Member of the Graduate Faculty | Research Professor, Electrical and Computer Engineering | Professor, Remote Sensing / Spatial Analysis - GIDP | Professor, Biosystems Engineering
My teaching and research focus on global remote sensing of land surface vegetation, with a strong emphasis on developing precise measurements, data, algorithms, and models for calibrated time series analysis. This work seeks to assess the impacts of climate change and land use on vegetation, phenology, ecohydrology, water, carbon, nutrient cycles, and ecosystem composition and function across diverse biomes. By bridging natural resources management with advanced remote sensing techniques, my research addresses complex challenges in both engineering and society, spanning areas such as agricultural productivity, ecosystem management, watershed analysis at scales from local to global, and the precise observation of some of these phenomena.Additionally, my research group has developed a program dedicated to engineering and applied use of drone technology, providing rapid, cost-effective platforms for land surface characterization, precision mapping, precision agriculture, and low-cost validation of global remote sensing data. A field that is evolving very quickly providing for opportunities but also challenges.I am also committed to fostering an engaging academic environment for students at all levels. I promote a program that actively involves undergraduate and graduate students in internships, MS and Ph.D. research opportunities, and immersive, collaborative research projects, encouraging them to develop their research interests through hands-on experience and teamwork.
The Department of the Interior has awarded a grant to Professor Kamel Didan from Biosystems Engineering for the project titled "Natural Vegetation Water Use and Efficiency at the US-Mexico Transboundary Region." Dr. Didan, a remote sensing expert, along with his VIP Lab (vip.arizona.edu) students and personnel, are leading the research aimed at studying water use and efficiency in natural vegetation along the US-Mexico border. Key components of the project include:
- Harmonizing regional land cover maps using a machine learning classification model to support transboundary natural resources and ecohydrology research.
- Utilizing remote sensing techniques to analyze the impact of climate and land use changes on vegetation water use.
- Offering insights to guide decision-making, restoration efforts, habitat quality assessments, and enhance regional resilience at the US-Mexico border.
- Expanding our understanding of transboundary water challenegs and its broader impact on regional vegetation.
Development of Algorithms and time series data from Remote sensing platforms, Time series analysis in support of Ecosystem and natural resources management, Climate-related and land use change influences on vegetation and phenology, Multidisciplinary use of remote sensing data, Development of a UAV/drone based program for the precision observation of the environment, including precision Agriculture, land mapping, and space-borne data validation. Prerequisite Courses: Data Science, Programming (Python), Image processing Majors: Computer Science, ECE, Natural Resources
Development of Algorithms and time series data from Remote sensing platforms, Time series analysis in support of Ecosystem and natural resources management, Climate-related and land use change influences on vegetation and phenology, Multidisciplinary use of remote sensing data, Development of a UAV/drone based program for the precision observation of the environment, including precision Agriculture, land mapping, and space-borne data validation. Prerequisite Courses: Data Science, Programming (Python), Image processing Majors: Computer Science, ECE, Natural Resources