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© The Author(s) 2020
J. Cavender-Bares et al. (eds.), Remote Sensing of Plant Biodiversity,
https://doi.org/10.1007/978-3-030-33157-3_9
Chapter 9
Using Remote Sensing for Modeling
and Monitoring Species Distributions
Jesús N. Pinto-Ledezma and Jeannine Cavender-Bares
9.1 Introduction
What drives species distributions? This is one of the most fundamental questions in
ecology, evolution, and biogeography, and it drew the attention of early naturalists
(Gaston 2009; Guisan et al. 2017). Although the question is classic and its answers
sometime seem obvious—for example, Alfred Russel Wallace recognized the effect
of geographical and environmental features on species distributional ranges
(Wallace 1860)—the answers are highly complex as a consequence of historical
evolutionary and biogeographic processes and the spatial and temporal dynamics of
abiotic and biotic factors (Soberón and Peterson 2005; Soberón 2007; Colwell and
Rangel 2009).
Here we explore the potential of satellite remote sensing (S-RS) products to
quantify species-environment relationships that predict species distributions. We
propose several new metrics that take advantage of the high temporal resolution in
Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI)
and MODIS normalized difference vegetation index (NDVI) data products.
Evaluating the potential of remotely sensed data in environmental niche modeling
(ENM) and species distribution modeling (SDM) is an important step toward the
long-term goal of improving our ability to monitor and predict changes in biodiversity globally. To achieve this, we first modeled the environmental/ecological niches
for the American live oak species (Quercus section Virentes) using environmental
variables derived from (1) interpolated climate surfaces data (i.e., WorldClim) and
J. N. Pinto-Ledezma (*) · J. Cavender-Bares
Department of Ecology, Evolution and Behavior, University of Minnesota,
Saint Paul, MN, USA
e-mail: jpintole@umn.edu
© The Author(s) 2020
J. Cavender-Bares et al. (eds.), Remote Sensing of Plant Biodiversity,
https://doi.org/10.1007/978-3-030-33157-3_9
Chapter 9
Using Remote Sensing for Modeling
and Monitoring Species Distributions
Jesús N. Pinto-Ledezma and Jeannine Cavender-Bares
9.1 Introduction
What drives species distributions? This is one of the most fundamental questions in
ecology, evolution, and biogeography, and it drew the attention of early naturalists
(Gaston 2009; Guisan et al. 2017). Although the question is classic and its answers
sometime seem obvious—for example, Alfred Russel Wallace recognized the effect
of geographical and environmental features on species distributional ranges
(Wallace 1860)—the answers are highly complex as a consequence of historical
evolutionary and biogeographic processes and the spatial and temporal dynamics of
abiotic and biotic factors (Soberón and Peterson 2005; Soberón 2007; Colwell and
Rangel 2009).
Here we explore the potential of satellite remote sensing (S-RS) products to
quantify species-environment relationships that predict species distributions. We
propose several new metrics that take advantage of the high temporal resolution in
Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI)
and MODIS normalized difference vegetation index (NDVI) data products.
Evaluating the potential of remotely sensed data in environmental niche modeling
(ENM) and species distribution modeling (SDM) is an important step toward the
long-term goal of improving our ability to monitor and predict changes in biodiversity globally. To achieve this, we first modeled the environmental/ecological niches
for the American live oak species (Quercus section Virentes) using environmental
variables derived from (1) interpolated climate surfaces data (i.e., WorldClim) and
J. N. Pinto-Ledezma (*) · J. Cavender-Bares
Department of Ecology, Evolution and Behavior, University of Minnesota,
Saint Paul, MN, USA
e-mail: jpintole@umn.edu