Rangeland Degradation Assessment in the South Slope of Al-Jabal Al-Akhdar, Northeast Libya Using Remote Sensing Technology
محورهای موضوعی : Relationship between Animal and RangelandAdel M.A. Mahmoud 1 , I. Mohd Hasmadi 2 , M.S. Alias 3 , A. Mohamad Azani 4
1 - Universiti Putra Malaysia
2 - Universiti Putra Malaysia
3 - Universiti Putra Malaysia
4 - Universiti Putra Malaysia
کلید واژه: Vegetation indices, Al-Jabal Al-Akhdar, Libya MSAVI2, Total Patch Area,
چکیده مقاله :
The degradation rate of Mediterranean steppes, especially in North Africa is 1% per year, and this considered a high rate of degradation. This study conducted in 2014 in the south slope of Al-Jabal Al-Akhdar, northeast Libya to quantify the vegetation recovery rate and assess selected Vegetation Indices (VIs) for mapping rangelands degradation status using remote sensing technology. Through a review of VIs we found that NDVI (Normalized Difference Vegetation Index) and MSAVI2 (Modified Soil Adjusted Vegetation Index) are the most useful indices for the study area to achieve the research objectives. Two Landsat (ETM+) satellite images (captured in September 2006 and 2014) used to map, monitor and assess the patterns of changes in plant cover. Three exclosures (fenced areas) with moderately to severely degraded soil and vegetation, were selected along a strong north-south rainfall gradient. Landscape Function Analysis (LFA) technique used to calculate Total Patch Area (TPA) for comparison purpose. According to the results, NDVI and MSAVI2 can be employed as a consistent and comparatively simple to use a tool in management and assessment of desertification processes in the Mediterranean rangelands. It seems that MSAVI2 more reliable than NDVI when the vegetation cover is very low. Overall, the plant cover did not change or increase for a large portion of regions at a time when 80% of the study area still under very severe and severe conditions of land degradation status.
Bai, Z., Dent, D., Olsson, L. and Schaepman, M., 2008. Global assessment of land degradation and improvement 1: Identification by remote sensing. Report 2008/01, FAO/ISRIC-Rome/Wageningen.
Bannari, A., Morin, D., Bonn, F. and Huete, A., 1995. A review of vegetation indices. Remote Sensing Reviews, 13(1-2): 95-120.
Chen, Y., 1999. Correlation of saltbush cover measurements to tm wavebands and vegetation indices. Geoscience and Remote Sensing Symposium, 1999. IGARSS'99 Proceedings. IEEE 1999 International: 2590-2592.
Clevers, J., 1988. The derivation of a simplified reflectance model for the estimation of leaf area index. Remote Sensing of Environment, 25(1): 53-69.
Fernández, N., Paruelo, J. M. and Delibes, M., 2010. Ecosystem functioning of protected and altered mediterranean environments: A remote sensing classification in doñana, spain. Remote Sensing of Environment, 114(1): 211-220.
Gao, B. C., 1996. Ndwia normalized difference water index for remote sensing of vegetation liquid water from space. Remote sensing of environment, 58(3): 257-266.
Gaitán, J. J., Bran, D., Oliva, G., Ciari, G., Nakamatsu, V., Salomone, J., Ferrante, D., Buono, G., Massara, V. and Humano, G., 2013. Evaluating the performance of multiple remote sensing indices to predict the spatial variability of ecosystem structure and functioning in patagonian steppes. Ecological indicators, 34: 181-191.
Hall, R., Skakun, R., Arsenault, E. and Case, B., 2006. Modeling forest stand structure attributes using landsat ETM+ data: Application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, 225(1): 378-390.
Huete, A. and Jackson, R., 1988. Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment, 25(1): 89-105.
Ikeda, H., Okamoto, K. and Fukuhara, M., 1999. Estimation of aboveground grassland phytomass with a growth model using landsat tm and climate data. International Jour. Remote Sensing, 20(11): 2283-2294.
Jackson, R. D. and Huete, A. R., 1991. Interpreting vegetation indices. Preventive Veterinary Medicine, 11(3): 185-200.
Jenks, George F., 1967. "The Data Model Concept in Statistical Mapping", International Yearbook of Cartography 7: 186–190.
Karnieli, A., Bayarjargal, Y., Bayasgalan, M., Mandakh, B., Dugarjav, C., Burgheimer, J., Khudulmur, S., Bazha, S. and Gunin, P., 2013. Do vegetation indices provide a reliable indication of vegetation degradation? A case study in the mongolian pastures. International Jour. Remote Sensing, 34(17): 6243-6262.
Le Houerou, H. N., 2000. Restoration and rehabilitation of arid and semiarid mediterranean ecosystems in north africa and west asia: A review. Arid Soil Research and Rehabilitation, 14(1): 3-14.
Le Houérou, H. N., 2001. Biogeography of the arid steppeland north of the sahara. Jour. Arid Environments, 48(2): 103-128.
Liu, A., Wang, J., Liu, Z. and Wang, J., 2005. Monitoring desertification in arid and semi-arid areas of china with noaa-avhrr and modis data. Geoscience and Remote Sensing Symposium, 2005. IGARSS'05. Proceedings. 2005 IEEE International: 2362-2364.
Mahmoud, A., Gadallah, A., Mohemmed, S., Mohamed, M., Abdel-Ghani, A., Alhendawi, R. and Russell, P. J., 2008. Aspects of range condition recovery in the southern jebel al akhdar, northeastern libya. Proceedings of the XXI International Grassland Congress and the VIII International Rangeland Congress (volumeⅠ), China.
Mróz, M. and Sobieraj, A., 2004. Comparison of several vegetation indices calculated on the basis of a seasonal spot xs time series, and their suitability for land cover and agricultural crop identification. Technical Sciences, 7: 39-66.
Najeeb, A. A., 2009. Estimation of the normalized difference vegetation index (ndvi) variation for selected regions in iraq for two years. Jour. University of Anbar for pure science, 3(3): 1991- 8941.
Nicholson, S. E., Davenport, M. L. and Malo, A. R., 1990. A comparison of the vegetation response to rainfall in the sahel and east africa, using normalized difference vegetation index from noaa avhrr. Climatic Change, 17(2-3): 209-241.
Owusu, A. B., 2013. Detecting and quantifying desertification in the upper east region of ghana using multi-spatial and multi-temporal normalized difference vegetation index. Jour. Environment and Earth Science, 3(10): 62-78.
Phillips, R., Beeri, O., Scholljegerdes, E., Bjergaard, D. and Hendrickson, J., 2009. Integration of geospatial and cattle nutrition information to estimate paddock grazing capacity in northern us prairie. Agricultural systems, 100(1): 72-79.
Qi, J., Chehbouni, A., Huete, A., Kerr, Y. and Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2): 119-126.
Rouse Jr, J., Haas, R., Schell, J. and Deering, D., 1974. Monitoring vegetation systems in the great plains with erts. NASA special publication, 351: 309.
Senseman, G. M., Tweddale, S. A., Anderson, A. B. and Bagley, C. F. 1996. Correlation of land condition trend analysis (lcta) rangeland cover measures to satellite-imagery-derived vegetation indices. DTIC Document.
Tongway, D. and Hindley, N., 2004. Landscape function analysis: A system for monitoring rangeland function. African Jour. Range and Forage Science, 21(2): 109-113.
Tongway, D. J. and Ludwig, J. A., 2011. Restoring disturbed landscapes: Putting principles into practice, Island Press.
Yeganeh, H., jamale Khajedein, S., Amiri, F. and Shariff, A. R. B. M., 2014. Monitoring rangeland ground cover vegetation using multitemporal modis data. Arabian Jour. Geosciences, 7(1): 287-298.
Zatout, M. M., 2014. Effect of negative human activities on plant diversity in the jabal akhdar pastures. International Jour. Bioassays, 3(9): 3324-3328.
