2021)

  • Institution: Beijing Forestry University, China 🇨🇳
  • Field: Forest Management
  • Final Grade: A+
  • Thesis: Feasibility of combining Landsat-8 data with ancillary variables for forest types and land cover classification in mountainous terrains of northern Pakistan

📍 Master of Philosophy (MPhil) in Remote Sensing and GIS (2017 – 2019)

  • Institution: PUCIT, University of the Punjab, Pakistan 🇵🇰
  • Field: Remote Sensing & GIS
  • Final Grade: 72.8%
  • Thesis: Remote Sensing & GIS application for monitoring and evaluating afforestation impact – A case study of the Billion Tree Tsunami Project in Peshawar, Pakistan 🌳

📍 Master of Science (MSc) in Geographic Information System (GIS) (2015 – 2017)

  • Institution: PUCIT, University of the Punjab, Pakistan 🇵🇰
  • Field: GIS
  • Final Grade: 73.2%
  • Thesis: Analyzing the impacts of deforestation on Land Surface Temperature in Northern Pakistan 🌍🌡️

📍 Post Graduate Diploma (PGD) in Remote Sensing & GIS (2014 – 2015)

  • Institution: NCE in Geology, University of Peshawar, Pakistan 🇵🇰
  • Field: GIS & Remote Sensing
  • Final Grade: 73%
  • Thesis: Spatial-Temporal assessment of Land-Use and Land-Cover changes in Lahore 🏙️

📍 Master of Science (MSc) in Electronics (2013 – 2015)

  • Institution: University of Peshawar, Pakistan 🇵🇰
  • Field: Electronics
  • Final Grade: 64%
  • Thesis: Developing Hardware & Android software for Outdoor Advertisement Display 📱💡

🏢 Work Experience

🔹 Research Assistant (Feb 2023 – Aug 2024)

  • Institution: Aerospace Information Research Institute, Chinese Academy of Sciences 🇨🇳
  • Location: Beijing, China
  • Key Responsibilities:
    • Spatio-Temporal Remote Sensing data acquisition & processing 🛰️
    • Developing Machine Learning-based tools for vegetation mapping 🌿🤖
    • Scientific Publications 📚

🔹 Remote Sensing Analyst (Jun 2022 – Dec 2022)

  • Organization: CABI International; ADB Project: Strengthening Food Security 🇵🇰
  • Location: National Agricultural Research Centre (NARC), Islamabad
  • Key Responsibilities:
    • Capacity development of the Crop Reporting Service Department 🌾
    • Google Earth Engine for Crop Classification 🛰️
    • Seasonal Crop Classification Maps 📊

🏆 Achievements & Contributions

✔ Published multiple scientific papers in Remote Sensing, GIS, and Forestry journals 📄🔬
✔ Expertise in Google Earth Engine, Machine Learning, GIS & Remote Sensing, and Crop Mapping 🌍🌾
✔ Significant contributions to afforestation projects (Billion Tree Tsunami) 🌳✅
✔ Developed ML-based tools for vegetation mapping and land cover classification 🤖🌎

🎖 Awards & Honors

🏅 A+ Grade in Master’s at Beijing Forestry University (Highest distinction)
🏅 Recognized for contributions to afforestation monitoring in Pakistan 🌲
🏅 Key researcher in major GIS & Remote Sensing projects 📊

Publication Top Notes:

Removal of environmental influences for estimating soil texture fractions based on ZY1 satellite hyperspectral images

Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analysis and Machine Learning Classification

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

Large Scale Fish Images Classification and Localization using Transfer Learning and Localization Aware CNN Architecture

Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan

Comparing different space-borne sensors and methods for the retrieval of land surface temperature