Predictive Vegetation Mapping of Bara and Rautahat Districts Using ANN
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The document discusses a study on deforestation and vegetation mapping in the Bara and Rautahat districts of Nepal, utilizing artificial neural networks for detecting changes and predicting vegetation coverage. The research highlights significant forest area loss from 1989 to 2013 and predicts a continuing deforestation rate of 0.0057 from 2013 to 2020. Major causes of deforestation include agricultural encroachment, urbanization, and sand extraction, with an observed increase in forest area due to community forest programs in Rautahat during 2009-2013.
Predictive Vegetation Mapping of Bara and Rautahat Districts Using ANN
- 1. Project Members Maheshwor Karki (14) David Nhemaphuki (18) Bibek Karki (13) Email id: david_devu@yahoo.com 1
- 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, Thursday,July31,2014 • 20 Terai districts 0.06% during 1990/91 to 2000/2001(Nepal’s forestry outlook study,2009) 1.7% per year 1978/79 1994 2
- 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 Thursday,July31,2014 3
- 4. Contd… Artificial Neural Network (ANN) • Is a feed-forward model • Back propagation learning algorithm • Improves itself by making corrections to its internal structure Thursday,July31,2014 4
- 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 Thursday,July31,2014 5
- 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 Thursday,July31,2014 6
- 8. Image Classification Thursday,July31,2014 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 8
- 9. Change Detection Thursday,July31,2014 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 9
- 10. Artificial Neural Network Thursday,July31,2014 • 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 10
- 11. Artificial Neural Network Model Development Thursday,July31,2014 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 11
- 13. Accuracy Assessment Thursday,July31,2014 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 13
- 14. Land Cover Thursday,July31,2014 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 14
- 15. Thursday,July31,2014 Change Maps Figure: Change map of Bara: 1989-1999 Figure: Change map of Bara: 1999-2009 15
- 16. Thursday,July31,2014 Figure. Change map of Bara: 1989-2013Figure. Change map of Bara: 2009-2013 Cont… 16
- 17. Thursday,July31,2014 Figure. Change map of Rautahat: 1989-1999 Figure. Change map of Rautahat: 1999-2009 Cont… 17
- 18. Thursday,July31,2014 Figure. Change map of Rautahat: 1989-2013Figure. Change map of Rautahat: 2009-2013 Cont… 18
- 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 Thursday,July31,2014 19
- 20. Deforestation Rate 1989-1999: r=.001933 1999-2009: r=.006163 2009-2013: r=.011297 Thursday,July31,2014 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 20
- 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 Thursday,July31,2014 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) 22
- 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 Thursday,July31,2014 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 23
- 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 Thursday,July31,2014 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 24
- 26. Figure: Predicted Land cover map of 2013Figure: Classified Land cover map of 2013 Figure: Change map Prediction of 2013 land cover Thursday,July31,2014 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 26
- 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 27
- 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. Thursday,July31,2014 28
- 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 Thursday,July31,2014 29
- 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 Thursday,July31,2014 30
- 31. Conclusion Thursday,July31,2014 • 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 31
- 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 34