Using Artificial Neural Network Model for the prediction of forest cover of Rautahat an Bara district. Analyze the changing pattern of the forest in these area.

1. Project Members
Maheshwor Karki (14)
David Nhemaphuki (18)
Bibek Karki (13)
Email id: david_devu@yahoo.com
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2. 1.Background
• Forest degradation a challenge for biodiversity conservation
• Agricultural encroachment, Forest extraction, sand and gravel extraction, Urbanisation
• Forest area 4 billion hectares in the world, 13 million hectares of forest is converting in
other use each year(FRA,2010)
• In Nepal,
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• 20 Terai districts 0.06% during 1990/91 to 2000/2001(Nepal’s forestry outlook
study,2009)
1.7% per year
1978/79 1994
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3. 2.Introduction
Classification, Change Detection
Prediction and Mapping
• Provides underlying picture of
changes in Land use and land
change
• Predict geographic distribution of
the vegetation composition
• Managing natural resources
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4. Contd…
Artificial Neural Network (ANN)
• Is a feed-forward model
• Back propagation learning algorithm
• Improves itself by making corrections to its internal structure
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5. 3. Objective
Main objective
• To predict vegetation using models Artificial Neural Network
Sub objectives
• To prepare the Multi Temporal Vegetation Coverage Map
• To detect the changes
• To find out the spatial pattern of vegetation
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6. 4. Study Area
• Geographic coordinates:
Bara: 27͘͘͘͘͘͘͘͘͘ 2’ N 85͘͘͘ 0’E
Rautahat: 26͘͘͘ 46’ N 85͘͘͘ 16’E
• Area:
Bara: 1190 sq. km
Rautahat :1126 sq. km.
• Climatic Zones:
Lower Tropical
Upper Tropical
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7. 5. Methods
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8. Image Classification
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Figure : Image Classification Algorithm
Downloaded Landsat Images
Radiometric Calibration
Dark pixel subtractions
Image Pre-processing
NDVI
Image Enhancement
Tasselled Cap Brightness
Selection of ROI Image Classification
(Supervised: Maximum
Likelihood Algorithm)
Forest and Non Forest Map
Accuracy Assessment
Composite Image
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9. Change Detection
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Classified Image
1999
Classified Image
2009
Classified Image
2013
Image Differencing
1999-1989
Image Differencing
1999-2009
Image Differencing
2009-2013
Temporal Change Detection
Map
Figure : Change Detection Method
Classified Image
1989
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10. Artificial Neural Network
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• Emulates properties of biological
nervous system and draw on the
analogies of adaptive biological
learning
Why ANNs?
• More accurate than traditional statistical methods
• ANNs can learn from and generalize from experience
• ANNs are universal functional approximator
• Ability to combine data from different source
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11. Artificial Neural Network
Model Development
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Classified Vegetation map
DEM
Slope
Aspect
Distance from road
Distance from Settlement
Input Layer Hidden Layer Output Layer
nodes
Figure : multi-layer perceptron neural network
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12. 6. Result
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13. Accuracy Assessment
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2009 2013
Overall accuracy 95.83% 87.85%
Kappa coefficient 0.91 0.76
study2013_ Non_Forest Forest Ground truth
Non_Forest 60 5 65
Forest 10 63 73
Total 70 70 140
study2009 Non_Forest Forest Grount truth
Non_Forest 60 5 65
Forest 0 55 55
Total 60 60 120
Confusion Matrix: 2009 Confusion Matrix: 2013
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14. Land Cover
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Year
Class
1989 1999 2009 2013
Change(%)
1989-1999
Change(%)
1999-2009
Change(%)
2009-2013
Change(%)
1989-2013
Forest 48940 48003 45134 43140 -1.91 -5.98 -4.42 -11.85
Non-Forest 78282 79215 82084 84078 1.19 3.62 2.43 7.40
Year
Class
1989 1999 2009 2013
Change(%)
1989-199
Change(%)
1999-2009
Change(%)
2009-2013
Change(%)
1989-2013
Forest 29336 27897 25308 25509 -4.91 -9.25 0.79 -13.05
Non-Forest 74285 75726 78315 78114 1.94 3.42 -0.26 5.15
Land cover of Bara district
29336.85
27897.39
25308.36 25509.69
23000
24000
25000
26000
27000
28000
29000
30000
1989 1999 2009 2013
ForestArea(hectares)
Year
Forest Area Rautahat
48940.65
48003.84
45134.91
43140.69
40000
41000
42000
43000
44000
45000
46000
47000
48000
49000
50000
1989 1999 2009 2013
AreaOfforestinhectares
year
Forest Area in Bara
Land cover of Rautahat district
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15. Thursday,July31,2014
Change Maps
Figure: Change map of Bara: 1989-1999 Figure: Change map of Bara: 1999-2009
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16. Thursday,July31,2014
Figure. Change map of Bara: 1989-2013Figure. Change map of Bara: 2009-2013
Cont…
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17. Thursday,July31,2014
Figure. Change map of Rautahat: 1989-1999 Figure. Change map of Rautahat: 1999-2009
Cont…
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18. Thursday,July31,2014
Figure. Change map of Rautahat: 1989-2013Figure. Change map of Rautahat: 2009-2013
Cont…
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19. Deforestation Rate
FAO 1995 formula:
q=((A2-A1)^1/(t2-t1))-1
Where,
q=deforestation rate(% lost areal year)
A1=initial forest area
A2=final forest area
t2-t1=interval in years during which change in land cover is being assessed
Puyravad Formula:(Based on compound interest and more intuitive than FAO formula)
r=1/(t1-t2)lnA2/A1
Where,
r=deforestation rate
A1=initial forest area
A2=final forest area
t2-t1=interval in years during which change in land cover is being
assessed
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20. Deforestation Rate
1989-1999:
r=.001933
1999-2009:
r=.006163
2009-2013:
r=.011297
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0.001933
0.006163
0.011297
0
0.002
0.004
0.006
0.008
0.01
0.012
1989-1999 1999-2009 2009-2013
DeforestationRate
Time Interval
Deforestation Rate in Bara
1989-1999:
r=.0050312
1999-2009:
r=.0097397
2009-2-13:
r=-.001980
0.0050312
0.0097397
-0.00198
-0.004
-0.002
0
0.002
0.004
0.006
0.008
0.01
0.012
1989-1999 1999-2009 2009-2013
DeforestationRate
Time Interval
Deforestation Rate in Rautahat
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21.  Spatial Metrics
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22. Baraspatialmetrics(1989/1999/2009/2013)
class
Metrics
1989 1999 2009 2013
Forest Non-Forest Forest Non-Forest Forest Non-Forest Forest Non-Forest
CA-Class Area 48940 78282 48003 79215 45134 82084 43140 84078
NP-Number of
patches
823 1779 350 2179 147 1043 233 3158
ED-Edge
Density
17.45 18.71 15.83 17.22 11.43 12.74 20.57 21.98
LPI- Largest
patch Index
22.15 55.30 21.66 56.21 10.63 61.43 10.46 62.51
CONTAG 43.4473 44.3296 47.0926 53.9301
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Class
Metrics
1989 1999 2009 2013
Forest Non-Forest Forest Non-Forest Forest Non-Forest Forest Non-
Forest
CA-Class
Area
29336 74285 27897 75726 25308 7831 25509 78114
NP-Number
of patches
364 1017 148 1040 117 1657 376 1609
ED-Edge
Density
11.80 13.28 10.64 12.37 12.61 14.31 16.73 18.39
LPI- Largest
patch Index
26.42 69.33 14.21 71.270 23.22 73.98 11.11 73.46
CONTAG 50.9184 52.3493 53.4769 51.6868
RautahatSpatialmetrics(1989/1999/2009/2013)
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23. 22.1545 21.6623
10.6344 10.4668
0
5
10
15
20
25
1989 1999 2009 2013
LPI
Year
Largest Pitch Index Bara
17.4534
15.8349
11.436
20.5733
0
5
10
15
20
25
1989 1999 2009 2013
EdgeDensity
Year
Edge Density Bara
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823
350
1478
233
0
200
400
600
800
1000
1200
1400
1600
1989 1999 2009 2013
NOofforestpatch
Year
Number of forest patches in Bara
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24. 11.8069
10.6469
12.6127
16.7341
0
2
4
6
8
10
12
14
16
18
1989 1999 2009 2013
EdgeDensity
Year
Edge Density Rautahat
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26.4208
14.2181
23.223
11.1175
0
5
10
15
20
25
30
1989 1999 2009 2013
LargestPatchIndex(LPI)
Year
Largest Patch Index Rautahat
364
148
117
376
0
50
100
150
200
250
300
350
400
1989 1999 2009 2013
NoofForestpatches
Year
Number of Forest patches
Rautahat
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25.  Prediction
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26. Figure: Predicted Land cover map of 2013Figure: Classified Land cover map of 2013 Figure: Change map
Prediction of 2013 land cover
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Classified 2013 Predicted 2013
Bara(forest) Rautahat(forest) Bara(forest) Rautahat(forest)
Area(hectare) 43140 25509 41437 23143
NP 233 376 898 699
ED 20.57 16.73 18.68 14.43
LPI 10.46 11.11 11.12 10.72
CONTAG 53.93 51.68 65.37 70.94
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27. Thursday,July31,2014
Figure: Predicted land cover map of the Study area
Prediction of 2020 land cover
Forest area change
(2013-2020):
=68648 -64587
=4061
Deforestation rate
during 2013-2020:
0.0057
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28. Discussion
• From the year 1989-2013, 5800 and 3800 hectare of forest area has been decreased
in Bara and Rautahat
• ICIMOD - Nepal Land Cover Map 1990 and 2010 deforestation rate is -0.0002 while
the deforestation rate of Bara is 0.0009. Our study shows that there is high
deforestation in both districts with deforestation rate of 0.0040and 0.0074
• Forest area has been increased in Rautahat during 2009-2013 due to people’s
participation and community forest programme through collaborative forest
management
• The predicted rate of deforestation is 0.0057 during 2013-2020 of study area
• There is high annual rate of deforestation rate during 1999-2009 in both districts in
our case.
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29. Limitations
 In field:
• All places are not accessible due to limited time
• Data collection time is limited in the morning and evening due to high
temperature
 Data
• Availability of data is a major problem of this study
• There is no data for the accuracy assessment of the classified image of the year
1989 and 1999
• Social, economic, political and cultural factors have not considered here
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30. Recommendation
• Other variables like solar radiation, climatic data, soil data can be used
• Exchange between forest sub classes could be computed and predicted
• Accuracy assessment of 1989 and 1999 could be done
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31. Conclusion
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• Change detection and prediction of vegetation mapping of Bara and Rautahat was
done
• Rate of deforestation for 2013-2020 has been predicted 0.0057 and also showing
significant change of forest in the past
• Timber extraction, urbanisation, agricultural encroachment, sand and gravel
extraction, soil erosion are found to be major cause of deforestation
• High rate of deforestation near the forest boundary and settlement area than near
the highway
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32. Thank you!
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33. Thursday,July31,2014
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34. Thursday,July31,2014
Explanatory Variable Cramer’s V Test
Distance from roads 0.1703
Distance from settlement 0.4782
DEM 0.7927
Slope 0.2665
Aspect 0.1609
Level of association Verbal description Comments
0.000 No Relationship Independent variable does not help in predicting the
dependent variable
0.00 to 0.15 Very weak Not generally acceptable
0.15 to 0.20 Weak Minimally acceptable
0.20 to 0.25 Moderate Acceptable
0.25 to 0.30 Moderately strong Desirable
0.30 to 0.35 Strong Very desirable
0.35 to 0.40 Very strong Extremely desirable
0.40 to 0.50 Worrisomely strong Either an extremely good relationship or the two variables
are measuring the same concept
0.50 to 0.99 Redundant The two variables are probably measuring the same concept
1.00 Perfect relationship If we know the independent variable, we can perfectly
predict the dependent variable
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