Bahram Daneshfar

Geostatistical Analyst

Application of quantitative and Earth Observation-based methods in agricultural monitoring

Key publications

  1. Mardian, J., Berg, A., Daneshfar, B. (2021). Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis, 255 http://dx.doi.org/10.1016/j.rse.2021.112292

    2021 - View publication details

  2. Arcand, Y., Bittman, S., Britten, M., Champagne, C., Daneshfar, B., David, S., Davidson, A., Derdall, E. Drury, C., Eden, J., Handyside, P., Hung, Y., Javorek, S., Jiang, Y., Jingui, Y., Jordan, C., Joosse, P., Li, S., McAuley, E., McNairn, H., MacDonald, D., Pacheco, A., Powers, J., Reid, K., Smith, E. L., Topp, E., Vanrobaeys, J., Villeneuve, S., Wilson, H., Yang, J. Agricultural Land Use. Synthesis of Science. Canada Water Agency Report. Freshwater Science Assessment-Line of Inquiry. Nov. 20, 2020.

    2020 - View publication details

  3. Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. Handbook on Remote Sensing for Agricultural Statistics. GSARS Handbook: Rome.

    Davidson, A.M., Fisette, T., Mcnairn, H. & Daneshfar, B. 2017. Detailed crop mapping using remote sensing data (Crop Data Layers). In: J. Delincé (ed.), Handbook on Remote Sensing for Agricultural Statistics (Chapter 4). Handbook of the Global Strategy to improve Agricultural and Rural Statistics (GSARS): Rome.

    2017 - View publication details

  4. Dong, T., Liu, J., Shang, J., Qian, B., Huffman, T., Zhang, Y., Champagne, C., Daneshfar, B. (2016). Assessing the impact of climate variability on cropland productivity in the Canadian Prairies using time series MODIS FAPAR, 8(4), http://dx.doi.org/10.3390/rs8040281

    2016 - View publication details

  5. Chipanshi, A., Zhang, Y., Kouadio, L., Newlands, N., Davidson, A., Hill, H., Warren, R., Qian, B., Daneshfar, B., Bedard, F., Reichert, G. (2015). Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape, 206 137-150. http://dx.doi.org/10.1016/j.agrformet.2015.03.007

    2015 - View publication details

  6. Qiao, C., Wang, J., Shang, J., Daneshfar, B. (2015). Spatial relationship-assisted classification from high-resolution remote sensing imagery, 8(9), 710-726. http://dx.doi.org/10.1080/17538947.2014.925517

    2015 - View publication details

  7. Liu, H., Huffman, T., Liu, J., Li, Z., Daneshfar, B., Zhang, X. (2015). Integration of multi-disciplinary geospatial data for delineating agroecosystem uniform management zones, 187(1), 1-17. http://dx.doi.org/10.1007/s10661-014-4102-1

    2015 - View publication details

  8. Du, Y., Huffman, T., Daneshfar, B., Green, M., Feng, F., Liu, J., Liu, T., Liu, H. (2015). Improving the spatial resolution and ecostratification of crop yield estimates in Canada, 95(3), 287-297. http://dx.doi.org/10.4141/CJSS-2014-017

    2015 - View publication details

  9. Champagne, C., McNairn, H., Daneshfar, B., Shang, J. (2014). A bootstrap method for assessing classification accuracy and confidence for agricultural land use mapping in Canada, 29(1), 44-52. http://dx.doi.org/10.1016/j.jag.2013.12.016

    2014 - View publication details