Abd-Elrahman, Amr

Associate Professor, Geomatics

Amr Abd-Elrahman joined the Geomatics program in April 2007. He is School of Forest, Fisheries, and Geomatics Sciences faculty and the Geomatics program adviser in the Gulf Coast Research and Education Center (Plant City).


SUR5386 Image Processing for Remote sensing
GIS6116 Geographic Information systems Analysis
SUR6934 GIS Fundamentals
GIS3072c Geographic Information Systems
SUR3641 Survey Computations
GIS4121 Geospatial Analysis
FOR3434/FOR5435 Forest Information Systems

His research area of interest is remote sensing and geospatial analysis applications in precision agriculture and natural resource management. Amr utilizes multi-spectral and hyperspectral images acquired from satellite, airborne, and small Unmanned Aircraft Systems (sUAS) as well as lidar datasets. He uses machine learning algorithms, including deep learning networks (e.g., CNN, FCN, Deeplab) to analyze high spatial resolution sUAS images.

At the application side, Amr leads and collaborates on multiple projects for agricultural crop yield modeling and phenotyping, vegetation biophysical and biogeochemical characteristics modeling, classifying submerged vegetation, and modeling water quality and soils parameters. He also used GIS to quantify forest ecosystem services.

  • Image classification using deep learning networks
  • Precession agriculture applications using small Unmanned Aircraft Systems (sUAS)
  • Coastal mapping using UAS-Lidar and hyperspectral imagery
  • Soil property prediction modeling and mapping use remote sensing and geospatial analysis
  • Integrating community-based data collection with Geomatics technologies in natural resources applications
  • Object-based image analysis of high-resolution imagery
  • Modeling vegetation biophysical and biogeochemical parameters using multispectral and hyperspectral imagery
  • Submerged aquatic vegetation and water quality studies through radiative transfer models
  • Image analysis using wavelet decomposition techniques
  • GIS and remote sensing applications of airborne laser scanners (LIDAR)
  • Pattern recognition and artificial intelligence applications in mapping
  • Ecosystem service quantification
  • Development of hyperspectral image sensing platform and unmanned air vehicle for image acquisition
    Gulf Coast Research and Education Center
    1200 N. Park Road
    Plant City, FL 33563

    (813) 757.2283

    📑 Curriculum Vitae

  • PhD, Civil Engineering – Geomatics Program- University of Florida Minor in Computer and Information Engineering, University of Florida, 2001
  • MS, Civil Engineering (Public Works) Ain Shams University, Cairo, Egypt, 1994
  • BS, Civil Engineering, Ain Shams University, Cairo, Egypt, 1990
  • Liu, T., Abd-Elrahman, A., Dewitt, B., Smith, S., Morton, J., & Wilhelm, V. L. (2019). Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers. GIScience & Remote Sensing, 56, 130-159.
  • Abdulridha, J., Ehsani, R., Abd-Elrahman, A., & Ampatzidis, Y. (2019). A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. Computers and Electronics in Agriculture, 156, 549-557.
  • Abd-Elrahman, A., Britt, K., Benjamin, A., Barnes, G., Dewitt, B., Hochmair, H., Wilkinson, B., Smith, S. (2019) Geomatics education at the University of Florida: a case study of challenges and adaptation. Surveying and Land Information Sciences 78, 5-16.
  • Liu, T., Abd-Elrahman, A., (2018). Deep Convolutional Neural Network Training Enrichment using Multi-View Object-based Analysis of Unmanned Aerial Systems Imagery for Wetlands Classification. International Society of Photogrammetry and Remote Sensing Journal, 139, 154-170.
  • Liu, T., Abd-Elrahman, A. (2018). An Object-based Image Analysis Method for Enhancing Classification of Land Covers using Fully Convolutional Networks and Multi-View Images of small Unmanned Aerial System. Remote Sensing, 10(3), 457
  • Liu, T. & Abd-Elrahman, A. Liu, T. g & Abd-Elrahman, A. (2018). Multi-view Object-based Classification of Wetland Land covers using Unmanned Aerial System Images. Remote Sensing of the Environment, 216, 122-138.
  • Liu, T. & Abd-Elrahman, A. (2018). A fully learnable context-driven object-based model for mapping land cover using multi-view data from unmanned aircraft systems. Remote Sensing of the Environment, 216, 328-344.
  • Roberts, J., Koeser, A., Abd-Elrahman, A., Hansen, G., Landry, S. & Wilkinson, B. (2018). Terrestrial Photogrammetric Stem Mensuration for Street Trees. Urban Forestry & Urban Greening, 35, 66-71.
  • Liu, T., Abd-Elrahman, A., Morton, J., & Wilhelm, V. L. (2018). Comparing Fully Convolutional Networks, Random Forest, Support Vector Machine, and Patch-based Deep Convolutional Neural Networks for Object-based Wetland Mapping using Images from small Unmanned Aircraft System. GISScience & Remote Sensing, 55:2, 243-264.
  • Xu, Y., Smith, S. E., Grunwald, S., Abd-Elrahman, A., & Wani, S. P. (2018). Effects of image pansharpening on soil total nitrogen prediction models in South India. Geoderma, 320, 52-66.
  • Ahmed, M, Abd-Elrahman, A., Escobedo, F., Martin, M. & Timilsina, N. (2017). Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. Journal of Environmental Management, 199, 158-171.
  • Xu, Y., Smith, S. E., Grunwald, S., Abd-Elrahman, A., Wani, S. P., & Nair, V. D. (2018). Estimating soil total nitrogen in smallholder farm settings using remote sensing spectral indices and regression kriging. Catena, 163, 111-122.
  • Xu, Y., Smith, S. E., Grunwald, S., Abd-Elrahman, A., Wani, S. P., & Nair, V. D. (2017). Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings. Environmental monitoring and assessment, 189(10), 502.
  • Pande-Chhetri, R., Abd-Elrahman, A., Liu, T., Morton, J., & Wilhelm, V. L. (2017). Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery. European Journal of Remote Sensing, 50(1), 564-576.
  • Xu, Y., Smith, S., Grunwald, S., Abd-Elrahman, A. & Wani, S. (2017). Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings. Journal of Environmental Management, 200, 423-433.
  • Xu, Y., Smith, S., Grunwald, S., Abd-Elrahman, A., & Wani, S. P. (2017). Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields. ISPRS Journal of Photogrammetry and Remote Sensing, 123, 1-19.
  • Delphin, S., Escobedo, F. J., Abd-Elrahman, A., & Cropper, W. P., 2016. Urbanization as a land use change driver of forest ecosystem services. Land Use Policy, 54, 188-199.
  • Clerici, N., Rubiano, K., Abd-Elrahman, A., Posada Hoestettler, J. M., & Escobedo, F. J., 2016. Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery. Forests, 7(7), 138.
  • Abd-Elrahman, A., Sassi, N., Wilkinson, B., & Dewitt, B., 2016. Georeferencing of mobile ground-based hyperspectral digital single-lens reflex imagery. Journal of Applied Remote Sensing, 10(1), 014002-014002.
  • Friedman, M., Andreu, M., Zipperer, W., Northrop, R., Abd-Elrahman, A., , 2015. Species composition of forested natural communities near freshwater hydrologic features in an urbanizing watershed of West Central Florida. Florida Scientist 78:111-129
  • Szantoi, Z., Escobedo, F. J., Abd-Elrahman, A., Pearlstine, L., Dewitt, B., & Smith, S., 2015. Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.Environmental monitoring and assessment,187: 1-15.
  • Pande-Chhetri, R., Abd-Elrahman, A., & Jacoby, C., 2014. Classification of Submerged Aquatic Vegetation in Black River Using Hyperspectral Image Analysis. Geomatica, 68:169-182.
  • Anne, N. J., Abd-Elrahman, A. H., Lewis, D. B., & Hewitt, N. A., 2014. Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands. International Journal of Applied Earth Observation and Geoinformation, 33:47-56.
  • Cademus, R., Escobedo, F. J., McLaughlin, D., & Abd-Elrahman, A., 2014. Analyzing Trade-Offs, Synergies, and Drivers among Timber Production, Carbon Sequestration, and Water Yield in Pinus elliotii Forests in Southeastern USA.Forests, 5:1409-1431.
  • Delphin, S., F. Escobedo, A. Abd-Elrahman, C. Wendell, 2013. Mapping potential carbon and timber losses from hurricanes using a decision tree and ecosystem services driver model Journal of Environmental Management. Environmental Management 129:599-607.
  • Szantoi, Z., F. Escobedo, A. Abd-Elrahman, S. Smith, and L. Pearlstine, 2013. Analyzing fine-scale wetland composition using high resolution imagery and texture features. International journal of Applied Earth Observation and Geoinformation 23:204-212.
  • Panda-Chhetri, R.and A. Abd-Elrahman, 2013. Filtering high-resolution hyperspectral imagery in a maximum noise fraction transform domain using wavelet-based de-striping. International Journal of Remote Sensing 34:2216-2235.
  • Timilsina, N., F. Escobedo, W. P. Cropper Jr., A. Abd-Elrahman, S. Delphin, and S. Lambert, 2013. A framework for mapping carbon hotspots and determining optimal forest structure and management regime characteristics. Environmental Management 114:293-302.
  • Dix, M., A. Abd-Elrahman, B. Dewitt, and L. Nash, 2012. Accuracy evaluation of terrestrial LiDAR and multibeam sonar Systems mounted on a survey vessel. Journal of Surveying Engineering, 138:203-213.
  • Nettleman, C.A. and A. Abd-Elrahman, 2012. A Contemporary Review of Deficiencies Associated with Calculated Tidal Datums and Property Ownership Law. Surveying and Land Information Science Surveying and Land Information Science 71:69-77.
  • Panda-Chhetri and R., A. Abd-Elrahman, 2011. De-striping hyperspectral images using wavelet transform and adaptive frequency domain filtering. International Society of Photogrammetry and Remote Sensing Journal 66:620-636.
  • Malone, S.L., L. Kobziar, C. Staudhammer, and A. Abd-Elrahman, 2011. Modeling relationships among 217 fires using remote sensing of burn severity in southern Pine forests. Remote Sensing 3:2005-2028.
  • Abd-Elrahman, A., M. Croxton, R. Pande-Chhetri, G. Toor, S. Smith, and J. Hill, 2011. Chlorophyll-a estimation using in-situ hyperspectral imaging system in aquaculture water bodies. ISPRS Journal of Photogrammetry and Remote Sensing 66: 463-472.
  • Shaker, I., A. Abd-Elrahman, A. Khedr and M. Atef, 2011. Building extraction from high resolution space images in high density residential areas in the Great Cairo region. Remote Sensing 3:781-791.
  • Abd-Elrahman A., R. Pande-Chhetri and G. Vallad, 2011. Design and development of a multi-purpose low-cost hyperspectral imaging system. Remote Sensing 3:570-586.
  • Eltokhy, M., A. Abd-Elrahman, T. Fathy, A. Awad, 2011. Preliminary Evaluation of Baseline Relative Accuracies Using L1 Frequency Observations of Navigation-Grade GARMIN Receivers. Journal of Surveying Engineering 137:26-32