IEEE Geoscience and Remote Sensing Magazine - September 2019 - 121

Rafael S. Oliveira (rafaelsoliv@gmail.com) received his
bachelor's degree in life sciences and his M.Sc. degree in
ecology from Universidade de Brasília in 1996 and 1999,
respectively, and his Ph.D. degree in integrative biology
from the University of California Berkeley in 2005. He is
currently a full professor at the University of Campinas,
and his research interests include the ecology of tropical
ecosystems and understanding the factors that shape different dimensions of biodiversity.
Jan Verbesselt (jan.verbesselt@wur.nl) received his undergraduate degree in bioengineering and his Ph.D. degree in
monitoring vegetation dynamics using satellite data image time
series from the University of Leuven, Belgium, in 2006. He was
a postdoctoral researchers at the Commonwealth Scientific and
Industrial Research Organisation, Australia (2007-2010) and
continued with a European Union Marie Curie Postdoctoral
Research Fellowship at the Laboratory of Geo-information Science and Remote Sensing, Wageningen University, The Netherlands (2010-2012). He is currently an associate professor in
remote sensing at the Laboratory of Geo-information Science
and Remote Sensing, Wageningen University. He is the author
of an open source toolkit, BFAST, that provides functionality to
detect, monitor, and characterize change within satellite image
time series (available at http://bfast.r-forge.r-project.org/). Since
2012, he has been a tenure-track professor of remote sensing
at Wageningen University, coordinating research efforts on
change detection and monitoring.
Marina Hirota (marinahirota@gmail.com) received her
B.Sc. degree in applied mathematics, her M.Sc. degree in
computer engineering, and her Ph.D. degree in meteorology. She performed postdoctoral research with theoretical
ecologists and is currently an assistant professor of meteorology at the Federal University of Santa Catarina, Brazil. Her
research interests are in the field of Earth system sciences
and, specifically, in trying to understand the processes and
interactions involved in biome shifts within tropical zones
of the planet by using dynamical system concepts, such as
resilience, tipping points, and hysteresis. She is particularly
focused on searching for multiscale and synergistic mechanisms within the atmosphere and biosphere to deepen both
quantitatively and qualitatively the scientific basis of tipping points in South American tropical ecosystems.
Ricardo da S. Torres (ricardo.torres@ntnu.no) received
his B.Sc. degree in computer engineering from the University of Campinas, Brazil, in 2000 and his Ph.D. degree in
computer science from the same university in 2004. He is a
professor in visual computing at the Norwegian University
of Science and Technology, Trondheim. Previously, he was
a professor at the University of Campinas, Brazil. He has
been developing multidisciplinary eScience research projects involving multimedia analysis, multimedia retrieval,
machine learning, databases, information visualization,
and digital libraries. He is the author/coauthor of more than
200 articles in refereed journal and conferences and serves
as a program committee member for several international
and national conferences. He is a Member of the IEEE.
SEPTEMBER 2019

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

REFERENCES
[1] R. Dixon, A. Solomon, S. Brown, R. Houghton, M. Trexier, and J.
Wisniewski, "Carbon pools and flux of global forest ecosystems,"
Science, vol. 263, no. 5144, pp. 185-190, 1994. doi: 10.1126/
science.263.5144.185.
[2] R. DeFries, C. Field, I. Fung, G. Collatz, and L. Bounoua,
"Combining satellite data and biogeochemical models to
estimate global effects of human-induced land cover change
on carbon emissions and primary productivity," Global Biogeochemical Cycles, vol. 13, no. 3, pp. 803-815, 1999. doi:
10.1029/1999GB900037.
[3] R. S. Tsay, "Outliers, level shifts, and variance changes in time series," J. Forecasting, vol. 7, no. 1, pp. 1-20, Jan. 1988. doi: 10.1002/
for.3980070102.
[4] J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor,
"Detecting trend and seasonal changes in satellite image time
series," Remote Sens. Environ., vol. 114, no. 1, pp. 106-115, 2010.
doi: 10.1016/j.rse.2009.08.014.
[5] D. Topál, I. Matyasovszkyt, Z. Kern, and I. Hatvani, "Detecting
breakpoints in artificially modified- and real-life time series
using three state-of-the-art methods," Open Geosci., vol. 8, no.
1, Jan. 2016. [Online]. Available: https://www.degruyter.com/
view/j/geo.2016.8.issue-1/geo-2016-0009/geo-2016-0009.xml
[6] L. P. Dutrieux, J. Verbesselt, L. Kooistra, and M. Herold, "Monitoring forest cover loss using multiple data streams, a case study
of a tropical dry forest in Bolivia," ISPRS J. Photogrammetry Remote Sensing, vol. 107, pp. 112- 125, Sept. 2015. doi: 10.1016/
j.isprsjprs.2015.03.015. [Online]. Available: http://www.science
direct.com/science/article/pii/S0924271615000970
[7] B. DeVries, M. Decuyper, J. Verbesselt, A. Zeileis, M. Herold,
and S. Joseph, "Tracking disturbance-regrowth dynamics in
tropical forests using structural change detection and landsat
time series," Remote Sens. Environ., vol. 169, pp. 320- 334, 2015.
doi: 10.1016/j.rse.2015.08.020. [Online]. Available: http://www
.sciencedirect.com/science/article/pii/S0034425715301061
[8] B. DeVries, J. Verbesselt, L. Kooistra, and M. Herold, "Robust
monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series," Remote Sens. Environ., vol.
161, pp. 107-121, 2015. doi: 10.1016/j.rse.2015.02.012. [Online]. Available: http://linkinghub.elsevier.com/retrieve/pii/
S0034425715000656
[9] F. Detsch, I. Otte, T. Appelhans, A. Hemp, and T. Nauss, "Seasonal
and long-term vegetation dynamics from 1-km GIMMS-based
NDVI time series at Mt. Kilimanjaro, Tanzania," Remote Sens. Environ., vol. 178, pp. 70-83, 2016. doi: 10.1016/j.rse.2016.03.007
[10] T. Udelhoven, "Timestats: A software tool for the retrieval of
temporal patterns from global satellite archives," IEEE J. Sel. Topics Appl. Earth Observ. Remote Ses., vol. 4, no. 2, pp. 310-317, June
2011. doi: 10.1109/JSTARS.2010.2051942.
[11] M.-L. Wu, Y.-M. Wang, D.-C. Wong, M.-H. Hwang, and C.-M. Chu,
"Blended tools for remote sensing education," in Proc. 2007 IEEE
Int. Geoscience and Remote Sensing Symp., July 2007, pp. 2217-2219.
[12] Á. Ferrán, S. Bernabé, P. G. Rodríguez, and A. Plaza, "A webbased system for classification of remote sensing data," IEEE J.
Sel. Topics Appl. Earth Observ. Remote Ses., vol. 6, no. 4, pp. 1934-
1948, Aug. 2013. doi: 10.1109/JSTARS.2012.2230247.

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https://www.degruyter.com/view/j/geo.2016.8.issue-1/geo-2016-0009/geo-2016-0009.xml https://www.degruyter.com/view/j/geo.2016.8.issue-1/geo-2016-0009/geo-2016-0009.xml http://bfast.r-forge.r-project.org/ http://www.sciencedirect.com/science/article/pii/S0924271615000970 http://www.sciencedirect.com/science/article/pii/S0924271615000970 http://www.sciencedirect.com/science/article/pii/S0034425715301061 http://www.sciencedirect.com/science/article/pii/S0034425715301061 http://linkinghub.elsevier.com/retrieve/pii/S0034425715000656 http://linkinghub.elsevier.com/retrieve/pii/S0034425715000656

IEEE Geoscience and Remote Sensing Magazine - September 2019

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2019