IEEE Geoscience and Remote Sensing Magazine - June 2021 - 111

doing this classification exercise to identify which are
the driest and most degraded lands. This helps communities
decide where to locate forest plantations and the
most suitable locations for water storage infrastructure
for better water management and development.
Eight students from a diploma course of the Andean Center
for Water Management have developed municipal or indigenous
water management plans.
Karen also utilizes NASA webtools presented by ARSET
to create flood extent and population density maps that
are shared by local governments involved with disaster response.
Fire detection maps are shared with wildfire managers
and volunteers and used for fire alerts in the Manuripi
Wildlife Reserve in coordination with the World Wildlife
Fund Pando and the Servicio Nacional de Áreas Protegidas
Bolivia; with Tunari sin Fuego and Geos Bolivia in the Cochabamba
and Chiquitania regions; in Noel Kempff Mercado
Park and its foundation; and with the Vice Ministry of
Civil Defense and indigenous peoples representatives. Data
and information alerts are then disseminated via email as
well as social networks and, later, by local authorities and
volunteers via radio, television, or other media.
CONCLUSIONS
A frequently overlooked aspect of remote sensing training
is the impact of language on learning outcomes and adoption
of geospatial technology. We have shown that multilanguage
remote sensing training delivered through the
ARSET program leads to an increase in the use of geospatial
technologies for native Spanish speakers. ARSET has a
global audience of more than 170 countries per year, and,
in 2020, 87% of participants resided outside the United
States. However, due to dedicated Spanish-language training,
nearly 30% of ARSET participants come from Spanishspeaking
countries.
Our success can be attributed to a multicultural and
multidisciplinary team of scientists, translators, and
program evaluators who have enabled the seamless integration
of remote sensing best practices in multiple languages.
Best practices include the codevelopment of inperson
and online training with regional partners; use of
geographically relevant case studies; and a rigorous endto-end,
multilingual process starting with outreach activities
at the beginning of the training cycle and concluding
with assessment and evaluation activities at the completion
of each training. We recommend that remote sensing
educational programs consider the incorporation of
multilanguage training as a means of improving their effectiveness.
With an awareness of the significant resources
required, this could involve unique institutional arrangements,
such as cost sharing of the various activities.
AUTHOR INFORMATION
Ana Prados (aprados@umbc.edu) is with the University
of Maryland Baltimore County, Baltimore, Maryland,
21228, USA.
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Erika Podest (erika.podest@jpl.nasa.gov) is with the
Jet Propulsion Laboratory, Pasadena, California, 91109,
USA.
David Barbato (barbato1@umbc.edu) is with the University
of Maryland Baltimore County, Baltimore, Maryland,
21228, USA.
Annelise Carleton-Hug (annelise@trilliumassociates
.com) is with Mill Run, Pennsylvania, 15464, USA.
Brock Blevins (brock.blevins@nasa.gov) is with Science
Systems and Applications, Inc., Lanham, Maryland,
20771, USA.
Selwyn Hudson-Odoi (shudsono@umbc.edu) is with
the University of Maryland Baltimore County, Baltimore,
Maryland, 21228, USA.
Amita Mehta (amita@umbc.edu) is with the University
of Maryland Baltimore County, Baltimore, Maryland,
21228, USA.
Cristina Karen Ovando Crespo (c.ovando@umss.edu)
is with the Universidad Mayor de San Simón Agronomía
Campus, Cochabamba, Bolivia.
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