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Effect of Land Use and Land Cover Changes on Soil Erosion: A Case Study of the Debre-Markos Blue Nile Basin in Ethiopia
토지이용 및 토지피복 변화에 따른 토양 유실량 평가: 에디오피아 데브레 마르코스 블루 나일 유역을 중심으로

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Authors
우베세트
Advisor
Sangjun Im
Major
농업생명과학대학 산림과학부
Issue Date
2018-02
Publisher
서울대학교 대학원
Keywords
Debre-Markos Blue Nile BasinGoogle EarthLandsatLand use and land coverRUSLESoil Erosion.
Description
학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 산림과학부, 2018. 2. Sangjun Im.
Abstract
Abstract
Soil erosion has become one of the most important environmental problems in globally, particularly in Ethiopia. The past land use and land cover (LULC) changes suspected to be the main causes of soil erosion in the study area, where is the Debre-Markos Blue Nile (DMBN) basin. Therefore, the objectives of this thesis are to: (1) assess the LULC change by using ERDAS 9.2 from Landsat images (1987, 2002, and 2017) and (2) to identify the LULC changes that causes soil erosion and estimate the annual soil erosion hazard by using ArcGIS 10.1. To determine the LULC changes first, the study carried out a mapping of each LULC for 1987, 2002 and 2017 by using the Supervised Classification method of Landsat image. To improve classification accuracy and reducing misclassification, a training data was derived from Google Earths (GE) geo-browser it has high spatial resolution images which provide opportunity for detailed LULC. The Landsat images classification for 2002 and 2017 were based on GE, while 1987 Landsat image were referenced by 2002s GE and pre-classified images, since GE images acquired from 2002 in this study area. After classification, accuracy assessment for 2017 classified image were interpreted using both digitized reference points and field varification way points. In the second place, the study estimates annual soil erosion by water using Revised Universal Soil Loss Equation (RUSLE). The LULCs map utilized for the final analysis of annual soil loss. In addition, 20 years` mean annual rainfall data from Ethiopia Metrological Agency, soil map from FAO Digital Soil Map of the World, digital elevation model (DEM) and previous reports to identify the cover management and supportive practice were used for the erosion estimation. From the achieved map the overall accuracy of 2017 was 84.5% and a Kappa coefficient of 0.81 was recorded. The LULCs change comparisen between 1987 and 2017 indicate that, from the total area about ~29% experienced with changes. The classification result showed that from 1987 to 2017 the dominant agriculture land (~44%) increased by 2% while the second and third dominant grassland (26%) and woodland (25%) had significantly decreased by 1.87% and 3.57 % of its coverage respectively. Other LULC types with small coverage such as afro-alpine, forest, agriculture, natural forest, plantation, settlements and water body experienced increased rate. Moreover, the LULC changes in DMBN-basin also affected the total soil loss. Thus, the soil erosion yield increased 3.04% (9996 tons/ yr-1) in comparison between 1987 and 2017. As a result, the rill and inter rill soil erosion had greatest (over 95%) relation with the dominant agriculture and grasslands during 1987 - 2017. The soil erosion with respect to the agricultural lands showed an increment of 6.13% from the previous agricultural land. In other words, the annual soil loss due to grasslands became decreased by 3.93%. The study also identified the soil erosion severity level, which very slight and slight soil erosion categories (< 5tons ha-1 yr-1) in DMBN basin is about 76%. On the other hand, about ~23% of soil erosion rate was recorded above the tolerable limit, that categorized medium (~9%), high (~10%), severe (~2%) and very severe level (~1%). In addition, the annual soil loss rate of severe and very severe levels on the steeper slopes have decreased by a little change in vegetation coverage in 2017. However, the total soil erosion had increased due to medium slope areas, where the agricultural activities increased implementation. Overall, the LULC changes analysis and annual soil erosion estimation and mapping its distribution is important and effective for identifying natural resource prone areas. Therefore, the local experts and administrative bodies uses this information to prepare plan for those priority areas to conserve and monitor the degraded resources.
Language
English
URI
https://hdl.handle.net/10371/141790
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College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Forest Sciences (산림과학부)Theses (Master's Degree_산림과학부)
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