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Decades of tree planting in Northern India had little effect on forest density and rural livelihoods

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Myriad scholars, policymakers, and practitioners advocate tree planting as a climate mitigation strategy and to support local livelihoods. But, is the broad appeal of tree planting supported by evidence? We report estimated impacts from decades of tree planting in Northern India. We find that tree plantings have not, on average, increased the proportion of dense forest cover, and have modestly shifted species composition away from the broadleaf varieties valued by local people. Supplementary analysis from household livelihood surveys show that, in contrast to narratives of forest dependent people being supported by tree planting, there are few direct users of these plantations and their dependence is low. We conclude that decades of expensive tree planting programs have not proved effective.
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Decades of tree planting in Northern India had little effect on forest density and
rural livelihoods
Abstract
Myriad scholars, policymakers, and practitioners advocate tree planting as a climate
mitigation strategy and to support local livelihoods. But, is the broad appeal of tree planting
supported by evidence? We report estimated impacts from decades of tree planting in Northern
India. We find that tree plantings have not, on average, increased the proportion of dense forest
cover, and have modestly shifted species composition away from the broadleaf varieties valued
by local people. Supplementary analysis from household livelihood surveys show that, in
contrast to narratives of forest dependent people being supported by tree planting, there are few
direct users of these plantations and their dependence is low. We conclude that decades of
expensive tree planting programs have not proved effective.
Data and materials availability: Replication data and materials for this analysis are available in
the UMN Dataverse: link posted upon final acceptance.
Code availability: Replication code for statistical analysis are available in the UMN Dataverse:
link posted upon final acceptance.
2
Main
Large scale tree planting is widely proposed as a central element of global climate
mitigation efforts, based on the potential of new trees to absorb carbon and support local
livelihoods1–4. Despite this broad appeal, some researchers and practitioners have raised concerns
about potential negative impacts of large-scale tree planting projects on vulnerable people and
forest ecosystems5–7. Others question whether forest restoration projects will achieve their
ambitious goals in the face of such challenges3,810.
Is large-scale tree planting supported by evidence? Fundamentally, we still lack rigorous
empirical studies that directly evaluate the performance of tree planting projects1113. This
evidence gap stems from the difficulty of obtaining long-term outcome data on forest cover and
rural livelihoods, and counterfactual research designs that credibly link these outcomes to policy.
Policymakers need to consider evidence on the efficacy of tree planting before allocating scarce
resources needed to fight climate change to such projects8. This study aims to provide such
evidence.
We worked with rural communities to identify and map tree plantation boundaries, and
matched these areas to historical land cover imagery in Kangra District of Himachal Pradesh in
Northern India. Through image classification, we estimate forest density and species
composition from longitudinal remote sensing at 6 point in time in 430 tree plantations from 60
randomly selected panchayats (local governments). Our analysis shows that, on average, tree
planting projects do not increase dense forest cover, and they modestly change species
composition away from the broadleaf varieties preferred by local people. The first result implies
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that tree planting has not contributed to climate change mitigation, and the second implies that
tree planting has not improved the availability of species that support rural livelihoods.
We supplement that analysis by surveying households and comparing the livelihood
contributions from different plantations. To do so, we conducted a quasi-random survey of 2400
households living proximal to plantations. We find that only a small proportion of any one
plantation’s potential users benefit from it through fuelwood collection, fodder collection, and
grazing. However, older plantations, larger plantations, and those closer to roads are used most
heavily. Additionally, while approximately 42% of our respondents have used at least one
plantation for fuelwood, fodder, or grazing, most of those plantation users rank their own
dependence as low. In sum, plantations only modestly contribute to rural livelihoods in our study
site.
Study Site
India provides an excellent context to assess the impact of plantations due to its long
history of tree planting programs14,15, continuing high level commitment to tree planting16,
prevalence of areas identified as having forest restoration potential17, and large number of forest
dependent people18. Yet recent systematic reviews on tree planting outcomes found no studies
from India12,13, and the case studies that do exist suggest plantations may fail19,20, endanger
livelihoods20,21, and threaten native forest cover2224.
Tree plantations have a long history in Kangra District, and trace their roots to concerns
about forest degradation due to excessive harvesting of wood fuel for cooking and excessive
grazing by domestic animals2528. As in most of India, the tree plantings we study are all on
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government-owned land and have been undertaken by the state forest department29. Although
there is also a history of commercially oriented forestry in Himachal Pradesh, a ban on
harvesting green trees since 1986 means that commercial timber production in this region has
been nonexistent for the entire period under study30. Figure 1 shows the location of the study site.
Figure 1. Study Site
We block-randomized selection of 60 study panchayats from four ranges within the Kangra district of Himachal
Pradesh, India.
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Impact Model
We use an Event Study Design31 to estimate the impact of plantations on forest cover.
The tree plantations in our sample (n=430) were established in a staggered manner over time.
The establishment years of the plantations range from 1965 to 2018. Figure 2 shows the
cumulative number of plantations, which have steadily increased since 1980. We combine this
information with dependent variables estimated using satellite land cover data available at six
time points: 1991, 1993, 1996, 1998, 2009, and 2018. These are the years for which cloud cover
allowed precise estimation of forest attributes at the spatial resolution in our analysis. We
estimate policy impact using observed data before and after the date when the plantation is
established, while controlling for a plantation’s site-specific geographic characteristics and
panchayat-level time trends31,32.
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Figure 2. Cumulative Number of Active Tree Plantations
The cumulative number of active tree plantations over time. Years of Landsat satellite image data observations
are indicated by red dashed lines.
We use the following equation o estimate the impact of plantations:
 =+() +  +.
Here,  indicates a land cover outcome in plantation, i, located in panchayat, p, measured at
time, t. The parameter vector  is a set of panchayat-year fixed effects, and  is an
idiosyncratic error term. Finally, () represents a policy impact function of the time
from the establishment of a plantation (where is centered on zero and takes positive and
negative values as years from the event). We focus on two policy impact functions: First, a
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flexible distributed fixed effects impact function, created by taking dummy variables for each
value of and estimating separate fixed effects for each year. Second, a simpler linear impact
function where we include as a linear trend and allow that trend to vary in the years before and
after plantation establishment. Below, we refer to as Plantation Age.
For outcomes, , we examine two types of land cover changes: forest density and
species composition. Change in density can affect biodiversity and carbon storage5. Species
composition estimates the usefulness of tree species for local people: broadleaf trees provide
more value as firewood and fodder for domestic animals, while needleleaf species are less
valuable for those purposes3336.
Using LANDSAT imagery, we temporally estimate the species composition of each pixel
lying within each plantation area based on four land cover categories: percent needleleaf species
cover; percent broadleaf species cover; percent mixed cover (needleleaf and broadleaf species);
and percent grassland (the residual category). For the analysis presented here, we focus on the
percent classified as broadleaf, the most relevant category for local people. To estimate forest
density, we combine our estimates of species composition with data from the Forest Survey of
India (FSI) to temporally estimate the percent of each plantation area (e.g. percent of pixels)
classified as “dense forest” (forest cover > 40% according to the FSI criteria). We detail this
process in the Methods section, and report image classification accuracies in our Supplementary
Information (Tables S1-S15).
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Longitudinal Data
The units of analysis are plantation-years. We compare forest cover observations before
and after tree planting using linear regression models with standard errors clustered at the
plantation level. At the plantation level, we include controls for slope, elevation, an interaction of
slope and elevation, plantation size, distance to nearest road (in minutes of travel time, square
root transformed to reduce skew), and an interaction of plantation size and distance to nearest
road. Finally, we include 300 (=60 plantations × (6-1) time periods) panchayat-year fixed effects.
There are between two and 21 plantations in each panchayat (Fig. S3 in our Supplementary
Information), and some plantations are shared by multiple panchayats.
Estimation Results
We present our main results in Figure 3. We report the dummy variable impact function
estimates as black dots with error bars and the linear impact function estimates as a solid blue
line with a blue shaded area. Both models report 95% confidence intervals (derived from
standard errors clustered on plantations). Effect sizes (y-axis) should be interpreted as
differences in an outcome τ years from the establishment of the plantation (Plantation Age is
truncated at ±20). The interaction between After Plantation (dummy variable indicating year
after established) and Plantation Age in the linear impact function lets us estimate separate pre-
and post- establishment forest cover trends. The fixed effects impact function approach is less
efficient, but it provides flexible nonparametric estimates of the impact of tree planting (27). We
believe the linear model presents a reasonable, more efficient, approximation for these effects,
but we report both in Fig. 3 for reference.
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Figure 3. The Effects of Plantations on Land Cover
Estimated linear and dummy variable impacts with 95% confidence intervals. Panel (a) shows no significant
difference in percent dense forest cover when comparing the years prior to the establishment of the plantation
(< 0) with the time when it was established (= 0). Similarly, there is no significant difference when
considering percent dense forest cover in the years after the plantation was established (> 0). These results are
consistent with both the more flexible impact model and the linear model. Panel (b) shows no significant
difference in broadleaf composition when comparing the years prior to the establishment of the plantation (<
0) with the time when it was established (= 0). However, there is a decline in broadleaf composition in the
years after the plantation was established (> 0). This decline is consistent in both the more flexible impact
model and the linear model, although the assumed structure of the linear model provides more precise estimates
(narrower confidence intervals) that reach standard significance thresholds over the entire post-establishment
timeframe.
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Panel (a) of Fig. 3 illustrates that older plantations do not have denser forest cover than
younger plantations. The linear effect of Plantation Age is negative and close to 0, while the
dummy variable impact function produces estimates that are scattered across 0 and which rarely
come close to meeting standard thresholds for statistical significance for any given age. Even
plantations in our dataset that are 20+ years old are not covered by meaningfully more or less
dense forest than recently planted areas. A Wald test shows that pre- and post-establishment
linear trends are not statistically distinguishable for forest density (see Table S16 in our
Supplementary Information).
Panel (b) reports negative effects of tree planting on broadleaf cover using both the
dummy variable and linear impact functions, although results are not large enough to be
significant at classic thresholds when using the dummy variable approach (until approx. 20 years
post-establishment). But, we estimate (from the linear impact model) that 20-year-old plantations
have ~10% less broadleaf cover than those that have just been established. A Wald test supports
the difference between pre- and post-establishment linear trends in broadleaf cover (Table S17).
Both models in Figure 3(b) provide consistent evidence against a claim that tree planting
increases the proportion of broadleaf cover.
In summary, establishing plantations has not improved the proportion of broadleaf cover
or dense forest cover in these areas. Full regression tables from these and other specifications
(Tables S16-S17) are available in our Supplementary Information. The results we report here are
robust to a variety of alternative specifications. In the Supplementary Information we also
present results for the other land cover classifications (Figures S5 and S6; Tables S18-S21). Note
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that our findings do not support a claim that tree planting replenished threatened forest cover or
prevented the proportion of dense forest or broadleaf cover from declining more rapidly. In that
case, we would see stronger evidence of declining dense or broadleaf cover in the years before
plantation establishment and a tempering of this trend after establishment.
Livelihood support
We now move to a cross-sectional analysis of how different plantation characteristics
influence the livelihood support plantations currently provide. We conducted surveys of 40
households in each panchayat between March 2018 and May 2019. For each of the 430
plantations in our sample, we aggregate household survey responses from all its panchayats.
Because some plantations are a part of one panchayat while others are part of up to three, we
have data from between 40 to 120 household survey respondents for each plantation.
Our units of analysis are now the cross-section of 430 plantations. We consider three
outcomes—the number of respondents using a plantation to collect fuelwood, the number using a
plantation to collect fodder; and the number using a plantation to graze animals (sheep, cattle,
goats, and buffalo). Figure 4 presents box plots of the number of households supported by each
plantation in this sample. Overall, most plantations have few direct users: they support fewer
than 10 households in our sample for any one use. Figure S7 shows that calculating the
proportion of plantation users instead produces a similar pattern.
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Figure 4. Box plots for plantation use outcome measures
These box plots illustrate the distribution of our outcome measures in the plantation use regression analyses. The
outcomes are the count of respondents using a plantation for fuelwood collection, the count using it for fodder
collection, and the count using it for grazing. The box plots are only calculated for counts greater than 0. Boxplots
center line is the median, box limits are the upper and lower quartiles, whiskers are 1.5x interquartile range, and
points are outliers. We provide the number of non-zero observations for each outcome measure in the legend (out of
430 observations total).
While most of these plantations are only used by a minority of those living in one of its
panchayats, we show in Table S23 that 42% of our household sample uses at least one plantation
for at least one of these purposes (+/- 2% at a 95% confidence level). However, when those
plantation users rated their dependence, only 9% indicated that their dependence on plantations
was medium or high (+/- 1.6% at a 95% confidence level). 91% of respondents who use
plantations for fuelwood, fodder, or grazing rank their dependence as low (Table S23). In sum,
while plantation use for fuelwood, fodder, or grazing is common, most households receive these
benefits from only a few plantations (Tables S23-S24). Most households are also not highly
dependent on these benefits.
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Analysis of livelihood benefits
Some plantations contribute more to local livelihoods than others. We use negative
binomial count regression models with panchayat fixed effects to explore within-panchayat
variation in plantation use. These models employ three explanatory variables: Plantation Age
(we focus on a linear impact function); a plantation’s distance from the road in minutes of travel
time (square root transformed to reduce skew); and a plantation’s size (logged). Select results are
in Table 1, and full results are in Table S25 of our Supplementary Information. Coefficients from
negative binomial models are difficult to directly interpret, so we instead report transformations
of those coefficients: the percent change in the expected number of plantation users associated
with a +1 increase in each explanatory variable in Panel (a). We also report estimates of the
impact of a large (two standard deviation) increase in these variables on the count of plantation
users, with other variables held at their observed values in Panel (b).
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Table 1. Results from fixed-effects negative binomial regressions of plantation use.
(a)
Percent change in the number of users due to a +1 unit change in the explanatory variable
(p-values in parentheses)
Variable
Fodder
Grazing
Plantation age
+0.28% (0.73)
+2.52% (<0.01)
Minutes from the road (sqrt)
-8.53% (0.01)
-4.58% (0.12)
Plantation area (log)
+29.94% (0.10)
+11.13% (0.53)
(b)
Estimated change in the number of users from a +2 standard deviation unit change in the explanatory
variable, with other variables held at their observed values
Variable and change
Change in count
95% CI
Plantation age (11 to 35)
+0.61
0.14, 1.08
Road minutes, sqrt. (0 to 6)
-0.84
-1.35, -0.32
Plant. area, log (1.61 to 2.95)
+0.80
0.09, 1.53
For the 430 plantations in this analysis, we estimate a negative binomial regression of the count of users with
panchayat-level clustered standard errors adjusted. In addition to the explanatory variables below, we also estimate
panchayat fixed effects, which means that our results draw on variation among plantations in the same panchayat.
(a) We use the results of those regressions to calculate the percent change in the expected count of plantation users
associated with an increase of 1 in each explanatory variable. We present p-values in parentheses (two-tailed
hypothesis tests). Full regression tables are available in our Supplementary Information, and more details on
estimation are available in our Methods section. (b) Examples of the effect of substantively interesting changes in
several variables on the expected count of plantation users. For these continuous variables we calculate change in
the expected count due to a +2SD increase, starting from the variable’s 1st quartile. See summary statistics in Table
S9. We use the observed values approach to hold constant the effects of other variables.
Though most plantations are not heavily used, there is some variation in the amount of
livelihood support that different plantations provide. For instance, older plantations have
consistently more users for all three measures of livelihood support, although the finding for
fodder collection is not statistically significant. This effect of plantation age is also only modest:
Panel b indicates that 35-year-old plantations have 0.61 more users for fuelwood collection, on
average, than 11-year-old plantations (a 47% increase in the count of plantation users).
There are more substantial effects for road distance on fuelwood and fodder collection. In
conjunction with our forest cover analyses, these results imply that plantations closer to the road
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are more useful from a livelihood perspective, but are also less likely to contain the broadleaf
species households prefer. Finally, our results also show that a plantation’s size is an important
predictor of its ability to jointly contribute to both environmental and livelihood goals. Larger
plantations have denser forest cover (Supplementary Information Table S16) and are more likely
to serve as a useful source of fuelwood collection.
Discussion
After decades of costly investments, we find no evidence that tree planting projects
secured additional benefits for carbon mitigation or livelihood support in Northern India.
Planting trees might seem like a straightforward way to increase carbon storage, but the process
of growing trees is expensive and complicated6,37. Our analysis suggests that planting trees may
be a less useful carbon mitigation strategy than its proponents claim1.
Why have these efforts to plant trees failed to improve forest conditions? There are
several possibilities. First, these plantations occur in densely settled agro-pastoralist landscapes,
where a variety of existing land uses limit spaces available for further tree plantations. As a
result, most tree planting happens within areas that already have some tree cover, limiting the
potential regrowth opportunities. Cleared areas are not a viable alternative because of
socioeconomic and ecological constraints of converting productive lands back to forests. If
policymakers wish to promote forest restoration through tree planting, then the underlying social
and ecological processes that led to forest degradation in the first place needs to be addressed3,5,6.
Second, forest bureaucracies have internal incentive structures focused on achieving tree
planting targets rather than sustaining longer-term socio-ecological benefits through providing
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support for continued tree growth15,36. Foresters may be incentivized to plant trees of low
livelihood value precisely because they believe these trees are more likely to survive in a
neglectful environment15.
While forest plantation and restoration programs are often premised on the view that
healthy ecosystems are better able to support subsistence and commercial livelihoods2,4, our
analyses raise critical concerns about plantations as a straightforward, cost-effective strategy for
sequestering carbon while supporting the poor. While planting trees is often framed as an
immediate point of action for climate change mitigation as economies pursue long-term
decarbonization, our findings provide empirical evidence for the need to temper these
expectations8,10. Policymakers and advocates should not just assume tree planting programs will
effectively meet their carbon sequestration and livelihood goals. Although our understanding of
this topic is improving, further research is needed to understand the ecological, socioeconomic,
and institutional conditions that might make tree planting more successful3840.
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Methods
This research is based on data collected between August 2017 and May 2019 in Kangra
district, Himachal Pradesh in northwest India. The objective of the data collection was to
examine the connections between livelihoods and plantations in diverse social contexts and
forest landscapes. We first discuss the various types of data we collected and then provide
additional details on the data analysis results reported in the main manuscript.
Data Collection
We selected four forest administrative units called ‘ranges’—Palampur, Daroh,
Dharamsala and Shahpur—that adequately represent variation in plantation types, elevation, and
forest use in Kangra. We randomly selected 60 of the 181 panchayats (formalized local
governments consisting of a few villages and habitations) within these four ranges (i.e. block
randomization, 15 panchayats per range). Using panchayats as the unit of analysis, we collected
data through four research instruments that focus on different aspects of communities (C-Form),
households (H-Form), plantations (p-Form), and plantation ecology (P-Form) for each
panchayat.
Data collection followed a sequence of steps starting with creating a list of plantations in
the panchayat, and then surveying communities, plantations, and households in that order. We
found that the most challenging aspect of connecting plantations to livelihoods was to
disaggregate and uniquely identify the different plantations in the panchayat, determine their
locations, and estimate their areas, planting years and management details. To solve this
challenge, we identified key informants (e.g. forest stewards or Rakhas, forest field staff,
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members of forest institutions, community leaders, etc.) in each panchayat who were
knowledgeable about the location and its tree planting history. Based on these key-informant
interviews we created a plantation survey (p-Form) that listed all plantations in the panchayat,
their local names, year of planting, planted and current tree species, previous and current land
use, and the institutions responsible for plantation creation and management.
Using the plantation list (p-Form) we randomly sampled 10 plantations in each panchayat
that were planted after 1980 and were greater than 5 hectares in size for detailed social-
ecological survey using the P-Form. If fewer than 10 plantations met our criteria, we simply
sampled all plantations that met the criteria. In addition to these plantations, we collected data on
all plantations that were planted in 2017 irrespective of their size. In total, we listed 1250
plantations in the p-Form and surveyed 430 plantations using the P-Form. The statistical analysis
reported in Figure 3 is for those 430 plantations with repeat measures in 6 years.
Institutional Data
We conducted preliminary meetings with local representatives in each panchayat, and
organized an informal group meeting with other officials. During this meeting, we collected
information on active institutions, including civic society and market actors. We used these
meetings to gather demographic data and identify key informants such as the forest guard,
Rakha, or retired officials who could recollect and narrate the landscape history of the panchayat.
We then conducted a series of meetings with identified key informants. We conducted transect
walks with the key informants through key areas of the panchayat in order to create a cognitive
resource and asset map of the panchayat that listed all key landmarks (e.g., child care center,
temple etc.,) institutions (e.g., veterinary clinic, forest guard hut etc.,) natural resources (e.g.,
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streams, pastures etc.,) habitations (e.g., clusters of households, hamlets etc.,) and plantations.
We triangulated this information from multiple sources and generated a list of plantations and
institutions that would be used during the household interview and plantation ecological survey.
Livelihood Data
Within the 60 panchayats we created a decision tree for survey data collection. First, we
collected secondary data on community demographics and conducted exploratory meetings with
panchayat leaders and residents. Based on this meeting, we identified key informants who
assisted in validating panchayat demographics and creating a list of households in the panchayat
including names of head of household and their father, caste, and number of family members.
We randomly selected 40 households in each panchayat from the list we developed and
administered surveys to each of them in Hindi. Each household interview was conducted by a
team of two trained field staff, one of whom solicited responses to questions in a conversation
style, while the other noted the responses on a printed questionnaire form. We found that a
modular questionnaire conducted as a conversation better engaged the respondents who were
able to triangulate responses from memory. All household interviews were conducted at the
respondent’s residence with minimal interference from non-respondents. We later entered the
data on survey forms in Qualtrics software. The household livelihood analysis reported in Figure
4 and Table 1 come from a cross sectional analysis of 40 surveys x 60 panchayats = 2400 total
households measured once.
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Biophysical Data
Biophysical information is derived from multi-temporal Landsat satellite image mosaics,
which are jointly analyzed with field data, as well as ancillary data. In particular, to facilitate
information extraction from the Landsat time series, we exploit a Shuttle Radar Topography
Mission (SRTM)-derived digital elevation model (DEM), a plantation-boundary Esri geographic
information system (GIS) polygon shapefile, training/testing land-cover/forest type field
polygons, and multi-temporal Forest Survey of India (FSI) forest-density maps, as ancillary
spatial data for the multi-temporal Landsat image classifications.
Pairs of Landsat images in a given year are utilized to generate the respective mosaics in
the time series, acquired at the limited times during which there was sufficiently minimal cloud
cover, and such that the spatial extent of the study area was covered. Some characteristics of
these Landsat images are summarized in Table S1 of the Supplementary Information. For
Path/Row 147/38, the image-acquisition dates are: 05/10/1992, 04/27/1993, 04/03/1996,
05/27/1998, 05/09/2009, and 04/05/2018. For Path/Row 148/38, the image dates are: 05/15/1991,
05/04/1993, 04/26/1996, 04/16/1998, 04/30/2009, and 04/17/2018. For the first image pair in the
time series, one image is from 1992 (05/10/1992), and the other image is from 1991
(05/15/1991), on a near-anniversary date; this is due to image-availability and cloud-cover
limitations.
Regarding Landsat image pre-processing, we radiometrically calibrated the raw data
(digital numbers; DNs) to units of radiance, and radiance image data were used as input to the
Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH®) algorithm for
atmospheric correction41, resulting in surface-reflectance images for the various image dates. For
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each Landsat image pair, we then mosaic the individual FLAASH-corrected images via the
“Seamless Mosaic” tool in the ENVI® (The Environment for Visualizing Images®) remote-
sensing digital image-processing software package. We then spatially clip the mosaicked images
to just encapsulate our study area. All Landsat images are in the UTM projected coordinate
system (Zone 43N; datum = WGS 1984).
In addition to the Landsat image bands, we also employ a Shuttle Radar Topography
Mission (SRTM)-derived digital elevation model (DEM), with a spatial resolution of 30 meters,
as another input to the image classifier. The SRTM DEM is thus also clipped to the study-area
extent, and the Landsat image bands are stacked with the SRTM DEM (also in UTM, Zone 43N,
WGS 84).
During 2018-2019, we delineated multiple field polygons per forest plantation via global
positioning system (GPS) receiver, whereby the dominant nominal land-cover/forest type was
recorded for each polygon. We employ these polygons for supervised classification algorithm
training and testing. More specifically, we utilize all field-delineated polygons (from 2018-2019)
for classifying the 2018 Landsat image mosaic, and in accuracy assessment, with appropriate
random selection, discussed below. In order to classify the other Landsat image mosaics in the
time series, we modify or remove a subset of training/testing field polygons that are in need of
modification (e.g., via modifying the polygon boundaries) or are invalid with respect to the
spatial configuration of features that are present within a given Landsat mosaic, collected earlier
than 2018. Starting with the 2009 Landsat mosaic, we visually/manually interpret historical,
multi-temporal, high-spatial-resolution Google Earth images, which are of a higher spatial
resolution than the 30-m Landsat images, to determine which training/testing land-cover/forest
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type field polygons need to be modified or deleted (if no longer valid) for the purpose of
classifying that Landsat image mosaic. The resultant modified/reduced set of training/testing
field polygons then constitute the starting point for subsequent evaluation of the field polygon
boundaries via visual interpretation of the maximally temporally corresponding Google Earth
images, with reference to the next most recent Landsat image mosaic.
We repeat this procedure, progressively proceeding backwards through the Landsat time
series. For the earliest Landsat dates, due to the unavailability of Google Earth images, the
Landsat images themselves served as the primary source material for image interpretation, in
concert with Landsat-derived vegetation index images and other ancillary data, serving as
reference data for evaluating the training/testing field polygon set. Thus, uncertainty in the
resultant image classifications increases progressively backward in time, particularly for the
earlier Landsat image dates (i.e., 1996, 1993, and 1991). This process yields the following final
training/testing field polygon counts, for each respective Landsat mosaic year: for 2018, 835
polygons; for 2009, 735 polygons; for 1998, 673 polygons; for 1996, 656 polygons; for 1993,
656 polygons; and for 1991, 656 polygons.
We utilize FSI forest-density raster maps for the years 2001, 2005, 2009, and 2019 to
provide multi-temporal forest-density reference data, where we match a given Landsat image to
the temporally-closest available FSI forest-density map. We resample the FSI forest-density
maps (cell size = 24 meters) via nearest-neighbor resampling to yield the same cell size as that of
the Landsat image pixels (i.e., 30 meters). We reproject the forest-density rasters to the UTM
projected coordinate system (Zone 43N; datum = WGS 1984), matching that of the Landsat
images, and we snap the forest-density raster cells to the Landsat pixels for proper alignment.
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We simplify the FSI forest-density classification system by merging the “Moderately Dense
Forest” (40 70% canopy cover) and “Very Dense Forest” (>70% canopy cover) classes to form
a single “Dense Forest” class. Other forest-density classes include Open Forest (10% - 40%) and
Scrub (<10%). We use the forest-density rasters in conjunction with the training/testing land-
cover type field polygons (via spatial join and other GIS operations) to generate (via deep-
learning classification) multi-temporal combined land-cover/forest-density classifications, which
feature composite classes.
We actually produce multi-temporal combined land-cover/forest-density classifications,
as well as multi-temporal land-cover-only classifications (based on the training/testing field
polygons). For both sets of classification trials, the Landsat image bands and the SRTM DEM
are used as inputs to a deep-learning classification algorithm—i.e., a 2-D convolutional neural
network (2DCNN) classifier42.
For every classification trial, the labeled pixels are split into two pixel-sets randomly,
including a training set and a testing set. We use the training set to train our classification model,
and the testing set is employed for accuracy assessment. In our experiments, the ratio between
training samples and testing samples is 1:1, which means that 50% of labeled samples per class
are selected for training, and the remaining labeled samples are exploited as testing samples.
Note that such training-testing sample selection is a random selection, and a new random
selection is implemented for each trial to ensure that different trials have different training and
testing samples.
For a given Landsat image mosaic year, the classification experiments are repeated 10
times to avoid sampling bias. Regarding the accuracy assessments, we compute several accuracy
24
metrics, described as follows: Overall accuracy (OA) is defined by calculating the ratio between
the number of pixels classified correctly and the number of all pixels in the set of testing
samples. Average accuracy (AA) is the average of all accuracies, computed across all classes.
Also, Kappa is a statistical index for a consistency test, which can be calculated from a confusion
matrix43. All the aforementioned accuracy-evaluation indices are be calculated by averaging
those indices across all 10 replications.
For the multi-temporal combined land-cover/forest-density classifications, accuracy-
assessment results are given in Tables S2-S7 in the Supplementary Information. For most years,
there are 15 classes, which are given in Tables S2-S7. However, note that the “Needleleaf Open
Forest” class is not part of the 2009 classification, and the “Pasture Open Forest” class is not part
of both the 2009 and 2018 classifications. Those classes are not part of those respective
classifications because they did not exist in the training/testing polygons, after being combined
with the forest-density data, for those Landsat mosaic years. This may at least partly be due to
error associated with the FSI maps. Post-classification change detection44 was performed on a
pairwise basis, based on the 2DCNN classified image mosaics. Accuracy assessment is only
performed based on results and data within the forest-plantation boundaries—specifically, within
those field polygons randomly selected for testing.
Regarding the multi-temporal land-cover-only classifications (that do not include forest-
density information in the classes), those classes are: needleleaf forest, broadleaf forest, mixed
forest, and pasture. The quantitative classification accuracy-assessment results for the various
Landsat image dates in our time series using this classification system are shown in our
Supplementary Information in Tables S9-S14. With a smaller number of classes involved, we
25
find that these classification accuracies are higher than the results based on the composite land-
cover/forest-density classes. Thus, these multi-temporal classified images and the quantified
changes that were detected based on those classifications serve as the basis for subsequent
statistical analyses of land-cover change. As noted, we also perform pairwise change-detection
analyses based on the multi-temporal land-cover classified images, and some of those results are
summarized in Table S15.
Data Analysis
Forest cover analysis
We estimate an effect of tree planting on forest density and species composition by
comparing newly established plantations (planted at time = 0) with plantations already in
existence (> 0) for different periods of time. Similarly, we compare recently established
plantations to areas that will be planted in the future (< 0) to look for noteworthy pre-
establishment trends in forest density and species composition. Our units of analysis are the 430
plantations in this study in six different years (1991, 1993, 1996, 1998, 2008, and 2009), yielding
2,580 observations total (plantation-years).
First, we consider including a simple binary variable in our models that indicates whether
Plantation Age is greater than 0 (After Plantation). This yields a difference-in-differences
analysis of the impacts of tree planting (Model 1 in Tables S16-S21). Second, we allow
Plantation Age to have separate linear effects on our outcome measures in the years before and
after planting (Model 2). We accomplish this by including Plantation Age in the model,
interacted with After Plantation. Third, we consider constructing dummy variables for each value
26
of Plantation Age and including all of these dummies in our regression models (Model 3).
Finally, we allow Plantation Age to have separate curvilinear effects on our outcome measures in
the years before and after planting (Model 4). This an extension of our second approach. We
highlight the second and third impact functions in the main text.
We use a Wald test on the output of Model 2 to compare the estimated linear effects of
Plantation Age in the years before and after planting for each outcome measure. Specifically, we
test a restriction that both estimated linear trends are equivalent. Tables S16-S21 in our
Supplementary Information present our regression results for all outcome measures. Results
presented in Tables S16 and S17 are used to construct Figure 3 in the main text. Fig. S5 and S6
in our Supplementary Information provide figures comparable to Fig. 3 for outcome measures
not discussed in the main text.
Plantation use analysis
For these analyses, our units are a cross-section of the 430 panchayats considered in the
forest cover analyses. For each plantation, we aggregate household survey responses from all the
panchayats of which it is a part (based on key informant interviews). Some plantations are a part
of two or three panchayats. As a result, each of the 430 plantations considered in this study are
available to between 40 and 120 respondents. Our outcome measure for each plantation is the
number of household survey respondents that indicated using it for: fuelwood collection; fodder
(animal feed) collection; and grazing animals.
We use negative binomial regression (NB) models to explain variation in the number of
users across plantations. We prefer a NB model over other count regression models. Model fit
27
comparisons show that a NB model (AIC 1286.604, BIC 1306.923) outperforms a Poisson
regression model (AIC 1693.154, BIC 1709.41) and either modestly underperforms or modestly
outperforms a zero-inflated negative binomial regression model (AIC 1286.199, BIC 1322.773)
depending on the metric considered.
The goal of this analysis is to determine whether plantation characteristics considered in
our forest cover analyses also explain variation in plantation use. We employ three explanatory
variables: Plantation Age (the linear impact function); distance from the road in minutes (square
root); and plantation size in hectares (logged). We also include panchayat fixed effects (we do
not use these fixed effects in the AIC/BIC comparisons). Introducing fixed effects into some
nonlinear regression models can produce bias through the “incidental parameters problem.”
However, simulation evidence suggests that in a NB model fixed effects bias standard errors and
not coefficient estimates45. We implement a standard error correction those authors recommend.
We present the results from all three negative binomial regression models in Table S25 of
our Supplementary Information. In Table 1 of the main text, we report transformations of these
coefficients in Panel A rather than the coefficients themselves. Applying the following equation
to a negative binomial regression coefficient for some variable yields the percent change in
the expected count associated with a unit increase in : 100 × [1]. For Panel B of Table
1 we calculate the change in the expected count of plantation users associated with a large
increase in each explanatory variable. We define a large increase as an increase of 2 standard
deviations from that explanatory variable’s first quartile, and hold other explanatory variables at
their observed values.
28
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... While deserts and xeric shrublands are the most poorly represented in our study (Figure 2b) and are suffering increasing degradation world-wide (Martínez-Valderrama et al., 2020), they are less inhabited (1.7% of the world population; Martínez-Valderrama et al., 2020). Agroforestry and afforestation in open biomes are being implemented widely in sub-Saharan countries(Djenontin et al., 2021), India(Coleman et al., 2021), China(Cao et al., 2011) and Brazil(Martinelli et al., 2019), despite repeated warnings and robust evidence of the harmful consequences of such tree planting to ecosystem services, biodiversity conservation and human livelihoods (reviewed byFleischman et al., 2020). Tree plantations may result in a reduction in net carbon sequestration or an increase in net emissions relative to previous land-cover types, such as grassland or peatland, and may divert attention from efforts to reduce emissions from deforestation and degradation(Sutherland et al., 2021). ...
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