Kamel Didan
Associate Professor, Department of Agricultural & Biosystems Engineering
(520) 621-8514
PhD, Remote Sensing, Spatial Analysis, and Modelling, ABE Dept., University of Arizona, 1999

The broad objectives and motives of my research derive from the questions surrounding global and climate change and their impact on vegetated land surfaces. We focus on the development of algorithms and data records using remote sensing, analytics, and models in order to generate highly calibrated time series data that inform climate-related and land use change influences on vegetation, carbon and nutrient cycles, ecosystem composition and function, and plant health and phenology over a wide range of biomes. A derivative of this research is the application of these data and methods to precision land surface mapping and agriculture.

I also teach a hybrid engineering and science course on small drones for the precision observation of the environment. This work is concerned with drone design, image processing algorithms, and the innovative applications of this technology in support of remote sensing data validation, precision agriculture and observation, and environmental work.

Selected Citations: 

Didan K., Yitayew, M. (2010). Prototype Geographic Information System For Agricultural Water Quality Management. ASCE Journal of Irrigation and Drainage Engineering. Vol 135 No. 1, pp 58-67. DOI: 10.1061/_ASCE_0733-9437_2009_135:1(58)

Nagler, P.L., Doody, T.M., Glenn, E.P., Jarchow, C.J., Barreto‐Muñoz, A. and Didan, K., 2015. Wide‐area estimates of evapotranspiration by red gum (Eucalyptus camaldulensis) and associated vegetation in the Murray–Darling River Basin, Australia. Hydrological Processes. (2015) 1-12.

Kim, Y., Kimball, J. S., Didan, K., & Henebry, G. M. (2014). Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agricultural and Forest Meteorology, 194, 132-143.

Kim, Y., Kimball, J. S., Zhang, K., Didan, K., Velicogna, I., & McDonald, K. C. (2014). International Journal of Remote Sensing, 35(10), 3700-3721.

Rahman A.F., Danilo D., Kamel D., Armando B. M., Joseph A. H., (2013). Detecting large scale conversion of mangroves to aquaculture with change point and mixed-pixel analyses of high-fidelity MODIS data. Remote Sensing of Environment, 130(2013), pp 96-107.

Whitsitt S., A M. Barreto, H. Maribel, H. Al-Helal, D. c. Chu, K. Didan, and J. Sprinkle. 2011, Constrained Data Acquisition for Mobile Citizen Science Applications. Proceedings of the Compilation of the co-located Workshops on DSM'11, TMC'11, AGERE!'11, AOOPES'11, NEAT'11, & VMIL'11. pp 267-271.

Michael A. White, Kirsten M. de Beurs, Kamel Didan, David W. Inouye, et. al, (2009), Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982 to 2006. Glob. Change Biol.15-2335–59. DOI: 10.1111/j.1365-2486.2009.01910.x

Huete A. R., Restrepo-Coup N., Ratana P., Didan K., Saleska S.R., Ichii K., Panathai S., Gamo M. (2008). Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in monsoon Asia. J. Agricultural and Forest Meteorology, 148 (2008), pp. 748-760. doi:10.1016/j.agrformet.2008.01.012

Jiang, Z., Huete, A.R., Didan, K., Miura T., (2008) Development of a 2-band enhanced vegetation index (EVI) without a blue band, Remote Sens. Environ. 112 (2008), pp. 3833-3845. doi:10.1016/j.rse.2008.06.006.

Scott S., K. Didan, Huete A., da Rocha H. (2007). Amazon Forests Green-up during 2005 drought. Science, doi: 10.1126/science.1146663.

Huete, A. R., K. Didan, Y. E. Shimabukuro, , P. Ratana, S. R. Saleska, L. R. Hutyra, W. Yang, R. R. Nemani, and R. Myneni (2006), Amazon rainforests green-up with sunlight in dry season, Geophys. Res. Lett., 33, No. 6, L06405, doi:10.1029/2005GL025538.

Brown M..E, J.E. Pinzon, K. Didan, J.T. Morisette, and C.J. Tucker, (2006). Evaluation of the consistency of long-term NDVI time series derived from AVHRR, SPOT-Vegetation,SeaWiFS, MODIS, and LandSAT ETM+ Sensors. IEEE Transaction on Geoscience and Remote Sensing 44, 1787-1793. doi 0.1109/TGRS.2005.860205

Myneni, R.B., Yang, W., Nemani, R. R., Huete, A. R., Dickinson, R. E., Knyazikhin, Y., Didan, K., Fu, R., Ju·rez, R. I. N., Saatchi, S. S., Hashimoto, H., Ichii, K., Shabanov, N. V., Tan, B., Ratana, P., Privette, J. L., Morisette, J. T., Vermote, E. F., Roy, D. P., Wolfe, R. E., Friedl, M. A., Running, S. W., Votava, P., El-Saleous N., Devadiga, S., Su, Y., and Salomonson, V. V. (2007). Large Seasonal Swings in Leaf Area of Amazon Rainforests, Proceedings of the National Academy of Sciences., PNAS 10.1073/pnas.0700618104.

Huete, A., Didan, K., Miura, T., and Rodriguez, E. Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices. Remote Sens. Environ., 83 (2003), pp. 195-213.

Huete, A., Didan, K. & Van Leeuwen, W. 2011, 'MODIS vegetation indices' in Ramachandran, B., Justice, C.O., and Abrams, M. (eds), Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science of ASTER and MODIS, Springer-Verlag, New York, pp. 579-602

Huete, A.R., Kim, Y., Ratana, P., Didan, K., Miura, T., and Shimabukuro, Y.E., 2008. “Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral and MODIS satellite data”. In Kalacska M., Sanchez-Azofeifa, A., (Eds), Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests, CRC Press, 320pp.

K.Didan,. et al., 2016. Vegetation Index and Phenology Multisensor Data records: ABTD and User Guide. V 4 (NASA-MEASURES Project). https://vip.arizona.edu/VIP_ATBD_UsersGuide.php

K.Didan,. et al., 2016. MODIS vegetation index algorithm Product suite User Guide. V6 (NASA-EOS Project). http://vip.arizona.edu/MODIS_UsersGuide.php

Research Themes: 
Land use change
Modeling and simulation
Natural resource management
Climate and Adaptation
Climate change
Ecosystem management
Land use
Remote sensing
Water quality
Food Systems and Security
International development
Water resource management
Informatics, Modeling, and Remote Sensing
Algorithm development
Data analysis
Data cleaning and information extraction
Data management
Data mining
Data visualization
Data-fusion technology
Natural Environment and Biodiversity
Carbon cycling
Ecosystem disturbances
Ecosystem ecology
Ecosystem services
Riparian ecology
Terrestrial ecology
Decision support
Urban Environment
Arid lands
Climate change impacts
Water distribution systems

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