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