Forest Degradation Assessment
in Sri Lanka
Raushan Kumar, Forestry Officer, FAO Sri Lanka
Nishantha Edirisinghe, Deputy Conservator of Forest
Sharat Kulatunga, Additional Conservator General of Forest
Date: 16 June 2015
Presentation Outline
History of forest degradation in Sri Lanka
Country approach of forest degradation assessments
Results
Why Measure and Monitor Forest Degradation
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In Sri Lanka 82% of the land area is under some form of state control
Over the years, state land, under forest cover, has been alienated under various
schemes, such as village expansion, regularization of encroachments, special leases,
and others.
From 1830, vast tracts of forest at middle altitudes were cleared for coffee
plantation and were replaced by tea from year 1850, after the coffee plantations
were devastated by a leaf-blight.
Forest clearance in the dry zone began around 1869, due to introduction of large
colonization schemes.
Why Measure and Monitor Forest Degradation
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Since year 1869, the closed canopy (> 70% canopy cover) of natural forest cover has
declined rapidly from about 80%, until, in 1999 it was estimated at less than 22%.
At the same time, the closed canopy natural forest cover is expected to decline from
22.7% in 1999 to about 17% by 2020, if no planned action is taken.
The continuous economic growth and increasing population has influenced the
demand for industrial round wood and poles rising from 1.7 million cubic meters in
1995 to 2.0 million cubic meters in 2020.
However, the present forest cover of the island is considered to be about 30% of the
total land area.
Forest degradation results in decline in biomass, carbon sequestration capacity,
goods and services received from forest ecosystem
Decline in Canopy Cover, Species
Richness / Composition
Indicator of Forest Degradation
Causes of Forest Degradation Sri Lanka
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Illicit Felling of trees
Shifting Cultivation
Cattle Damage / Livestock
Grazing
Illegal Cultivation
Encroachment
Extraction of gravel, mineral and
metal
Type of damage
Underlying drivers
Degradation of Local demand for
forests
timber and wood
products for
household, Industrial
and Infrastructure
Catalysts
Inhibitors
Limitations of
monitoring capacity,
Political
interference,
Population growth
Forest policies and protected area
management , Home gardens ,
Migration,
Community dependence and
customary rights
Forest Cover %
50%
45%
Dense Forests
44%
40%
Open Forests
35%
30%
27%
25%
24%
23%
22%
20%
15%
10%
8%
7%
7%
5%
0%
1956
1983
1992
1999
2010
Dense forest (Canopy Cover over 40%)-22%
Sparse Forests (Canopy Cover 10-40%)- 7.7 %
Forest Type
Percentage
Lowland Rain Forests
1.9
Moist Monsoon Forests
1.8
Dry Monsoon Forests
17.1
Montane Forests
0.7
Sub Montane Forests
0.4
Riverine Dry Forests
0.0
Mangrove Forest
0.2
Savannah Forest
1.0
Open and Sparse Forest
6.5
Total
29.7
Forest Degradation Definition (FAO, FRA, 177)
Parameter
Forest type
Secondary forest
Change within the forest
Structure
Crown cover
Species composition
Stocking
Reduction of capacity to provide
Productivity
Goods
Services
Carbon stocks
Other functions
Time scale
Cause - human- induced
Cause - natural
Reference state
Natural forest
Site
Carbon stock at initial date
FAO 2000 FAO 2001, 2006
FAO 2003
10%
long
long
UNEP/ CBD 2001
Study Area:Polonnaruwa District, Kandagama Forest
Steps in Forest Degradation Assessment in Sri Lanka
Forest Type Map
LANDSAT and Google Image
Based Monitoring (NDVI)
Instruction to Field Staffs / Field
Checks / Primary Selection of
Forest Degradation Sites
Causes of Degradation
Identification
Circular Plot, Ground Inventory
(Tree, Plants, Shrubs and Seedlings)
Analysis
Tree Density
Species Composition /
Richness
Forest Degradation
Threshold
Regeneration
Level
Site Specific
ANR Plan
Results: Forest Degradation Assessment
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Per Hectare Tree Density in
Polannaruwa district degraded
forest lands
 Minimum = 1
 Maximum = 133
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Density Thresholds preparation:
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1 to 25
26 to 50
50 to 75
75 to 100
> 100
Forest Name
Kandegama
Density Class
Minimum
Average
Maximum
Kumaragalakantha Minimum
Average
Maximum
Minimum
Meegaswewa
Average
Maximum
Pimburanththewa Minimum
Average
Maximum
Minimum
Welweri
Average
Maximum
Per hectare
1.6
10.8
68.4
10.1
24.2
50.5
0.8
9.1
132.9
1.6
18.2
130.5
5.1
20.2
106.1
Results: Forest Degradation Assessment
Plot ID Plot-1 Plot-2 Plot-3 Plot-4 Plot-5 Plot-6 Plot-7 Plot-8 Plot-9 Plot-10 Plot-11 Plot-12
NDVI
-0.024 -0.037 0.039 0.012 0.106 0.126 0.084 0.036 0.007 0.053 0.133 0.015
Species
0.00 2.81 2.09 2.55 3.00 2.26 3.00 2.82 1.74 3.20 3.82 2.70
Richness
4.50
4.30
4.10
3.90
3.70
3.50
3.30
3.10
2.90
2.70
2.50
2.30
2.10
1.90
1.70
1.50
1.30
1.10
0.90
0.70
0.50
0.30
0.10
-0.10
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
Plot-1 Plot-2 Plot-3 Plot-4 Plot-5 Plot-6 Plot-7 Plot-8 Plot-9 Plot-10 Plot-11 Plot-12
-0.06
Species Richness
NDVI
Results: Forest Degradation Assessment
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Polonnaruwa District:
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Country Level:
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Area of Polonnaruwa District: 329,300 ha
Natural Forest area 134,801 ha
Scrub Forest: 16,850 ha
Area under forest degradation / restoration: 900 ha
Species Name Importance
Value Index
(IVI)
Tharana
24.94
Wali Kohu
17.82
Welan
17.74
Maila
15.23
Borathamana
14.97
Searae
14.24
Milla
11.75
Badulla
10.59
Country Geographical Area: 6,570,134 ha
Total Forest Area: 1,951,330 ha (29.7%)
Total Forest Degraded area : Aprox. 160,000 ha (8.2%)
They are located both in central highlands (wet zone) and lowland areas (dry zone).
Restoration:
 Species selection is site specific and based on importance and adaptability
 Site specific disturbances are taken into consideration for species selection
Conclusion
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This study indicates, potential use of ground based inventory in combination with
remote sensing technology for forest degradation monitoring.
Out of total 12 plots 9 shows similar NDVI trends as compared to species richness
NDVI and LAI (or other VI depending on country and site) is overall indicator of
health of plants whereas species richness indicates diversity and ecosystem health
on ground.
If Selected Vis and Ecological Indicator if combined together these two have
potential to meet goals set in forest degradation definitions for country
circumstances.
While countries forest departments have their ground force; addition of Remote
Sensing increased cost effectiveness of monitoring mechanism
Effective institutional arrangement and functional NFI is very essential
Raushan Kumar
Raushan.Kumar@fao.org
Thank You
Website: http://www.un-redd.org
Picture Source: http://www.crisp.nus.edu.sg/~research/tutorial/optical.htm