Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia

Asirat Teshome Tolosa

doi:doi:10.11648/j.ijema.20180606.12

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Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia

Asirat Teshome Tolosa

Published in International Journal of Environmental Monitoring and Analysis (Volume 6, Issue 6)

Received: 30 November 2018 Accepted: 11 December 2018 Published: 2 January 2019

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Abstract

Soil erosion is one of the natural resources which can be influenced by Land use land cover change (LCC). The main influencing factor for land use land cover change is the increase of population, which in turn resulted in land degradation. This study aimed at modeling and analyzing LCC and its effect on soil erosion. The study was conducted in the highlands of, Blue Nile Basin, Ethiopia. Three Landsat images (1986, 2000 and 2016) were used to analyze the LCC. Supervised classification using maximum likelihood algorism was used to analyze the LCC. Four land cover types (LCTs) cropland, forest, and grassland and shrubland were defined. Multi-criteria decision analysis (MCE) using the Analytic Hierarchy Process (AHP) was used to prioritize the most influencing factor for soil erosion. Five major factors; land use, slope, soil types, Topographic Wetness Index (TWI) and altitude were considered to analyze the erosion hotspot area. The result showed that cropland and grassland increased from 41.6% and 15.4% in 1986 to 58.8% and 28.3% in 2016, respectively. However, shrub-land and forest decline from 32.3% and 10.6% in 1986 to 5.6% and 7.3% in 2016, respectively. The AHP analysis showed that LCT is the most contributors for erosion. It is observed that free grazing in the area is the common practice which is the main contributor to erosion. Hence, 50% of the gully erosion is influenced by LCT. The resultant erosion risk map shows that 1.12% of the area lies under the low-risk zone, whereas 19.02%, 72.67% and 7.2% of the total area fall in medium, high and very high-risk categories respectively. The results verified by field data collected and the judgment of the experts.

Published in	International Journal of Environmental Monitoring and Analysis (Volume 6, Issue 6)
DOI	10.11648/j.ijema.20180606.12
Page(s)	152-166
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

GIS, Landsat, Remote Sensing, Analytic Hierarchy Process, MCE, Supervised Classification

References

[1]	Manson, S. M., 2005. Agent-based modeling and genetic programming for ´ n modeling land change in the Southern Yucatan Peninsular Region of Mexico. Agriculture, Ecosystems and Environment, 111, pp. 47-62.
[2]	Goldewijk, K. K. & Ramankutty, N., 2004. Land cover change over the last three centuries due to human activities : The availability of new global data sets., 61, pp. 335-344.
[3]	Zeleke, G. & Hurni, H., 2001. Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands Implications of Land Use and Land Cover Dynamics for Mountain Resource Degradation in the Northwestern Ethiopian Highlands., 21 (2), pp. 184-191.
[4]	Wondie, M. et al., 2011. Spatial and temporal land cover changes in the semen mountains national park, a world heritage Site in northwestern Ethiopia. Remote Sensing, 3 (4), pp. 752-766.
[5]	Alemu, B., 2015. The Effect of Land Use Land Cover Change on Land Degradation in the Highlands of Ethiopia. Journal of Environment and Earth Science, 5 (1), pp. 1-13.
[6]	Wondie, M. et al., 2016. Modeling the dynamics of landscape transformations and population growth in the highlands of Ethiopia using remote-sensing data. International Journal of Remote Sensing, 37 (23), pp. 5647-5667.
[7]	Fisseha, G. et al., 2011. Analysis of land use/land cover changes in the Debre-Mewi watershed at the upper catchment of the Blue Nile Basin, Northwest Ethiopia., 1 (6), pp. 184-198.
[8]	Lunetta, R., Ediriwickrema, J. & Johnson, D., 2002. Impacts of vegetation dynamics on the identification of land-cover change in a biologically complex community in North Carolina, USA. Remote Sensing of, 82, pp. 258-270.
[9]	Molla, M., 2015. Land Use/Land Cover Dynamics in the Central Rift Valley Region of Ethiopia: Case of Arsi Negele District. African Journal of Agricultural Research, 10, pp. 434-449.
[10]	Lunetta, R. & Elvidge, C., 1999. Remote sensing change detection.
[11]	Hurni, H., Tato, K. & Zeleke, G., 2005. The implications of changes in population, land use, and land management for surface runoff in the upper Nile basin area of Ethiopia. Mountain Research and Development.
[12]	Chang, T. & Bayes, T., 2013. Development of erosion hotspots for a watershed. Journal of Irrigation and Drainage.
[13]	Lambin, E., Geist, H. & Lepers, E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual review of the environment.
[14]	Vrieling, A., Sterk, G. & Beaulieu, N., 2002. Erosion risk mapping: a methodological case study in the Colombian Eastern Plains. Journal of Soil and Water.
[15]	Conforti, M. et al., 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Natural hazards, 56, p.: 881-898.
[16]	Ganasri, B. & Ramesh, H., 2015. Assessment of soil erosion by RUSLE model using remote sensing and GIS: A case study of Nethravathi Basin. Geoscience Frontiers.
[17]	Bewket, W. & Teferi, E., 2009. Assessment of soil erosion hazard and prioritization for treatment at the watershed level: case study in the Chemoga watershed, Blue Nile basin, Ethiopia. Land Degradation & Development, 20, pp. 609-622.
[18]	Bewket, W. & Sterk, G., 2002. Farmers’ participation in soil and water conservation activities in the Chemoga watershed, Blue Nile basin, Ethiopia. Land Degradation & Development, 13, pp. 189-200.
[19]	Assefa, et al., Identification of Erosion Hotspot Area using GIS and MCE Technique for Koga Watershed in the Upper Blue Nile Basin, Ethiopia.
[20]	Amsalu, A., Stroosnijder, L. & Graaff, J. De, 2007. Long-term dynamics in land resource use and the driving forces in the Beressa watershed, highlands of Ethiopia. Journal of Environmental Management, 83, pp. 448-459.
[21]	Hurni, H. & Ludi, E., 2000. Reconciling conservation with sustainable development: a participatory study inside and around the Simen Mountains National Park, Ethiopia.
[22]	Ahmad, P. & Prasad, M., 2011. Environmental adaptations and stress tolerance of plants in the era of climate change.
[23]	Satty, T., 1980. The analytical hierarchy process: planning, priority setting, resource allocation. RWS publication, Pittsburg.
[24]	Mottet, A. et al., 2006. Agricultural land-use change and its drivers in mountain landscapes: A case study in the Pyrenees. Agriculture, ecosystems.
[25]	Saeed, S. et al., 2014. Impact of Altitude on Soil Physical and Chemical Properties in Sra Ghurgai (Takatu mountain range) Quetta, Balochistan., 5 (3), pp. 730-735.
[26]	Dejene, A., 2003. Integrated Natural Resources Management to Enhance Food Security.
[27]	Fu, B. & Chen, L., 2000. Agricultural landscape spatial pattern analysis in the semi-arid hill area of the Loess Plateau, China. Journal of Arid Environments, 44, pp. 291-303.
[28]	Western, A. & Grayson, R., 1998. The Tarrawarra dataset: Soil moisture patterns, soil characteristics, and hydrological flux measurements. Water Resources Research.
[29]	Beven, K. & Kirkby, M., 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable del’hydrologie du bassin versant. Hydrological Sciences Journal, 24, pp. 43-69.

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APA Style

Asirat Teshome Tolosa. (2019). Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. International Journal of Environmental Monitoring and Analysis, 6(6), 152-166. https://doi.org/10.11648/j.ijema.20180606.12

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ACS Style

Asirat Teshome Tolosa. Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. Int. J. Environ. Monit. Anal. 2019, 6(6), 152-166. doi: 10.11648/j.ijema.20180606.12

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AMA Style

Asirat Teshome Tolosa. Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. Int J Environ Monit Anal. 2019;6(6):152-166. doi: 10.11648/j.ijema.20180606.12

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@article{10.11648/j.ijema.20180606.12,
  author = {Asirat Teshome Tolosa},
  title = {Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia},
  journal = {International Journal of Environmental Monitoring and Analysis},
  volume = {6},
  number = {6},
  pages = {152-166},
  doi = {10.11648/j.ijema.20180606.12},
  url = {https://doi.org/10.11648/j.ijema.20180606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20180606.12},
  abstract = {Soil erosion is one of the natural resources which can be influenced by Land use land cover change (LCC). The main influencing factor for land use land cover change is the increase of population, which in turn resulted in land degradation. This study aimed at modeling and analyzing LCC and its effect on soil erosion. The study was conducted in the highlands of, Blue Nile Basin, Ethiopia. Three Landsat images (1986, 2000 and 2016) were used to analyze the LCC. Supervised classification using maximum likelihood algorism was used to analyze the LCC. Four land cover types (LCTs) cropland, forest, and grassland and shrubland were defined. Multi-criteria decision analysis (MCE) using the Analytic Hierarchy Process (AHP) was used to prioritize the most influencing factor for soil erosion. Five major factors; land use, slope, soil types, Topographic Wetness Index (TWI) and altitude were considered to analyze the erosion hotspot area. The result showed that cropland and grassland increased from 41.6% and 15.4% in 1986 to 58.8% and 28.3% in 2016, respectively. However, shrub-land and forest decline from 32.3% and 10.6% in 1986 to 5.6% and 7.3% in 2016, respectively. The AHP analysis showed that LCT is the most contributors for erosion. It is observed that free grazing in the area is the common practice which is the main contributor to erosion. Hence, 50% of the gully erosion is influenced by LCT. The resultant erosion risk map shows that 1.12% of the area lies under the low-risk zone, whereas 19.02%, 72.67% and 7.2% of the total area fall in medium, high and very high-risk categories respectively. The results verified by field data collected and the judgment of the experts.},
 year = {2019}
}

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TY - JOUR
T1 - Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia
AU - Asirat Teshome Tolosa
Y1 - 2019/01/02
PY - 2019
N1 - https://doi.org/10.11648/j.ijema.20180606.12
DO - 10.11648/j.ijema.20180606.12
T2 - International Journal of Environmental Monitoring and Analysis
JF - International Journal of Environmental Monitoring and Analysis
JO - International Journal of Environmental Monitoring and Analysis
SP - 152
EP - 166
PB - Science Publishing Group
SN - 2328-7667
UR - https://doi.org/10.11648/j.ijema.20180606.12
AB - Soil erosion is one of the natural resources which can be influenced by Land use land cover change (LCC). The main influencing factor for land use land cover change is the increase of population, which in turn resulted in land degradation. This study aimed at modeling and analyzing LCC and its effect on soil erosion. The study was conducted in the highlands of, Blue Nile Basin, Ethiopia. Three Landsat images (1986, 2000 and 2016) were used to analyze the LCC. Supervised classification using maximum likelihood algorism was used to analyze the LCC. Four land cover types (LCTs) cropland, forest, and grassland and shrubland were defined. Multi-criteria decision analysis (MCE) using the Analytic Hierarchy Process (AHP) was used to prioritize the most influencing factor for soil erosion. Five major factors; land use, slope, soil types, Topographic Wetness Index (TWI) and altitude were considered to analyze the erosion hotspot area. The result showed that cropland and grassland increased from 41.6% and 15.4% in 1986 to 58.8% and 28.3% in 2016, respectively. However, shrub-land and forest decline from 32.3% and 10.6% in 1986 to 5.6% and 7.3% in 2016, respectively. The AHP analysis showed that LCT is the most contributors for erosion. It is observed that free grazing in the area is the common practice which is the main contributor to erosion. Hence, 50% of the gully erosion is influenced by LCT. The resultant erosion risk map shows that 1.12% of the area lies under the low-risk zone, whereas 19.02%, 72.67% and 7.2% of the total area fall in medium, high and very high-risk categories respectively. The results verified by field data collected and the judgment of the experts.
VL - 6
IS - 6
ER -

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Author Information

Asirat Teshome Tolosa

Department of Hydraulic and Water Resources Engineering, Debre Tabor University, Debre Tabor, Ethiopia

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APA Style

Asirat Teshome Tolosa. (2019). Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. International Journal of Environmental Monitoring and Analysis, 6(6), 152-166. https://doi.org/10.11648/j.ijema.20180606.12

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ACS Style

Asirat Teshome Tolosa. Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. Int. J. Environ. Monit. Anal. 2019, 6(6), 152-166. doi: 10.11648/j.ijema.20180606.12

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AMA Style

Asirat Teshome Tolosa. Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia. Int J Environ Monit Anal. 2019;6(6):152-166. doi: 10.11648/j.ijema.20180606.12

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@article{10.11648/j.ijema.20180606.12,
  author = {Asirat Teshome Tolosa},
  title = {Modeling the Implication of Land Use Land Cover Change on Soil Erosion by Using Remote Sensing Data and GIS Based MCE Techniques in the Highlands of Ethiopia},
  journal = {International Journal of Environmental Monitoring and Analysis},
  volume = {6},
  number = {6},
  pages = {152-166},
  doi = {10.11648/j.ijema.20180606.12},
  url = {https://doi.org/10.11648/j.ijema.20180606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijema.20180606.12},
  abstract = {Soil erosion is one of the natural resources which can be influenced by Land use land cover change (LCC). The main influencing factor for land use land cover change is the increase of population, which in turn resulted in land degradation. This study aimed at modeling and analyzing LCC and its effect on soil erosion. The study was conducted in the highlands of, Blue Nile Basin, Ethiopia. Three Landsat images (1986, 2000 and 2016) were used to analyze the LCC. Supervised classification using maximum likelihood algorism was used to analyze the LCC. Four land cover types (LCTs) cropland, forest, and grassland and shrubland were defined. Multi-criteria decision analysis (MCE) using the Analytic Hierarchy Process (AHP) was used to prioritize the most influencing factor for soil erosion. Five major factors; land use, slope, soil types, Topographic Wetness Index (TWI) and altitude were considered to analyze the erosion hotspot area. The result showed that cropland and grassland increased from 41.6% and 15.4% in 1986 to 58.8% and 28.3% in 2016, respectively. However, shrub-land and forest decline from 32.3% and 10.6% in 1986 to 5.6% and 7.3% in 2016, respectively. The AHP analysis showed that LCT is the most contributors for erosion. It is observed that free grazing in the area is the common practice which is the main contributor to erosion. Hence, 50% of the gully erosion is influenced by LCT. The resultant erosion risk map shows that 1.12% of the area lies under the low-risk zone, whereas 19.02%, 72.67% and 7.2% of the total area fall in medium, high and very high-risk categories respectively. The results verified by field data collected and the judgment of the experts.},
 year = {2019}
}

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