Deriving the Spatiotemporal NPP Pattern in
Terrestrial Ecosystems of Mongolia Using
MODIS Imagery
Chinsu Lin and Narangarav Dugarsuren
Abstract
Net primary production (
NPP
) is a carbon cycle process that is
examined within terrestrial ecosystems. Exploring the distri-
bution of nationwide
NPP
helps to diagnose the response of
ecosystems to natural/anthropogenic influences and resource
management. Based on the
CASA
model, the
MODIS-NDVI-
derived
spatiotemporal pattern of Mongolian
NPP
was analyzed by
factorial
ANOVA
and regression analysis. Results showed that
the nationwide distribution of
NPP
was coincidence with the dis-
tribution of terrestrial ecosystems. During the growing season,
the monthly-
NPP
average of every terrestrial ecosystem behaved
temporally as an inverse-U shape that peaked in June/July
and varied as a power and logarithm function of the monthly
average temperature and precipitation respectively. The desert
had an insignificant growth of
NPP
during the growing season,
while the forest, grassland, and desert steppe had a significant
positive-growth in April/June period and then a significant
negative-growth in July/October. Interannual
NPP
showed insig-
nificant change during a five-year period.
Introduction
A number of studies concerning the global carbon cycle and
observation of the atmosphere CO
2
concentration have shown
that annual net primary production (
NPP
) has increased in the
middle and high latitudes of the Northern Hemisphere since
the 1980s (Hicke
et al.
, 2002; Fang
et al
., 2003; Nemani
et al.
,
2003; Piao
et al.
, 2005). A consequence of CO
2
fertilization,
elevated N deposition, climate change, and changes in the
forested area are likely factors resulting in the
NPP
increase
(Bhatti
et al.
, 2003; Chertov
et al.
, 2009). Mongolia is located
at the junction of Siberian taiga and central Asia desert. It has
an extreme continental climate with long, cold winters and
short summers. A significant climate shift from the humid-
ity of the North to an arid climate in the South is responsible
for green cover spatially decreasing from the North to the
South of Mongolia. The large temporal and spatial variations
of precipitation in Mongolia make it more challenging to
examine spatial-temporal variations in
NPP
and its relation-
ship to precipitation change with the aid of remote sensing.
Mongolia’s terrestrial ecosystems can be classified into forest,
grassland (steppe), desert steppe, and desert. Areas of each of
the terrestrial ecosystems are fragile and sensitive to environ-
mental changes. Mapping changes of the
NPP
spatial pattern
could help continuous exploration of how global warming
influences the production of variant ecosystems in Mongolia.
NPP
is generally defined as the difference between the total
carbon uptake from the air through photosynthesis (
GPP
) and
the carbon loss back to the atmosphere due to respiration by
living plants (R
a
) and is therefore the net carbon flux from
the atmosphere into green plants per unit time (Clark
et al.
,
2001; Girardin
et al.
, 2010; Zanotelli
et al.
, 2013). Its altera-
tion greatly affects global carbon balance and global climate
change (Nemani
et al.
, 2003), and so it is a key focus area of
study for ecologists. Primary productivity is generally deter-
mined at tree-level via complicated physiological processes
(Pidwirny, 2006). The amount of biomass produced through
photosynthesis per unit area in a given time by plants can
then be calculated in order to derive the primary productiv-
ity of a community. The amount of biomass for an individual
tree can be estimated using newly developed remote sensing
technologies, such as airborne lidar (Chen
et al.
, 2007; Gonza-
lez
et al.
, 2010; Lin
et al.
, 2011; Lo and Lin 2013), terrestrial
lidar (Ku
et al.
, 2012), and satellite imageries with very high
spatial resolution (Wang, 2010; Gonzalez
et al
., 2010; Gleason
and Im, 2012), or hyperspectral data (Jensen
et al.
2012). It
is also possible to derive the primary productivity of a plant
community or terrestrial ecosystem using inventory data by
a forest structure-based modeling technique (Tsogt and Lin,
2014) and site- or stand-based eddy covariance (EC) methods
(Schwalm
et al
., 2007; Peichl
et al
., 2010; Zanotelli
et al
.,
2013). Comparison of carbon cycle processes from years with
different seasonal and annual weather patterns at the regional
scale can give an indication of potential regional responses
and biospheric feedbacks to climate change (Turner
et al.
,
2011). Global scale satellite data with suitable spatial resolu-
tion are therefore more suitable for use in the derivation of
the primary productivity of a huge continental landscape.
NPP
is regulated by many environmental factors such as pre-
cipitation, temperature, solar radiation, and soil nutrient avail-
ability. Among these, water stress is the most critical limiting
factor determining light use efficiency (
LUE
) and vegetation
productivity in Mongolia. The significance is due to the low
precipitation and high evapotranspiration found in these areas
(Li
et al
., 2008). Constraint factors of
NPP
may vary spatially
and temporally (Nemani
et al.
, 2003). Due to Mongolia’s im-
mense size and highly variable precipitation in time and space,
logistical challenges make it difficult to monitor vegetation
conditions using field measurements. This makes it difficult
to obtain a comprehensive understanding of how ecosystems
respond to environmental factors. Remote sensing is able to
detect the response of vegetation to the integrated effects of
environmental factors and provides an opportunity to estimate
their spatiotemporal variations at regional and global scales.
A number of models, such as
CASA
,
TEM
, Century,
BIOME
3,
BIOME-BGC
, and 3
PGS
, have been developed to investigate the
magnitude and geographical distribution of
NPP
at the global
Department of Forestry and Natural Resources, National
Chiayi University, 300 University Road, Chiayi, 60004,
Taiwan
).
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 7, July 2015, pp. 587–598.
0099-1112/15/587–598
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.7.587
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2015
587
