Mark Friedl Publications

Publications on this site range from 2012 to present.
*Denotes Student

2015

Salmon, J.M. M.A. Friedl, S. Frolking, D. Wisser and E. M. Douglas, 2015. Global rained, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories, and climate data. International Journal of Applied Earth Observation and Geoinformation, 38, pp. 321-334; doi:10.1016/j.jag.2015.01.014.

Michael Toomey, Mark A. Friedl, Steve Frolking, Koen Hufkens, Stephen Klosterman, Oliver Sonnentag, Dennis D. Baldocchi, Carl J. Bernacchi, Gil Bohrer, Edward Brzostek, Sean P. Burns, Carole Coursolle, David Y. Hollinger, Hank A. Margolis, Harry McCaughey, Russell K. Monson, J. William Munger, Stephen Pallardy, Richard P. Phillips, Margaret Torn, Sonia Wharton, Marcelo Zeri, Andrew D. Richardson, Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis, Ecological Applications, 25.1, 99-115. http://dx.doi.org/10.1890/14-0005.1

2014

Cai, S. Liu, D. Sulla-Menashe, D* and M.A. Friedl 2014.  Enhancing MODIS land cover product with a spatial-temporal modeling algorithm.  Remote Sensing of Environment, 147, pp. 243-255. doi:10.1016/j.rse.2014.03.012

Friedl, M.A., J.M. Gray, E.K. Melaas*, A.D. Richardson, K. Hufkens, T.F. Keenan, A. Bailey and J. O’Keefe. 2014. A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environmental Research. Letters. 9054006 doi:10.1088/1748-9326/9/5/054006

Glanz, H., L. Carvalho, D. Sulla-Menashe* and M.A. Friedl, 2014. A parametric model for classifying land cover and evaluating training data based on mulit-temporal remote sensing data, ISPRS Journal of Photogrammetry and Remote Sensing, 97, pp. 219-228; doi:10.1016/j.isprsjprs.2014.09.004

Gray, J. M., Frolking, S., Kort, E. A., Deepak, K., Kucharik, C. J., Ramankutty, N. & Friedl, M. A. (2014).  Direct human influence on atmospheric CO2 seasonality from increased cropland productivity. Nature, 515, 398-401. doi: 10.1038/nature13957

Gray, J.M., M.A. Friedl, S. Frolking, N. Ramankutty, A.Nelson and M. Gumma, 2014. Mapping Asian Cropping Intensity with MODIS.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7.8, 3374-3379. doi: 10.1109/JSTARS.2014.2344630

Huang, X* and M.A. Friedl, 2014.  Distance metric-based forest cover change detection using MODIS time series, International Journal of Applied Remote Sensing and Geoinformation, 29:78-92 doi:10.1016/j.jag.2014.01.004

T.F. Keenan, B. Darby, E. Felts, O. Sonnentag, M. Friedl, K. Hufkens, J. O’Keefe, S. Klosterman, J.W. Munger, M. Toomey, A.D. Richardson, 2014. Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment, Ecological Applications,24:1478–1489. DOI: 10.1890/13-0652.1

Keenan, T.F., J. Gray, M.A. Friedl, M. Toomey, G. Bohrer, D. Y. Hollinger, J.W. Munger, J.O’Keefe, H.P. Schmid, I. Sue Wing, B. Yang and A.D. Richardson, 2014. Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nature Climate Change, doi:10.1038/nclimate2253

Klosterman, S.T., K. Hufkens, J.M. Gray, E. Melaas*, O. Sonnetag, I. Lavine, L. Mitchell, R. Norman, M.A. Friedl, and A.D. Richardson, 2014.  Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences, 11, 4305-4320. Doi: 10.5194/bg-11-4305-2014.

Li, L.; Friedl, M.A.; Xin, Q.; Gray, J.; Pan, Y.; Frolking, S, 2014Mapping Crop Cycles in China Using MODIS-EVI Time Series. Remote Sensing6, 2473-2493. doi:10.3390/rs6032473

Martellozzo, F, N. Ramankutty, R.J. Hall, D.T. Price, B. Purdy and M.A. Friedl, 2014.  Urbanization and the loss of prime farmland: a case study in the Calgary-Edmonton corridor of Alberta, Regional Environmental Change, DOI 10.1007/s10113-014-0658-0

Sulla-Menashe, D., R. Kennedy, Z. Yang, J. Braaten, O.N. Krankina and M.A. Friedl 2013, Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation, Remote Sensing of Environment, 151, pp 114-123; DOI: 10.1016/j.rse.2013.07.042

Verma,M., M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, B. Gielen, O. Roupsard, E. J. Moors, P. Toscano, F. P. Vaccari, D. Gianelle, G. Bohrer, A. Varlagin, N. Buchmann, E. van Gorsel, L. Montagnani, and P. Propastin, 2014. Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set, Biogeosciences, 11, 2185-2200. doi:10.5194/bg-11-2185-2014

2013

Bolton, D.K. and M.A. Friedl 2013.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics, Agricultural and Forest Meteorology, 173, 74-84. doi:10.1016/j.agrformet.2013.01.007

Frolking, S., T. Milliman, K. Seto and M.A. Friedl 2013.  A global fingerprint of macro-scale changes in urban structure from 1999-2009, Environmental Research Letters, (8) 2013, 10 pp. doi:10.1088/1748-9326/8/2/024004

Justice, C.O., M.O, Roman, I. Csiszar, E.F. Vermote, R.E. Wolfe, S.J. Hook, M. Friedl, Z.S. Wang, C.B. Schaaf, T. Miura, M. Tschudi, G. Riggs, D.K. Hall, A. Lyapustin,S. Sadashiva, C. Davidson, E.J. Masuoka, 2013 Land and cryosphere products from Suomi NPP VIIRS: Overview and Status, Journal of Geophysical Research-Atmospheres, 118(17), pp. 9753-9765, DOI: 10.1002/jgrd.50771.

Melaas, E.K.*, M.A. Friedl and Z. Zhe 2013.  Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETN+ data, Remote Sensing of Environment, 132, 176-185. doi:10.1016/j.rse.2013.01.011

Melaas, E.K.*, A.D. Richardson, M.A. Friedl, D. Dragoni, C.M. Gough, M. Herbst, L. Montagnani, and E. Moors 2013.  Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems, Agricultural and Forest Meteorology, 171-172, 46-56. DOI: 10.1016/j.agrformet.2012.11.018

2012

Avitable, V., Baccini, A., Friedl, M.A. and C. Schmullius, 2012.  Capabilities and limitations of Landsat and land cover data for aboveground biomass estimation in Uganda, Remote Sensing of Environment, 117(15), pp. 366-380. doi:10.1016/j.rse.2011.10.012

Baccini, A., Goetz, S.J., Walker, W.S. Laporte, N.T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P.S.A., Dubayah, R., Friedl, M.A., Samanta, S. and R.A. Houghton 2012. Estimated carbon dioxide emissions from tropical deforestation improved by carbon density maps, Nature Climate Change, 2, 182-185 doi:10.1038/nclimate1354

Chong, L., S. Ray, G. Hooker and M.A. Friedl 2012. Functional factor analysis for periodic remote sensing data, The Annals of Applied Statistics, 6(2), pp 610-624, DOI: 10.1214/11-AOAS518.

Hufkens, K., M.A. Friedl, T.F. Keenan, O. Sonnentag, A. Bailey, J. O’Keefe and A. D. Richardson 2012. Ecological impacts of a widespread frost event following early spring leaf-out, Global Change Biology, 18 (7), pp. 2365-2367, DOI: 10.1111/j.1365-2486.2012.02712.x

Hufkens, K, Friedl, M.A. Sonnetag, O., Braswell, B.H., Millman, T. and A.D. Richardson, 2012. Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology, Remote Sensing of Environment, 117(15), pp. 366-380. doi:10.1016/j.rse.2011.10.006

Olofsson, P., Stehman, S.V., Woodcock, C.E., Friedl, M.A. Sulla-Menashe, D.*, Sibley, A.M., Newell, J.D. and M. Herold 2012.  A global land cover validation data set, I: Fundamental Design principles, International Journal of Remote Sensing, 33(18), pp 5768-5788. DOI: 10.1080/01431161.2012.674230

Sonnentag, O., Hufkens, K.,Teshera-Sterne, Young, A.M., Friedl, M.A., Braswell, B.H., Milliman, T., O’Keefe, J., and A.D. Richardson, 2012, Digital repeat photography for phenological research in forest ecosystems, Agricultural and Forest Meteorology, 152, pp. 159-177. doi:10.1016/j.agrformet.2011.09.009

Stehman, S.V., Olofsson, P., Woodcock, C.E., M. Herold , and M.A. Friedl, 2012.  A global land cover validation data set, II: Augmenting a stratified sampling design to estimate accuracy by region and land-cover class, International Journal of Remote Sensing, 33(22), pp. 6975-6993. DOI:10.1080/01431161.2012.695092