Satellite observations have been widely used to examine
afforestation effects on local surface temperature at large spatial scales.
Different approaches, which potentially lead to differing definitions of the
afforestation effect, have been used in previous studies. Despite their
large differences, the results of these studies have been used in climate
model validation and cited in climate synthesis reports. Such differences
have been simply treated as observational uncertainty, which can be an order
of magnitude bigger than the signal itself. Although the fraction of the
satellite pixel actually afforested has been noted to influence the
magnitude of the afforestation effect, it remains unknown whether it is a key
factor which can reconcile the different approaches. Here, we provide a
synthesis of three influential approaches (one estimates the actual effect
and the other two the potential effect) and use large-scale afforestation
over China as a test case to examine whether the different approaches can be
reconciled. We found that the actual effect (ΔTa) often relates
to incomplete afforestation over a medium-resolution satellite pixel (1 km).
ΔTa increased with the afforestation fraction, which explained
89 % of its variation. One potential effect approach quantifies the impact
of quasi-full afforestation (ΔTp1), whereas the other
quantifies the potential impact of full afforestation (ΔTp2) by
assuming a shift from 100 % openland to 100 % forest coverage. An
initial paired-sample t test shows that ΔTa<ΔTp1<ΔTp2 for the cooling effect of
afforestation ranging from 0.07 to 1.16 K. But when all three methods are
normalized for full afforestation, the observed range in surface cooling
becomes much smaller (0.79 to 1.16 K). Potential cooling effects have a
value in academic studies where they can be used to establish an envelope of
effects, but their realization at large scales is challenging given its
nature of scale dependency. The reconciliation of the different approaches
demonstrated in this study highlights the fact that the afforestation
fraction should be accounted for in order to bridge different estimates of
surface cooling effects in policy evaluation.
Introduction
Afforestation has been and is still proposed as an effective strategy to
mitigate climate change because forest ecosystems are able to sequester
large amounts of carbon in their biomass and soil, slowing the increase of
atmospheric CO2 concentration (Fang et al., 2014; Pan et al., 2011).
Additionally, forests regulate the exchange of energy and water between the
land surface and the lower atmosphere through various biophysical effects,
including radiative processes such as surface reflectance and non-radiative
processes such as evapotranspiration and sensible heat flux (Bonan, 2008;
Juang et al., 2007). As the net result of the surface energy balance, land
surface temperature (LST) is widely used to measure the local climatic
impact of afforestation (Li et al., 2015; Winckler et al., 2019a).
Climate model simulations and site-level observations have been utilized to
explore the impact of forest dynamics on land surface temperature (Lee et
al., 2011; Pitman et al., 2009; Swann et al., 2012). However, afforestation
impacts on local LST derived from models tend to be highly uncertain as they
are limited by the coarse spatial resolution of models and uncertainties in
model parameters and processes (Oleson et al., 2013; Pitman et al., 2011),
while insights from site-level assessments cannot be extrapolated to large
spatial domains (Lee et al., 2011). Alternatively, remote-sensing-based LST
products enable the assessment of local LST changes due to forest dynamics
on large spatial scales (Li et al., 2015; Shen et al., 2019).
A number of studies investigated the surface temperature impact of
afforestation based on satellite observations, and they have been cited in
high-level climate science synthesis reports (e.g., IPCC Special Report on Climate and Land, authored by Jia et al.,
2019), even though there are large differences in afforestation impacts on
LST between different methods. For example, Alkama and Cescatti (2016)
found a cooling effect of about 0.02 K from afforestation in temperate
regions, while Li et al. (2015) reported a 0.27±0.03 K potential
cooling from afforestation in the northern temperate zone (20–50∘ N) based on the “space-for-time” method. In contrast, Duveiller et al. (2018) found a much stronger potential cooling effect of 2.75 K for
afforestation in the northern temperate region. While such differences were
acknowledged in a recent modeling study (Winckler et al., 2019b), they were
simply treated as observational uncertainty for climate model evaluation,
with the uncertainty range being as big as, or even an order of magnitude
larger than, the afforestation effect. It remains unclear whether the
differences arising from these different methods can be reconciled.
Until now, studies using satellite data to investigate afforestation impact
on surface temperature have mainly focused on three methods. The first
method, termed the “space-and-time” approach (Fig. 1, red box), aims to
examine the actual, realized effect of afforestation (“actual effect”) by
isolating the forest-cover-change effect from the gross temperature change
over time in places where forest-cover change actually occurred (Alkama and
Cescatti, 2016; Li et al., 2016a). The second method, termed the
space-for-time approach (Fig. 1, orange box), compares the surface
temperature of forest with adjacent “openland” (i.e., cropland or grassland)
under similar environmental conditions (e.g., background climate and
topography) and estimates the potential effect of afforestation if
afforestation were to occur (Ge et al., 2019; Li et al., 2015; Peng et al.,
2014). Note that such effects are often quantified using medium-resolution
land-cover datasets (typical resolution = 1 km), which do not necessarily
represent 100 % ground coverage, and we therefore term such a potential
effect a “mixed potential effect”.
Illustration of the three approaches to quantifying the
local surface temperature effect of afforestation. Panels (a) and (b) represent two
nearby pixels, both classified as openland at time t1 by
medium-resolution satellites (1 km spatial resolution), with one of them
classified as forest at time t2 (i.e., having experienced afforestation)
and the other unchanged. Note that neither of these pixels will have 100 %
complete coverage of either openland (i.e., grassland or cropland) or
forest, but they will have been classified as either openland or forest by
medium-resolution satellite products. Panels (c) and (d) represent pixels with
100 % forest or 100 % openland coverage whose temperature can be derived
from pixels of mixed land-cover types by using the singular value
decomposition (SVD) technique (Duveiller et al., 2018). The dotted red box
describes the quantification of the actual effect of afforestation
(ΔTa) occurring from t1 to t2 by the space-and-time
method. The orange box represents the mixed potential effect determined by
hypothesizing potential shifts between openland and forest based on the
space-for-time approach (ΔTp1). The green box represents the
full potential effect of afforestation (ΔTp2) derived by
hypothesizing a transition from 100 % complete openland coverage to
100 % complete forest coverage.
The third method, recently used by Duveiller et al. (2018), uses the
“singular value decomposition” (SVD) technique (Fig. 1, green box), which is
claimed to extract the hypothetical LST for different land-cover types by
assuming a 100 % coverage of the target cover type. The afforestation
effect on LST is then quantified as the difference between the LST of a
pixel with a hypothetical 100 % forest coverage and the LST of an adjacent
pixel with 100 % openland coverage. As with the second method, such an
approach quantifies the potential effect of afforestation, but in this
case, it quantifies the “full potential effect” by assuming transitions
between land-cover types with 100 % complete ground coverage.
Previous studies have revealed the fraction of forest change as an important
factor determining the magnitude of the afforestation effect. Alkama and
Cescatti (2016) indicated that the actual temperature effect is
fraction-dependent, and Li et al. (2016a) pointed out that use of a higher
threshold to define forest change resulted in a stronger potential effect.
Nonetheless, whether the fraction of forest change can explain the
differences in the afforestation effect produced by different methods, e.g.,
whether the potential effect can be “actualized”, has not been
demonstrated. Testing the role of afforestation fraction in reconciling the
afforestation effects produced by different methods can help clarify
potential confusion and contribute to appropriate policy evaluation.
This study develops detailed conceptual and methodological descriptions for
each of the three approaches and uses large-scale afforestation over China
as a case study to compare the three approaches. We tested the following
hypotheses. (1) The actual effect on LST increases with the area that has
actually been afforested, defined as afforestation intensity (or Faff).
(2) The actual effect is smaller than the potential effects. (3) When
extending Faff to a hypothetical value of 100 %, the actual effect
approaches the potential effect. If proven, this third hypothesis implies
that the LST impacts from different approaches could be reconciled given the
same boundary condition of full afforestation. In that case, we then have a
fourth hypothesis (4) stating that changes in underlying biophysical
processes including radiation and sensible and latent heat fluxes that drive
LST changes should also be reconciled among different methods. To keep the
focus on reconciling methodological differences, only changes in the daytime
surface temperature were considered in this study. Nevertheless, similar
conclusions regarding the different approaches are expected for nighttime
surface temperature.
MethodThree approaches to quantifying the impacts of afforestation on LSTActual effect of afforestation (ΔTa)
The space-and-time approach assumes that the gross change in land surface
temperature (ΔT) over a given time period during which afforestation
occurred contains both signals of temperature change due to afforestation
(ΔTa) and background temperature variation (ΔTres)
due to changes in large-scale circulation patterns (Alkama and Cescatti,
2016; Li et al., 2016a):
ΔT=ΔTa+ΔTres,
where ΔT is the gross temperature change during the period from
t1 to t2 for the pixel under study. ΔT can be calculated as
the difference between LSTt2 and LSTt1, with LSTt2 being the
surface temperature after afforestation and LSTt1 being that before
afforestation. It thus follows that
ΔTa=ΔT-ΔTres.ΔTres can be approximated by averaging changes in surface
temperature for those pixels adjacent to the target afforestation pixel for
which the forest cover remained constant between t1 and t2 (i.e.,
Faff=0 %; Sect. 2.2.3). Here, pixels with Faff>0 % were defined as afforestation target pixels. A searching window of 11 km × 11 km was established, centered on the afforestation pixel. Within
this window, pixels with Faff=0 % were defined as control pixels
and were used to derive ΔTres. Afforestation pixels and
adjacent control pixels were both determined based on the net forest change
between t1 and t2 using Global Forest Change (GFC) data (Fig. 2;
Sect. 2.2.3).
Schematic overview of the processing steps. The different
output results correspond to the actual effect (ΔTa), mixed
potential effect (ΔTp1), and full potential effect of
afforestation (ΔTp2).
Mixed potential effect (ΔTp1)
The space-for-time method relies on the “space-substitute-for-time”
assumption to obtain the potential impact of afforestation on local
temperature (Zhao and Jackson, 2014). By assuming that forest and openland
share the same environmental conditions (background climate, topography,
etc.) within a small spatial domain, the potential temperature effect of
afforestation is examined using the search window method with a window size
of up to 40 km × 40 km (Ge et al., 2019; Li et al., 2015; Peng et al.,
2014). Here, to be consistent with our actual effect approach, a more
conservative window size of 11 km × 11 km was used, smaller than that
used in the majority of previous studies (Ge et al., 2019; Li et al., 2015;
Peng et al., 2014). In most previous studies, existing medium-resolution
(1 km) land-cover maps were used directly. Such land-cover products rely on
certain thresholds to classify satellite pixels into discrete land-cover
types. Given the widespread spatial heterogeneity in land-cover
distribution, it is to be expected that only in rare cases will these
medium-resolution pixels have 100 % coverage of a given land-cover type.
Therefore, when determined in this way, the potential effect of
afforestation has been named the mixed potential effect, in contrast to
the full potential effect, on which we will focus in the next section,
which assumes a potential transition between land-cover types of 100 %
coverage.
To ensure consistency with the land-cover data used in the full potential effect approach (i.e., the singular value
decomposition method), the GlobeLand30 land-cover map was
aggregated from its original resolution (30 m) to 1 km resolution. The
land-cover type assigned to a given 1 km pixel during aggregation was based
on the land-cover type with an area fraction >50 % within that
pixel, to be consistent with the rationale behind the generation of
medium-resolution land-cover products (Sect. 2.2.3). A 1 km forest pixel
was then chosen as the target pixel and put at the center of a search window
with dimensions 11 km × 11 km. The mixed potential effect of
afforestation (ΔTp1) was defined as the difference between the
temperature of the target pixel (LSTF) and the average temperature of
all the surrounding openland pixels within the window (LSTO′‾):
ΔT=ΔTp1-LSTO′‾,
where LSTF is the surface temperature of the target forest pixel
at t2, and LSTO′ represents the elevation-corrected
surface temperature of openland pixels at t2 within the search window.
Given our search window size, ΔTp1 could be biased by the
elevation difference between the target forest pixel and surrounding
openland pixels. Therefore, a linear relationship was first fitted between
the observed openland temperature, LSTO, and the elevation of the
openland pixel (EleO). This fitted temperature lapse rate (k) was then
used to derive elevation-corrected openland temperature LSTO′:
LSTO′=LSTO+k×ΔEleF-O,
where ΔEleF-O is the elevation difference between forest and
openland pixels. The elevation is available from NASA's Shuttle Radar
Topography Mission (SRTM) dataset
(https://lpdaac.usgs.gov/products/srtmgl1v003/, last access: 23 December 2022).
Full potential effect (ΔTp2)
The full potential effect represents the temperature change due to
hypothesizing a shift from 100 % openland to 100 % forest coverage and
was determined here by employing the singular value decomposition (SVD)
method used in Duveiller et al. (2018). The SVD technique assumes that the
temperature observed for a pixel at 1 km scale is a linear composition of the
temperatures of different land-cover types at a finer resolution (in our
study at a 30 m resolution). For each 1 km pixel, the observed temperature can
be written as the composition of the temperature of each component
land-cover type and its corresponding fraction, based on the land-cover
fractions derived from the 30 m resolution GlobeLand30 map (Sect. 2.2.3).
The temperature of each type of land cover was assumed constant within a
search window of 11 km × 11 km. For each given search window, the
following equations can be obtained:
y1⋮yn=x11…x1m⋮⋱⋮xn1⋯xnm×β1⋮βm,
where n is the total number of 1 km pixels within the window, after
accounting for the elevation difference (thus the maximum value of n is 121
given our 11 km × 11 km search window), m is the number of land-cover
types, xij refers to the fraction of land-cover type j in pixel i, and βi refers to the temperature of land-cover type i. To minimize elevation
impacts, the linear regression relationship for a given 1 km pixel was
included only when the elevation difference between this pixel and the
central pixel of the search window was smaller than 100 m. Using matrix
notation, Eq. (5) can be simplified to
y=X×β,
where the matrix X contains land-cover fraction for the n 1 km pixels as an
explanatory variable, the response variable y contains n LST observations, and
the coefficient vector, β, contains the regression coefficients which
show temperatures of different land-cover types. Note that this linear
equation system cannot be easily solved because the matrix X is “closed”;
i.e., by definition, the elements in each row of the matrix X add up to 1.
After removing the mean of each column (Zhang et al., 2007), the matrix X
was transformed by applying the SVD technique to another matrix, Z, of
reduced dimension (more details in Duveiller et al., 2018). After this
transformation, we have the following:
y=Z×β′+ε
in which the β′ coefficient can be obtained from Eq. (8):
β′=ZtZ-1Zty.
However, the β′ vector calculated from the transformed matrix Z
cannot directly provide surface temperatures for corresponding land-cover
types. To obtain temperatures for each land-cover type by assuming 100 %
ground coverage, an identity matrix Y with its dimension equal to the number
of land-cover types must be constructed to represent the hypothetical case
in which each 1 km pixel was 100 % covered by a single land-cover type. The
same transformation as applied to the matrix X was then applied to Y, to
obtain a transformed matrix Z′. Finally, the predicted
temperature (LST100%′) for each land-cover
type assuming a 100 % coverage is calculated as
LST100%′=Z′β′.
For the central pixel of the local search window, ΔTp2 is
defined as the difference between the predicted LST100%′ for forest (LST100%F′) and
openland (LST100%O′).
ΔTp2=LST100%F′-LST100%O′
More details, including an illustration of the SVD method, can be found in
Fig. 7 in Duveiller et al. (2018).
At the scale of the searching windows used in this analysis (11 km × 11 km), any nonlocal effects cancel out when comparing temperature
differences over neighboring areas because the effects of advection and
atmospheric circulation have been reported to be similar for adjacent areas
(Pongratz et al., 2021; Winckler et al., 2019a). Hence the quantified
afforestation effect for each of the three methods can be considered to be
the local effect only.
Dataset and processingThe test case: large-scale afforestation over China
China was selected as the test case for addressing the important
methodological issues in quantifying land surface impacts of afforestation
because afforestation is a key national strategy for sustainable development
and climate mitigation (Bryan et al., 2018; Qi and Wu, 2013). According to
the 8th National Forest Inventory conducted in 2013, China's
afforestation area has reached 69×106 ha, accounting
for 33 % of the total global afforestation area (Chen et al., 2019).
Afforestation in China during 2000–2012 occurred mainly in regions with
more than 400 mm of precipitation per year (Fig. 3a), which is considered a
threshold below which there is a high risk of afforestation failing due to
water limitation (Mátyás et al., 2013). China covers a wide range of
latitude from 3.9 to 53.6∘ N, and its forest
ecosystems cover an elevation range of 100 to 4000 m. This wide range of
climate zones, from tropical/subtropical to temperate and boreal, makes it
highly suitable for our methodological analysis because the impact of
afforestation on LST might differ with latitude and background climate (Lee
et al., 2011; Alkama and Cescatti, 2016). Further justification for using
China as a test case comes from the strongly diverging published LST impacts
of afforestation there, which range from an actual effect of -0.0036 K per decade by Li et al. (2020) to a potential effect of -1.1 K by Peng et
al. (2014).
(a) Spatial distribution of afforestation intensity
(Faff) in China during 2000–2012. The solid black line crossing China
is the 400 mm annual precipitation isoline. (b) Frequency distribution of
Faff and cumulative afforestation area with the increase in Faff.
The dashed red line represents the cumulative afforestation area
corresponding to Faff=10 %.
MODIS dataset and preparation
In this study, the actual effect was estimated by combining the actual
satellite-derived afforestation for 2000 to 2012 (see Sect. 2.2.3) with
satellite-based estimates of biophysical variables for the periods
2002–2004 (t1) and 2010–2014 (t2). MODIS remote sensing products
for land surface temperature (MOD11A2), albedo (MCD43B3), and
evapotranspiration (MOD16A2) were used to characterize the biophysical
effects (Table 1). The datasets were regridded to harmonize with spatial
(1 km) and temporal (annual) resolutions (Table 1).
Summary of the datasets and their main characteristics.
TypeDatasetSelected bandResolutionProjectionTime spanForest changeGlobal forest changeForest gain; loss year30 m, annualWGS842000–2012Land-cover typeGlobeLand 30Land-cover type30 m, –UTM2000; 2010Land surface TemperatureMOD11A2Daytime temperature1 km, 8 dsinusoidal2002–2004; 2010–2014AlbedoMCD43B3Albedo WSA shortwave1 km, 16 dsinusoidal2002–2004; 2010–2014Incoming shortwave radiationCERESsfc_sw_down_all_mon1∘, monthlyWGS842002–2004; 2010–2014Surface broadband emissivityMOD11C3Emis_29; Emis_31; Emis_320.05∘, monthlysinusoidal2002–2004; 2010–2014EvapotranspirationMOD16A2ET_500m500 m, 8 dsinusoidal2002–2004; 2010–2014ElevationSRTM30Be7530 m, –WGS84–
The MOD11A2 product provides 8 d land surface temperature for 10:30 and
22:30 from the Terra satellite, but here we focused on daytime surface
temperature. Only valid LST observations from the original data were used to
compute the average daily values for a given year. Years for which more than
40 % of daily data are missing were excluded from the analysis. Annual
data were then aggregated to obtain the average annual temperature for
periods t1 and t2.
The MCD43B3 product provides white-sky and black-sky shortwave albedo at
16 d temporal resolution (Table 1). The observed white-sky albedo was used
as the daytime albedo (Peng et al., 2014). For evapotranspiration (ET), we
used the ET band in MOD16A2, which includes water fluxes from soil
evaporation, wet canopy evaporation, and plant transpiration. To calculate
the mean annual albedo and evapotranspiration for 2002–2004 (t1) and
2010–2014 (t2), we used the same approach as used for LST.
Land-cover datasets and processing
Two land-cover datasets were used in this study: the actual effect
approach was based on the Global Forest Change (GFC) dataset, while the
mixed potential effect and full potential effect used the GlobeLand30
land-cover data (Table 1).
The SVD technique, used in the full potential effect approach, requires a
land-cover map with a higher spatial resolution than the 1 km spatial
resolution of the LST data. The GlobeLand30 product, which is based on
Landsat images, provides land-cover information for China at a 30 m
resolution for the years 2000 and 2010 (Chen et al., 2015). Cultivated land
and grassland in GlobeLand30 were classified as openland. Discrete
land-cover type information at 30 m resolution in 2010 was aggregated to
obtain the area fractions of the different land-cover types at 1 km
resolution, which were then used to construct matrix X in Eq. (5) (Fig. 2).
Furthermore, land-cover type information at the 1 km scale was extracted,
based on the vegetation type with area fraction >50 % for
every 1 km × 1 km window. These data were then applied in the
space-for-time method to identify forest and openland (Fig. 2).
GlobeLand30 data are not suitable for detecting forest change (Zeng et al.,
2021). The Global Forest Change (GFC) data, however, provide forest gain
and forest loss at a spatial resolution of 30 m between 2000 and 2012 and have
been used for mapping global forest change (Hansen et al., 2013). This
product shows an overall accuracy of greater than 99 % for areas of forest
gain at the global scale when compared with statistical data reported in
Forest Resource Assessment (FRA), lidar detection (Geoscience Laser
Altimeter System), and MODIS NDVI time series (Hansen et al., 2013) and
thus has been recommended for use in forest and forest-change estimates
(Chen et al., 2020; Zeng et al., 2021). Using this dataset, forest loss
events were identified for each year between 2000 and 2012, but forest gain
was only identified for the whole period, simply because forest loss is an
abrupt change which can be effectively identified over short time periods,
whereas forest gain is a gradual change which can only be confidently
identified over longer time spans. Here, forest losses and gains from GFC
were aggregated at a 1 km resolution to obtain net forest change (defined as
forest gain minus forest loss) during this period (Fig. 2). A positive net
change indicates afforestation, and the area percentage of afforestation for
the 1 km pixel area was defined as Faff. The land-cover type of pixels
with Faff=0 % was considered to be stable. This net forest-change
information was then used in the calculation of the actual
afforestation-induced temperature effect (ΔTa) (Fig. 2).
Decomposition of changes in surface temperature
Changes in surface temperature following forest-cover change are the net
result of changes in underlying fluxes that collectively determine the land
surface energy balance:
ΔSWin-ΔSWout+ΔLWin-ΔLWout=ΔH+ΔLE+ΔG,
where ΔSWin, ΔSWout, ΔLWin, and ΔLWout are the changes in incoming and outgoing shortwave and longwave
radiation, respectively, and ΔH, ΔLE, and ΔG are
changes in sensible heat flux, latent heat flux, and ground heat flux,
respectively. All the terms of Eq. (11) are expressed in watts per square meter (W m-2).
Firstly, it can be reasonably assumed that ΔSWin≈0 and
ΔLWin≈0, given that all three approaches consider only
local effects on surface temperature by following a comparison of target
pixels with surrounding control pixels, thus excluding feedbacks from, e.g.,
cloud formation (Duveiller et al., 2018). Changes in reflected shortwave
radiation can be derived as
ΔSWout=SWin×Δα,
where SWin is available from the CERES EBAF surface product Ed 4.1
(Kato et al., 2018; Liu et al., 2018) (Table 1), and Δα is
the surface albedo change. To approximate ΔLWout, we used its
first-order differential equation:
ΔLWout=σ(4εBT3ΔT+ΔεBT4),
where σ is Stefan–Boltzmann's constant (5.67×10-8 W m-2 K-4), T is daytime surface temperature, and ΔT is the
afforestation impact on surface temperature. Surface broadband emissivity,
εB, is usually obtained from an empirical relationship
(Zhang et al., 2020):
εB=0.2122ε29+0.3859ε31+0.4029ε32,
where ε29, ε31, and ε32
are obtained from the estimated emissivity for bands 29 (8400–8700 nm),
31 (10 780–11 280 nm), and 32 (11 770–12 270 nm) in the MOD11C3 data
(Duveiller et al., 2018).
Latent heat flux change (ΔLE) refers to changes in the energy used
for evapotranspiration (ET, unit: mm d-1), which can be obtained from
the change in evapotranspiration (ΔET):
ΔLE=ΔET×28.94Wm-2(mmd-1)-1.
Therefore, the sum of sensible heat change and ground heat change (ΔH+ΔG) can be calculated as the difference between net radiation
change and latent heat flux change (ΔLE) based on Eq. (11). The
afforestation effects on albedo (Δα), εB
(ΔεB), and ET (ΔET) needed in the above
equations were calculated in a similar way to ΔT for each of the
three different approaches as described in Sect. 2.1.
Statistical analysis
The spatial distributions of original samples for the three methods are
different because of the different land-cover maps used (Figs. 2 and A1 in Appendix A),
and, therefore, the statistical analysis was limited to those pixels shared
by all three approaches: 96 058 sample pixels at 1 km resolution. The
distribution of these shared sample pixels retained the characteristics of
the spatial distribution of the original samples (Fig. A2).
Differences in the afforestation effects on LST of the three approaches were
tested by performing paired-sample t tests between pairs of approaches. The
paired-sample t test was used, rather than a normal t test, to avoid the bias
due to strong spatial heterogeneity in the LST effects of afforestation that
could occur if the values of all pixels had been pooled together for a
normal t test. The test was done using the “ttest_rel” method
from the “scipy.stats” package in Python. The Bonferroni correction was
applied to adjust the significance level (p value) to mitigate the
increasing type I error when doing multiple paired-sample t tests, which in
our case involves three pairs (Lee and Lee, 2018; UC Berkeley, 2008). The
Bonferroni correction sets the significance cutoff at α/k (with
α as the p value before correction and k as number of pairs). In
this study, with three hypotheses tests (i.e., three pairs) and an original
significance level α=0.05, the adjusted p value is 0.0167. In
order to investigate ΔTa in relation to the afforestation
intensity, a linear regression was performed between ΔTa and
Faff using the ordinary least-squares method.
ResultsSpatial distribution of afforestation and its effect on land surface temperature
In China, afforestation areas are mainly located in the northeast, southwest,
and south, where sufficient precipitation is available (Fig. 3a) and largely
driven by afforestation of former cropland or abandoned cropland, with a
relatively small contribution from forest regeneration or replanting
following natural disturbance or timber harvest. One prominent feature of
afforestation in China is its small afforestation patch, with most
afforested pixels (1 km2) having an afforestation fraction of less than
30 % (Fig. 3b). Pixels with an afforestation intensity below 10 %
account for 93 % of the total number of pixels (Fig. 3b), representing
0.14×106 ha, more than half (55.6 %) of the total afforestation area (Fig. 3b).
Although all three approaches result in similar spatial and latitudinal
patterns regarding afforestation effects on LST (Fig. 4), their magnitudes
differ substantially. The actual effect has the lowest temperature change,
followed by the mixed potential effect, with the full potential effect
showing the greatest temperature change (Fig. 4a–c). For the latitude range
of 20–36∘ N where afforestation effects show a dominant cooling
effect, the full potential effect (ΔTp2) reaches -1.75±0.01 K, while the mixed potential effect (ΔTp1) was smaller at
-0.96±0.00 K, but both of them were much larger than the actual effect
(ΔTa) of -0.09±0.00 K. Similarly, the full potential
effect (ΔTp2) showed the strongest warming effect (0.35±0.01 K) in the area north of 48∘ N, stronger than the mixed
potential effect (0.22±0.01 K), and again the actual effect is the
smallest (0.07±0.01 K). However, regarding the latitude where the
effects change from a warming to cooling effect, the three approaches
largely converge (Fig. 4d). Between 40 and 48∘ N, the
afforestation effects are largely neutral, with the mean temperature change
for the three approaches being 0.07±0.01 K (ΔTa=-0.01±0.01 K; ΔTp1=0.11±0.01 K;
ΔTp2=0.12±0.01 K).
Afforestation effects on LST quantified by three
approaches: (a) actual effect based on a space-and-time approach (ΔTa), (b) mixed potential effect based on a space-for-time approach
(ΔTp1), and (c) full potential effect assuming a transition from
100 % openland coverage to 100 % forest coverage using the SVD method
(ΔTp2). The solid black line crossing China is the 400 mm
precipitation isoline. (d) Zonal averages of the annual mean daytime LST
change within 2∘ latitudinal bins, with shaded areas representing
the standard errors (SE). Note that in (d), ΔTa
corresponds to the vertical axis on the left; ΔTp1 and ΔTp2 correspond to the vertical axis on the right.
Reconciling temperature effects of afforestation
Even though the observed land surface temperature is assumed to be uniform
for the 1 km afforested satellite pixel, the underlying afforestation
intensity varies substantially (Fig. 3a). This leads to our first hypothesis
that for a 1 km pixel, ΔTa should be influenced by the area
fraction that has been afforested within the pixel (i.e., afforestation
intensity or Faff). Indeed, the actual daytime surface cooling
increases significantly with afforestation intensity (Fig. 5), with a
0.079±0.017 K (mean ± SD) increase for each 10 %
increase in Faff.
Changes in ΔTa as a function of
afforestation intensity (Faff), defined as the fraction of afforested
area to the total pixel area at a 1 km resolution. Error bars indicate the
standard error of ΔTa within each 10 % bin of Faff.
The red line represents the fitted linear regression line between ΔTa and Faff.
Comparison of ΔT for the three approaches for
bins of afforestation intensity. Error bars are given as the standard error,
and different letters indicate that ΔT calculated by the two
approaches concerned are significantly different using the adjusted p value
after applying the Bonferroni correction for multiple paired-sample t tests.
The afforestation effects obtained from the three approaches were compared
for each Faff interval (Fig. 6). When afforestation intensity is less
than 60 %, significant differences exist in the temperature change
obtained by the three approaches, with ΔTa<ΔTp1<ΔTp2. This result confirms our second
hypothesis that the actual effect is expected to be smaller than potential
effects. However, for pixels with relatively low Faff, the mixed
potential effect is found to be smaller than the full potential effect,
which is reasonable but, to our knowledge, has not been reported before.
When the afforestation intensity is greater than 60 %, the significant
difference in cooling effect between the different approaches disappears,
likely because afforestation intensity and the associated forest coverage
at 1 km resolution reach values high enough to allow the potential effects
to be realized.
When considering the overall differences in ΔT for the three
approaches, irrespective of the afforestation intensity, ΔTa
(-0.07±0.00 K) over China was significantly lower than ΔTp1 (-0.63±0.00 K), which is further significantly lower than
ΔTp2 (-1.16±0.01 K) (p<0.05, paired-sample t test, n= 96 058), once again confirming our second hypothesis (Fig. 7).
Moreover, extrapolation of the relationship shown in Fig. 5 suggests that
ΔTa would reach -0.79±0.17 K (mean ± SD) if a 1 km
pixel was 100 % afforested, which is conceptually comparable to the
potential effects. ΔTa was indeed found to be higher than
ΔTp1 but lower than ΔTp2. This result confirms our
third hypothesis and demonstrates that the potential cooling effect could
indeed be reached when a pixel is fully afforested.
Comparison of ΔT for the three approaches,
irrespective of the afforestation intensity. Error bars are given as the
standard error, and different letters indicate ΔT being significantly
different (p=0.0167, paired-sample t test, n= 96 058). For
comparison, the predicted ΔTa with Faff reaching 100 %,
which is conceptually comparable with ΔTp1 and ΔTp2, is also shown.
Reconciling changes in surface energy fluxes by afforestation
In order to investigate whether the underlying surface energy fluxes could
be reconciled following the reconciliation of the LST changes, changes in
surface energy fluxes due to afforestation were quantified using each of the
three approaches, under the same boundary conditions as for full
afforestation (i.e., changes following the actual effect approach were
extended for Faff=100 %). As illustrated in Fig. 8, changes in
all the relevant surface energy fluxes under the three different approaches
have the same direction, with similar magnitudes, confirming the
reconciliation of the different approaches in terms of surface energy
fluxes. More specifically, the three approaches converge on a reduction in
reflected shortwave radiation (ΔSWout) of 0.56–1.23 W m-2 due to the lower albedo of forest compared to openland (Fig. A3). Emitted longwave radiation (ΔLWout) was reduced by
1.03–3.10 W m-2, and sensible and ground heat fluxes
(ΔH+ΔG) reduced by 4.84–6.14 W m-2.
All these reduced fluxes were offset by an increased latent heat flux of
7.99–8.41 W m-2 (ΔLE), the single energy flux
leading to surface cooling.
Afforestation-induced changes in surface energy fluxes
(W m-2) following the three approaches: (a) the actual effect based on a
space-and-time approach, (b) the mixed potential effect using
medium-resolution land-cover maps based on a space-for-time approach, and
(c) the full potential effect assuming a transition from 100 % openland
coverage to 100 % forest coverage using the SVD method. For each approach,
changes were calculated for the reflected shortwave radiation (SWout),
outgoing longwave radiation (LWout), latent heat flux (LE), and the
combination of sensible and ground heat fluxes (H+G). No changes were
assumed for incoming shortwave and longwave radiation. Changes in energy
fluxes for the actual effect approach have been adjusted to the condition
of full afforestation (i.e., Faff=100 %) in a similar way as for
the predicted ΔTa in Fig. 7, by fitting linear regressions
between energy flux variables and Faff (Fig. A4).
Discussion
The three approaches (Li et al., 2015; Alkama and Cescatti, 2016; Duveiller
et al., 2018) used to quantify local surface temperature change following
forest-cover change and presented in detail in this study have been
cited over 919 times in research papers (Web of Science, December 2021) and
in high-level climate science synthesis reports. Despite the apparently
large differences in temperature effect among them, to our knowledge, no
studies have examined whether these differences can be reconciled. This
study fills that gap by comparing the three approaches for a single study
case, i.e., large-scale afforestation in China. China is highly suitable for
the purpose of this study as the size of an afforestation patch is, in
general, smaller than the spatial resolution (1 km) at which the temperature
effects of afforestation were conducted in the previous studies describing
the three approaches (Li et al., 2015; Alkama and Cescatti, 2016; Duveiller
et al., 2018). Hence, the difference between the actual and potential
temperature effects is expected to be large.
Indeed, we found surface cooling following afforestation was much less when
estimated as the actual effect (ΔTa) compared to the potential
effects (ΔTp1 and ΔTp2). This lower ΔTa has been attributed to incomplete afforestation at a 1 km
resolution, at which potential effects are quantified by assuming complete
afforestation (i.e., a complete shift from openland to forest). Consistent
with our first hypothesis, the afforestation fraction at a 1 km resolution
explained 89 % of the variation in ΔTa, making it a key
determinant of the surface cooling following afforestation (Fig. 5). This
result is consistent with the fact that the observed temperature for a mixed
surface is determined by the area fractions of its respective components,
with each component having a unique temperature. This fact also forms the
theoretical foundation for the SVD technique used to derive the full
potential effect (Duveiller et al., 2018).
Modeling (Li et al., 2016b) and satellite-based (Alkama and Cescatti, 2016)
studies have found that temperature change after afforestation (or
deforestation) is highly sensitive to the fraction of the model grid cell or
satellite pixel that is subjected to afforestation (or deforestation),
echoing our finding that ΔTa significantly changes with
Faff. In addition, we provide strong evidence in support of our third
hypothesis that when Faff reaches 100 %, the expected actual effect
is comparable to the potential effects (Fig. 7). This finding shows that the
three approaches compared here are consistent when the same boundary
condition, i.e., full afforestation, is applied and demonstrates that all
three methods are mutually compatible. It is, therefore, the basis of the
reconciliation of the three approaches. It also highlights the fact that the
actual afforestation area must be considered when evaluating the climate
mitigation effects of afforestation.
Our results also show that the mixed potential effect (ΔTp1) is
smaller than the full potential effect (ΔTp2) (Figs. 6, 7).
We suspect that this phenomenon likely also relates to the incomplete forest
coverage for the identified forest pixels at the 1 km scale used in the
space-for-time analysis because a threshold value of 50 % forest cover
was used when upscaling the 30 m land-cover map to 1 km resolution. This
threshold, however, is consistent with the commonly applied value in
land-cover classification based on medium-resolution satellite images, such
as MCD12Q1, which uses a tree coverage value of 60 % to identify forest
pixels (Sulla-Menashe et al., 2019). For the purpose of comparison, we
also calculated the mixed potential effect based on the MCD12Q1 land-cover
map but using the same LST data. The result shows that potential effects
derived using MCD12Q1 data versus those derived using spatially upscaled
GlobeLand30 data are almost identical (Fig. A5), lending credibility to our
estimated ΔTp1 in comparison to previous studies using MODIS
land-cover data (Li et al., 2015). Progressively increasing the forest-cover
threshold from 50 % to 90 % steadily increases ΔTp1 from
-0.62±0.02 to -0.75±0.02 K (Fig. A6). Further increasing the
thresholds used to identify 1 km resolution openland pixels from 50 % to
90 % increases ΔTp1 from -0.63±0.00 to -1.10±0.02 K (Fig. A7), bringing ΔTp1 even closer to ΔTp2
(-1.16±0.01 K). This is consistent with the finding of a previous
study on the dependence of the temperature effect on the forest-cover-change
thresholds that were used to define afforestation: the higher the threshold,
the stronger the impact on temperature (Li et al., 2016). Our results add
further support to the compatibility of the three approaches given the same
boundary condition, i.e., the complete transformation from full openland to
full forest coverage.
Several factors may contribute to the remaining differences in the
temperature effects produced by different methods even after reconciliation.
As described in the Method section, there are discrepancies in the
assumptions of the three approaches, which lead to differences in the
control pixels (i.e., adjacent comparison pixels). For instance, for the
“pure” potential effect, it is assumed that the LSTs for pixels with the same
land-cover type are uniform, and forest pixels are compared with openland
pixels, whereas in the mixed potential effect approach, the central
target forest pixel is compared with the mean value of non-forest pixels
within the searching window. Moreover, space-for-time is an assumption that
cannot be verified (Chen and Dirmeyer, 2016) and which will inevitably result in
differences in the estimated actual and potential effects, although the
consistency between potential and “actual” effects after reconciliation in
our study does illustrate the broad validity of this assumption.
Differences between the actual and potential temperature effects can also
arise from influences of both the timing of the afforestation and the time
period elapsed following afforestation. However, such influences are
expected to be small in our study. We argue that such influences should be
more pronounced in the case of deforestation than afforestation. The
temperature effect caused by deforestation is considered to be instant (Liu
et al., 2018). As a result, if deforestation occurred in 1 specific year
of our starting time window (i.e., 2002–2004), using the time-averaging LST
over the whole time window to represent the LST before deforestation will
greatly bias the quantified ΔT. In contrast, afforestation-driven
surface temperature change can only gradually increase with forest
development. The LST effect depends on different stages of forest
development and is expected to saturate only when the forest canopy
stabilizes (Zhang et al., 2021; Windisch et al., 2021). Observation studies
show that closed dense-canopy old forests can exert a greater cooling effect
than the open-canopy young forests (Zhang et al., 2021; Windisch et al.,
2021; Duveiller et al., 2018, 2020). Hence, given the gradual nature of the afforestation effect on LST,
when we quantify the afforestation effect by comparing the time-averaging
LST before and after afforestation, the influence of the specific timing of
afforestation is expected to be small. Furthermore, the GFC dataset used in
this analysis defined forest gain using the condition of successful
detection of a stable closed forest canopy that is clearly different from a
non-forest state (Hansen et al., 2013), which enhances the chance of
temperature change saturation following afforestation. But, given a maximum
stand age of 12 years inferred from the GFC dataset, differences in surface
temperatures may still exist between newly established forests and the
mature existing forests that were used in the potential effect approaches.
Thus, we cannot exclude the possible contribution of time period elapsed
following afforestation to the difference between the actual and potential
effects, which failed to be reconciled.
Previous analyses have documented latitudinal patterns of surface
temperature change induced by afforestation (Alkama and Cescatti, 2016; Li
et al., 2015, 2016a; Peng et al., 2014). When comparing the three approaches
for a single case study, consistent latitudinal patterns of local surface
temperature effects following afforestation are observed (Fig. 4). Notably,
all three approaches show a warming effect in the northern high latitudes
and an opposite cooling effect in the southern low latitudes, with a largely
neutral effect in the 40–48∘ N latitude band, providing further
evidence that the three approaches are compatible. In particular, although
the three approaches used different land-cover maps, they derived consistent
LST impacts following afforestation, which highlights that fact that the
reconciliation provided in this study is rather robust and unlikely to be
dependent on the land-cover datasets used.
In addition to the reconciliation of the land surface temperature change, we
checked and confirmed that the changes in surface energy fluxes that
underlie and drive the changes in surface temperature are compatible under
the boundary condition of full afforestation. This finding confirms the
inherent consistency in the three approaches and clarifies the reasons
behind the apparent discrepancies in existing studies as discussed in the
Introduction. Nonetheless, when it comes to the biophysical impacts of
afforestation in the real world, our findings have far-reaching
implications. Full afforestation is often possible at small spatial scales
but becomes challenging at large scale. Therefore, the realization of the
full potential effect by afforestation is scale-dependent. For example, a
complete afforestation of the semi-arid Loess Plateau in the northwest of
China is predicted to generate a surface cooling effect of 2.40±0.07 K, but substantial afforestation efforts over the past 4 decades in that
region have only realized a cooling of 0.11±0.01 K, as measured by the
actual effect. Because of greater water consumption by forest compared to
openland and the need to maintain land area for food production, achieving
the full cooling potential may not be feasible (Huang et al., 2018; Liu and
She, 2012; Liang et al., 2019).
Potential cooling effects have a value in that they can serve to establish
the envelope of effects and measure possible outcomes given the condition of
full afforestation. However, given the challenge of full afforestation at
large spatial scales, potential effects should be converted into a more
realistic estimate (i.e., actual effects), by taking into account the
intensity of afforestation, to better represent policy ambitions. The analog
could also be made for the effects of the surface energy impacts of
afforestation. Taking 10 % as the afforestation intensity threshold to
compare the cumulative surface energy effect between the actual and
potential approaches, actual cumulative biophysical changes (5.06 EJ) for
2000–2012 are much smaller than mixed potential changes (20.13 EJ) and full
potential change (19.02 EJ) (methods in Sect. A2 in Appendix A; Fig. A8). Again, this shows
that simply using the potential effects for policy making or evaluation
risks greatly overestimating the biophysical effects of afforestation.
Conclusions
In this study we provided a synthesis of the three influential methods used
to quantify afforestation impact on surface temperature change and provided
evidence that these different methods could in fact be reconciled. The
actual effect of surface temperature change following afforestation was
highly dependent on the intensity of afforestation (Faff), which
explained 89 % of the variation in ΔTa. With the common
boundary condition of full afforestation being applied, differences in
afforestation impacts on LST reported by the three methods in previous
studies greatly reduced, showing that simply treating these differences as
uncertainty is incorrect and could greatly overestimate the uncertainty. In
other words, when full afforestation is assumed, the actual effect
approaches the potential effect, demonstrating the effectiveness of the
space-for-time approach and that the potential cooling effect of
afforestation could indeed be realized. Potential cooling effects have a
value in academic studies where they can be used to establish an envelope of
effects, but their realization at large scales is challenging given the
scale dependency. The reconciliation of the different approaches
demonstrated here stresses that the afforestation fraction should be
accounted for in order to bridge different estimates of surface cooling
effects in policy evaluation.
Figures
The distributions of the original sample pixels (at a
1 km resolution) for (a) the actual effect and (b) the two potential effects.
(a) Histogram of ΔTa of all pixels
based on the GFC dataset. (b) Histogram of ΔTa for
samples used for the statistical test. (c) Histogram of ΔTp1 of all pixels based on the GFC dataset. (d) Histogram of ΔTp1 for samples used for the statistical test.
Spatial distribution of afforestation-induced changes in
albedo (α) over China from three approaches: (a) actual albedo
change following afforestation based on the space-and-time method (Δαa), (b) mixed potential albedo change using medium-resolution
land-cover maps based on the space-for-time approach (Δαp1), and (c) full potential effect (Δαp2) based on the SVD approach.
Changes of actual effect in (a)ΔLW, (b)ΔSW, (c)ΔH+ΔG, and (d)ΔLE (W m-2) as a
function of afforestation intensity (Faff) following the actual effect approach. Error bars indicate the standard error within each 10 %
percent bin of Faff. The solid black lines represent the fitted linear
regression line between each energy flux variable and Faff.
The mixed potential effects (ΔTp1) obtained
based on MODIS land-cover data (MCD12Q1) and the land-cover distribution map
defined at the threshold of 50 % GlobeLand30 at 1 km resolution.
The influence of the forest-cover threshold applied to
the land-cover map underlying the estimation of the mixed potential effect
(ΔTp1).
The influence of the openland-cover threshold used to
identify a 1 km pixel as openland in the estimation of the mixed potential
effect (ΔTp1).
Afforestation-induced cumulative changes in surface
energy fluxes (exaJoules) in China for the period 2000–2012 following the
approaches of (a) the actual effect, (b) the mixed potential effect, and (c) the full
potential effect (methods in Sect. A2).
Cumulative surface energy effect
The cumulative surface energy effect (fcum) in Fig. A8 refers to the
sum of the flux change (J) from all the samples while at the same time
accounting for the forest change area (m2). More specifically, the
cumulative surface energy change (fcum) can be calculated from Eq. (A1):
fcum=∑i=1i=nareai×Fi,
where Fi is the flux change per unit area (W m-2) for pixel i, n is
the total number of samples, and areai is the forest change area in
pixel i.
Data availability
All datasets used in this study are summarized in Table 1 and are publicly
available. MOD11A2 land surface temperature is available from 10.5067/MODIS/MOD11A2.061 (Wan et al., 2021). MOD11C3 surface broadband emissivity is available from 10.5067/MODIS/MOD11C3.006 (Wan et al., 2015). Evapotranspiration is available from 10.5067/MODIS/MOD16A2.006 (Running et al., 2017). MCD43B3 is inaccessible and substituted by MCD43A3, which can be downloaded from 10.5067/MODIS/MCD43A3.006 (Schaaf and Wang, 2015). The Global Forest Change data are available from http://earthenginepartners.appspot.com/science-2013-global-forest (last access: 4 January 2023; Hansen et al., 2013). The land-cover-type dataset (GlobeLand30) is available from http://www.globallandcover.com/defaults_en.html?src=/Scripts/map/defaults/En/download_en.html&head=download&26type=data
(last access: 4 January 2023; Jun et al., 2014). Incoming shortwave radiation data can be accessed at https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF_L3B.004.1
(NASA/LARC/SD/ASDC, 2019). The elevation data are available from 10.5067/MEaSUREs/SRTM/SRTMGL1.003 (NASA JPL, 2013). Intermediate data and
scripts used to generate the results in this study are available from the
corresponding author upon reasonable request.
Author contributions
CY and SL designed the study. HW
conducted the analysis. All three authors contributed to writing and
revision of the text.
Competing interests
At least one of the (co-)authors is a member of the editorial board of Biogeosciences. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study was supported by the National Natural Science Foundation of China
(grant no. 41971132) and by the Strategic Priority Research Program of the
Chinese Academy of Sciences (grant no. XDB40000000).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41971132) and the Strategic Priority Research Program of the Chinese Academy of
Sciences (grant no. XDB40000000).
Review statement
This paper was edited by Alexandra Konings and reviewed by two anonymous referees.
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