Puzzling patterns:Predictive modeling of afforestation
spending in Himalayan forests
Pushpendra Rana (Indian Forest Service/University of Illinois)
Vijay Ramprasad (University of Minnesota)
Forrest Fleischman (University of Minnesota)
Kangjae Lee (University of Illinois)
NASA SARI Meeting, 11/07/2019, Sustainable Forestry in South Asia, TERI, New Delhi
Increased flow of funds for afforestation
350
170
0
50
100
150
200
250
300
350
400
Bonn Challenge Commitments (n=50)
Res
tora
tio
n t
arge
t: M
ha
by
20
30
Forest Landscape Restoration (Mha)
Bonn challenge
0
2
4
6
8
10
12
14
GoI progress (2011 to2016/2017)
GoI pledge (by 2020) GoI additional pledge (by2030)
Deg
rad
ed la
nd
scap
es u
nd
er r
esto
rati
on
(M
ha)
India and Bonn challenge
0
50
100
150
200
250
300
350
An
nu
al b
ud
get
(bill
ion
do
llars
)
India and Bonn challenge (IUCN, 2018)
*Aichhi targets (15% of degraded ecosystems by 2020)
$300 billions
per year
(Ding et al. 2017)
Question
• How can we explain current patterns of plantation programs?
Outline
- 4 patterns of afforestation spending – Himachal Pradesh
- 2 likely explanations
- Conclusion
- Future directions
Himachal Pradesh
Forest Survey of India Report (2017)
• Geographical area: 55,673 sq. km.
• Altitude: 350 m to 6975 above mean sea level
• Forest area: 27.12% of total geographical area
• 1/3rd of state is permanently under snow,
glaciers and cold desert
Afforestation spending in Himachal Pradesh
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
Firs
t FY
P (
195
0-5
6 )
Seco
nd
FYP
(1
95
6-6
1)
Thir
d F
YP (
19
61-6
6)
An
nu
al P
lan
s (1
966
-69
)
Fou
rth
FYP
(1
96
9-7
4)
Fift
h F
YP (
197
4-7
8)
An
nu
al P
lan
s (1
978
-79
& 7
9-8
0 )
Sixt
h F
YP (
19
80-
85)
Seve
nth
FYP
(1
98
5-9
0)
An
nu
al P
lan
s (1
990
-91
& 9
1-9
2)
Eigh
th F
YP(1
99
2-9
7)
Nin
th F
YP (
199
7-2
00
2)
Ten
th F
YP (
20
02-0
7)
Elev
enth
FYP
(20
07
-12
)
Elev
enth
FYP
(20
12
-17
)
Are
a p
lan
ted
(H
ecta
res)
Year
0
20
40
60
80
100
120
Firs
t FY
P (
19
50
-56
)
Seco
nd
FYP
(1
95
6-6
1)
Thir
d F
YP (
19
61
-66
)
An
nu
al P
lan
s (1
96
6-6
9 )
Fou
rth
FYP
(1
96
9-7
4)
Fift
h F
YP (
19
74
-78
)
An
nu
al P
lan
s (1
97
8-7
9 &
79
-80
)
Sixt
h F
YP (
19
80
-85
)
Seve
nth
FYP
(1
98
5-9
0)
An
nu
al P
lan
s (1
99
0-9
1 &
91
-92
)
Eigh
th F
YP(1
99
2-9
7)
Nin
th F
YP (
19
97
-20
02
)
Ten
th F
YP (
20
02
-07
)
Elev
enth
FYP
(2
00
7-1
2)
Elev
enth
FYP
(2
01
2-1
7)
Tota
l aff
ore
stat
ion
sp
end
ing
(mill
ion
do
llars
)
Year
Source: Forest Department Statistics, 2019
Puzzling pattern 1: More spending in areas with high predicted
plantation mortality
• A quarter of afforestation
spending where probability
of experiencing mortality is
greater than 70%
• About 36% of afforestation
spending is on plantations
whose probability of
experiencing mortality is
greater than 60%0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
0-1
0 %
10
-20
%
20
- 3
0 %
30
-40
%
40
-50
%
50
-60
%
60
-70
%
70
-80
%
80
-90
%
90
-10
0 %
Tota
l sp
end
ing
(mill
ion
do
llars
)Predicted plantation mortality (n = 2204)
~USD 3.2 million
Puzzling pattern 2: High budget flow towards ‘non-forest’ areas; low
spending in Open Forest and increase in spending in Dense Forests
47.7
26.9
18.49
0
10
20
30
40
50
60
Non-Forest OF >20% OF >40%
Shar
e o
f af
fore
stat
ion
sp
end
ing
Tree canopy classes (Forest Survey of India)
13.22
7.33 8 8.05
15.69
47.7
0
10
20
30
40
50
60
0-20 20-40 40-60 60-80 80-100 No forest cover
Per
cen
t o
f to
tal s
pen
din
g
Moderately Dense Forest
Puzzling pattern 3: More tree planting in bio-diverse areas
0
0.5
1
1.5
2
2.5
3
Low Moderate High Very High
>0 to 33 34-49 50 -69 70-90
Tota
l sp
end
ing
(mill
ion
do
llars
)
Biodiversity index
• 81% of afforestation
spending is happening in
places where biodiversity
values are moderate to very
high
Pattern 4: Very little spending on community-managed plantations
• About 53% of afforestation spending goes to DPFs, about 30% to UPFs and 15% to RFs.
• Only 1.15% funding to CFs (Cooperative Forest Societies)
• Little community participation in plantations though JFM is promoted
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
Reserve Forests Demarcated ProtectedForests
UndemarcatedProtected Forests
Cooperative ForestSociety Forests
Tota
l sp
end
ing
(mill
ion
do
llars
)
Forest legal classification
Estimating predictive plantation mortality
Plantation mortality: Forest cover change using FSI data
Ensemble model (xgbTree, Random Forest and Naïve Bayes) to
predict forest mortality in 16,674 forests
• Predicting plantation mortality probabilities (n=2204)
• Compare them with afforestation spending
Attributes (n=31): Forest users, demographics, site quality
factors including edaphic and biophysical factors, forest fires,
baseline forest cover/other land use categories and other
predictors related to deforestation/mortality
Estimating predictive plantation mortality
n = 16, 674 forests for predicting forest cover loss n = 2204 plantations for predicting plantation mortality
Hypotheses
• Limited productive land available for planting trees
• Increased plantation activity in bio-diverse areas
– Clearing of native vegetation for making space for tree-planting
leading to decline in bio-diverse forests
The category "non-forest"
includes all lands without
forest cover, such as
agricultural croplands,
grasslands, wastelands, scrub,
water bodies, riverbeds, snow-
covered mountains and built
up areas (FSI, 2001)
Rocky, precipitous and
inaccessible areas
Alpine or sub-alpine pastures
‘Non-forest’ tree canopy density class
Plantations bound to fail
Biodiversity richness map
• Biodiversity richness map
of Himachal Pradesh
2012
• IIRS Biodiversity
Characterization at
Landscape level
assessment (vegetation
type, disturbance,
landscape level features
etc.)
Plantations activity in biodiversity rich areas
• Spread of exotic species
• Species-diverse landscapes
to low-density forests (Veldman et al. 2019)
• Conversion of native
grasslands to tree lots and
clearing of native diverse
vegetation to make space
for tree plantations (Brancalion et al. 2019; Brancalion
and Chazdon 2017 ; Lindenmayer et
al. 2012)
Conversion of pastures into tree lots
Ballah
PatialkarThala
Kareri
Biodiversity rich areas have seen decline in forest cover and have high predictive mortality
Potential budget savings
1.61
10.07
25.57
39.13
65.89
0
10
20
30
40
50
60
70
> 90-100 % > 80-90 % > 70-80 % > 60-70 % > 50-60 %
Mo
ney
sav
ed (
mill
ion
do
llars
; 2
01
2 t
o 2
01
7)
Predictive plantation mortality
0.15
1.15
0.81 0.86
0.42
3.4
0.1
0.670.51 0.45
0.29
2.02
0.06
0.470.32 0.29
0.18
1.32
0
0.5
1
1.5
2
2.5
3
3.5
4
2015 2016 2017 2018 2019 Overall (2016 to2019)
Mo
ney
sav
ed (
mill
ion
do
llars
)
Year
Predictive mortality >50% Predictive mortality >60% Predictive mortality >70%
Number of plantations = 2204 Potential budget savings – Himachal Pradesh (2012 to 2017)
The global tree restoration potential (Bastin et al. 2019, Science)
• Extra 0.9 billion hectares of canopy cover, which could store 205 GtC of Carbon
• 46% of the carbon sequestration estimate comes from increased tree cover in grasslands, savannas, and shrublands (Veldman et al. 2019)
(Bastin et al. 2019, Science)
Conclusion
• More spending in areas with high predicted plantation mortality
• High budget flow towards ‘non-forest’ areas; low spending in
open forests and increased spending in dense forests
• More tree planting in bio-diverse areas
• Biodiversity rich areas have seen decline in forest cover and
have high predictive mortality
• Very little spending on community-managed plantations
Future directions
• Further research on establishing uncertainty estimates on “global tree
potential” and exploring trade-offs between carbon storage,
biodiversity and livelihoods
• Restoration initiatives should not replace existing natural ecosystems
including natural grasslands and pastures
• Use of modern technological methods and tools including remote
sensing, GIS, AI and drone-based applications
• Community involvement is must – solving land tenure related conflicts
Thank you..
University of IllinoisHimachal Pradesh Forest Department (GIS Lab)Kangjae Lee (UIUC)NASA project and team (Forrest Fleischman led “Impacts of afforestation on sustainable livelihoods in rural communities in India”, LCLUC grant)
Acknowledgements