Geospatial Informatics Key to Recovering
and Sharing Historical Ecological Data
for Modern Use
Maggi Kelly, Kelly Easterday, Michelle Koo, James H. Thorne, Shruti Mukythar, and Brian Galey
Abstract
Many scientific disciplines need to locate, digitize, and inte-
grate the collections of historical ecological data that often
remain hidden in paper archives. Synthesizing historical and
contemporary ecological data with ecosystem models can help
researchers understand how species, communities, and land-
scapes are changing across space and time. Since these data
are often stored in multiple collections and in various formats,
data integration can be challenging. This paper presents a
case study of the digitization of a large historical vegetation
survey, i.e., the Wieslander Vegetation Type Mapping (
VTM
)
project in California which highlights the importance of recov-
ering and sharing such datasets. The protocol developed to
digitize, georeference, visualize, and share these data is found-
ed in geospatial concepts, and the
VTM
project showcases the
increasingly important role of geospatial experts in the fields
of ecology, history, and other sciences. These methods are
flexible, transferable and broadly applicable to other fields.
Introduction
As current and future challenges around climate change,
disease and pest management, and land cover change move
to the forefront of policy, planning and management, there is
a need to combine and synthesize diverse streams of histori-
cal and contemporary ecological data to better understand the
patterns, drivers, and consequences of species, community,
and landscape change over time (Beller
et al
., 2017; Bürgi
et
al
., 2017; Rapacciuolo
et al
., 2014). Enabling such a long-term
perspective in data analysis can be challenging because for
most of the 20
th
century, geographical and ecological records
were developed and maintained by individuals or small
academic groups, and focused in place and time (Frehner and
Braendli, 2006; Michener, 2006). This has been a standard
scientific norm but it makes multi-scale, cross-disciplinary
research more challenging because important datasets can
be difficult to find, retrieve, evaluate and use in multi-scalar
ecosystem models (Borgman, 2012; Hampton
et al
., 2013;
Jones
et al
., 2006; Morrison
et al
., 2017; Tenopir
et al
., 2011).
Working with historical ecological data to answer press-
ing contemporary ecological challenges such as climate and
land use change will require developments in data curation,
integration, and sharing. Many researchers argue that such
activities will require open technologies that facilitate sharing
including web services, open standards, and application pro-
gramming interfaces (
APIs
), which have the potential to create
emergent knowledge through novel combinations of informa-
tion (Carpenter
et al
., 2009; Kelly
et al
., 2016; McIntyre
et al
.,
2015; Peters, 2010; Tingley and Beissinger, 2009).
Methods for the reconstruction of past vegetation abun-
dance and pattern can include biological methods such as
dendrochronology or palynology (Egan,2005), but when
biological samples are not available, many researchers use
historical map and plot data, as well as other cultural refer-
ences (Beller
et al
., 2017; Grossinger
et al
.,2007; Stein
et al
.,
2010; Whipple
et al
., 2011) to understand past conditions. In
the Eastern and Midwestern United States of America there
are extensive archival records, including the General Land Of-
fice surveys and other early land surveys (Galatowitsch, 1990;
Mladenoff
et al
., 2002; Schulte and Mladenoff, 2001), but in
the North American West these data are less common and the
reconstruction of past conditions can be hampered by a rela-
tive paucity of vegetation data.
The recovery of historical geographic and ecological data
for modern use requires a suite of key concepts including
georeferencing, error and uncertainty estimation, spatial data
management, cartography and visualization, and integration
of data into spatial modeling platforms. These concepts are
well known to geospatial experts yet maybe new to other
researchers and data managers (Golledge, 2002; Goodchild,
2009). Detailed workflows underpinned by geospatial knowl-
edge and expertise are needed to ensure key concepts and
information are preserved. These workflows can then be
reproduced to make various historical data collections digital.
This “applications” article describes a case study of one im-
portant historical vegetation data collection and the protocol
and technology used to recover data and make them available
for modern ecological analyses. The case study is the Cali-
fornia Wieslander Vegetation Type Mapping (
VTM
) collection
(Wieslander, 1935).
The
VTM
collection, created in the 1920s and 1930s, has
been described as “the most important and comprehensive
botanical map of a large area ever undertaken anywhere on
the Earth’s surface” (Jepson
et al
., 2000). It was pioneered by
Albert E Wieslander, an employee of the Forest Service and
Maggi Kelly is with the Department of Environmental Science
and Policy, University of California Berkeley, 130 Mulford
Hall, #3114, Berkeley CA 94720; Geospatial Innovation
Facility, University of California Berkeley; and University of
California Division of Agriculture and Natural Resources.
Kelly Easterday is with the Department of Environmental
Science and Policy, University of California Berkeley, 130
Mulford Hall #3114, Berkeley CA 94720.
Michelle Koo is with the Museum of Vertebrate Zoology,
University of California Berkeley.
James Thorne is with the Department Environmental Science
and Policy, University of California, Davis, CA 95616.
Shruti Mukythar, and Brian Galey are with the Geospatial
Innovation Facility, University of California Berkeley.
Photogrammetric Engineering & Remote Sensing
Vol. 83, No. 11, November 2017, pp. 779–786.
0099-1112/17/779–786A
© 2017 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.83.10.779
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
November 2017
779
