Mark
Friedl
Professor & Chairman
Department
of Geography and Environment, Boston University
Contact Information
675 Commonwealth
Ave, Boston, MA 02215
Tel: (617)
353-5745;
Fax: (617)
353-8399
Email: friedl@bu.edu;
Baccini,
A, M.A. Friedl, C.E. Woodcock and R. Warbington 2004. Forest biomass estimation over regional
scales using multisource data, Geophysical Research Letters,
Vol. 31,
L10501, doi:10.1029/2004GL019782.
Zhang, X., M.A. Friedl, C.B. Schaaf and A.H. Strahler 2004. Climate Controls on vegetation phenological patterns in northern mid- and high latitudes inferred from MODIS data, Global Change Biology, Vol 10, pp. 1133-1145, 2004.
Lotsch,
A, M.A. Friedl, and J. Pinzon, 2003.
Spatio-Temporal Deconvolution of NDVI Image sequences using
independent
component analysis, IEEE Transactions on Geoscience and Remote
Sensing, Vol.
41. No. 12, pp. 2938-2942.
Schneider, A., Friedl, M.A., McIver, D.K. and C.E. Woodcock 2003. Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data, Photogrammetric Engineering and Remote Sensing, Vol 69, no. 12, pp 1377-1386.
Lotsch, A., Friedl, M.A., Anderson, B.T. and C.J. Tucker 2003. Coupled vegetation-precipitation variability observed from satellite and climate records, Geophysical Research Letters, 30(14), 1774, doi: 10.1029/2003GL017506
Yang,
R. and M.A. Friedl 2003. Modeling the effects of 3-D
vegetation
structure on surface radiation and energy balance in boreal forests, Journal
of Geophysical Research, Atmospheres, 108 (D16), 8615, doi:
10.1029/2002JD003109.
Teaching
Interests
I
teach courses in physical geography, land surface climatology and
micrometeorology, and statistical methods for environmental
science. My courses emphasize quantitative methods using
biophysical models and statistical techniques. Courses in
physical geography, land surface climatology, and micrometerology focus
on processes controlling energy and mass exchange between soils,
vegetation, and the atmosphere. Courses in statistical methods
cover topics related to analysis and empirical modeling of geophysical
and biophysical variables.