Geographic Information Science Center of Excellence
South Dakota State University
SDSU Website
USGS Website

GIScCE Faculty
Dr. Matthew Hansen (Co-Director)
Dr. Tom Loveland (Co-Director)
Dr. Kwabena Asante
Dr. Mark Cochrane
Dr. Kevin Gallo
Dr. Geoffrey Henebry
Dr. Shuguang (Leo) Liu
Dr. David Roy
Dr. Gabriel Senay
Dr. Jim Vogelmann
Dr. Mike Wimberly
Dr. Chunsun Zhang
Dr. Zhiliang Zhu

Dr. Tom Loveland (Co-Director) is a research geographer at EROS and director of the USGS Land Cover Institute. He has been engaged in research on the use of remote sensing for land use and land cover investigations for over 25 years and has conducted studies that have spanned local to global scales. He was among the first to create continental and global-scale land cover data sets derived from remotely sensed imagery. He currently leads a USGS research team that is developing a contemporary land cover history of the United States. In addition, Dr. Loveland is leading the Landsat Continuity Mission Science Team and is a member of the NASA National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project science team. He is a member of the editorial board for the Journal of Land Use Sciences and has served in leadership roles in a number of national and international science organizations including the American Society of Photogrammetry and Remote Sensing, Climate Change Science Program, and the International Geosphere-Biosphere Programme. Dr. Loveland has published almost 90 scientific papers and has received career achievement awards from the American Society of Photogrammetry and Remote Sensing and the Association of American Geographers. Dr. Loveland has B.S. and M.S. degrees in geography from South Dakota State University and a Ph.D. in geography from the University of California, Santa Barbara.

Fig. 1. Variability in the rates and types of land cover change in selected United States ecoregions.
Fig. 2. IGBP 1km DISCover land cover data set, the first moderate resolution map of global land cover derived using remotely sensed data.


Fig. 1
Fig. 2