1. The use of Remote Sensing in Land Cover Mapping and Change Detection in Somalia Simon Mumuli Oduori, Ronald Vargas Rojas, Ambrose Oroda and Christian Omuto 12 th June, 2007. Nairobi, Kenya

2. Land Cover (LC) The Observed Biophysical Cover on the Earth’s Surface. The tangible indicator of the kind of ecological activity taking place on the earth’s surface. Significantly modified by human activities (negatively & positively). Effort is to fight negative effects Therefore Characterisation is Imperative GIS and Remote Sensing are Imperative tools Need to assess Land cover mapping and change detection methodologies in arid environments.

3. Objectives  To compare land cover mapping methodologies (accuracy assessment): visual satellite image interpretation and automatic image classification.  To detect land cover changes in time using both previous methodologies.  To detect the impact of Settlement & Water Points on land cover.

4. The Study Area North and south of Garowe 7.000 km 2 each (see Figure). Different eco-regions. Includes Sool Plateau and Sanag Plateaus, the Nugal Valley and the Mudug Plain. The climate of the study area is classified as arid, with rainfall ranging between 100 to 200 mm per year. Relative Humidity for the area of study is 60% to 70%. Temperature varies between 20° to 28°C.

6. Methods Bibliographic Research (libraries, NGOs, UN-Agencies etc) Visual satellite image interpretation approach using LCCS. Supervised classification using the nearest neighborhood algorithm in ERDAS Imagine Software using LCCS. Multi-temporal satellite images (1973, 2001 and 2006) for land cover change assessment. Normalized Difference Vegetation Index (NDVI) has been used to assess the vegetative vigor around these areas (NDVI = (IR – R) / (IR + R)) over time. Field survey for land cover data collection, together with semi- structured interviews with local people. An accuracy assessment for land cover mapping. Integration of land cover maps, NDVI, water and settlement points in a GIS analysis for detecting impacts.

13. RESULTS: supervised classification land cover maps (1988- 2001)

14. RESULTS: accuracy assessment (North Garowe) Interpretat ion Units Checked Land Cover Class Units Found to Belong to Sparse Vegetation Bare Lands Open Shrubs Shrubs with Emergent Trees Tiger Bush Settlement Open Herbaceous Closed Herbaceous Open Trees Accuracy (%) 22 Sparse Vegetation 1902000100 21 Bare Lands 477000300 32 Open Shrubs 2028000201 6 Shrubs with Emergent Trees 001500000 12 Tiger Bush 0000111000 0 Settlement 000000000 0 Open Herbaceous 000000000 0 Closed Herbaceous 000000000 1 Open Trees 000000001 94 Total 25738511160276

15. RESULTS: accuracy assessment The accuracy with which each land cover has been Classified by the digital method is the following: 1. Sparse Vegetation is 19/25*100 = 76% 2. Open Shrubs is 28/38*100 = 74% 3. Bare Lands is 7/7*100 = 100% 4. Shrubs with Emergent Trees is 5/5*100 = 100% 5. Tiger Bush is 11/11*100 = 100% 6. Open Trees is 1/2*100 = 50%

16. RESULTS: polygon land cover maps (2001)

17. RESULTS: accuracy assessment (North Garowe Interpretation Units Checked Land Cover Class Units Found to Belong to Sparse Vegetation Bare Lands Open Shrubs Shrubs with Emergent Trees Tiger Bush SettlementOpen Herbaceous Closed Herbaceous Open Trees Accuracy (%) 15 Sparse Vegetation 733000101 0 Bare Lands 000000000 20 Open Shrubs 628000301 6 Shrubs with Emergent Trees 204000000 18 Tiger Bush 2020121100 0 Settlement 000000000 2 Open Herbaceous 200000000 8 Closed Herbaceous 521000000 25 Open Trees 2122000000 94 Total 26840012150230

18. RESULTS: accuracy assessment The accuracy with which each land cover has been interpreted by the Visual Interpretation is the following: 1. Sparse Vegetation is 7/26*100 = 27% 2. Open Shrubs is 28/38*100 = 74% 3. Shrubs with Emergent Trees is 0% 4. Tiger Bush is 12/12*100 = 100% The vegetation in this area is highly heterogeneous.

19. RESULTS: some findings The main confusion was between Bare Lands and Sparse Vegetation. The Tiger Bush is very distinct and easy to identify visually. The Open Shrubs were also easy to identify, though they were confused with Sparse Vegetation. However, it must be noted that the preliminary interpretation was done without any prior local field knowledge. Again, some land cover classes were not reached due to the poor road network in the study area. The vegetation in arid environments is very heterogeneous, therefore difficult to map using visual interpretation. That’s explains the low accuracy.

20. RESULTS: some findings The accuracy assessment for South Garowe is: Visual interpreted map: 78% Automatic supervised classification map: 85% The reason behind this accuracy in comparison with North Garowe is that this area is generally homogeneous in terms of land cover/ vegetation. Therefore, both methods can represent in a better way this spatial variability.

21. RESULTS: change detection by visual interpretation

23. ClassesHectares% change 2HL8106.020.01 2HR140.260.01 2SR6527.250.04 2TP8225.960.02 5U45.390.00 6S-1044.89-0.07 Statistics on surface and percentage variation from 1985-88 to 2000-01 (changes are always less than 0.1%). Conclusion  No changes of land cover in the observed period  The few changes, only limited to a small decrease of bare soil cover

24. RESULTS: change detection by automatic classification SNo.Land CoverArea in 1988 (Ha) Area in 2001(Ha) Land Cover Change Ha) 1Woodlands70 6868 288-62 398 2Open Shrubs91 0400853 915-56 485 3Open Herbaceous 239 00796 545-142 462 4Sparse Herbaceous/ Sparse Shrubs 82 972185 561+102 589 5Bare Soils93 971252 580+158 609 Statistics on surface and percentage variation from 19888 to 2001 (changes are in some cases greater than 50%). Conclusion  Considerable changes of land cover in the observed period  The changes are in all the land cover classes

25. RESULTS: detecting impact from settlement (1988-2001)

27. CONCLUSIONS  One polygon based land cover map and two pixel based land cover maps were generated (1988-2001). An accuracy assessment for both was calculated. Although the difference in accuracy, both approaches have their pros and cons for representing complex land cover patterns as those of an arid environment.  Significant land cover change was detected using the raster based map. The Visual interpretation map identified few changes in land cover. The main reason can be related to scale issues (minimum mapping unit).  Digital classification is more reliable in detecting very small changes in land cover. It is useful in arid environments like Somalia. However, its fuzzy pattern is not well acknowledged.

28. CONCLUSIONS Vegetated land had changed to bare lands over the years due mainly, as recorded in the interviews and some field perceptions, to overgrazing and tree cutting for charcoal burning. Settlements and water-points contributed to increased pressure on the fragile environment and consequently caused negative changes in land cover around them during the period between 1973 and 2006.

29. Thank You for Listening