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@ARTICLE{Mohamadi:1026327,
author = {Mohamadi, Panah and Ahmadi, Abbas and Feizizadeh, Bakhtiar
and Jafarzadeh, Ali Asghar and Rahmati, Mehdi},
title = {{U}tilizing the conventional, object-oriented and
pixel-based techniques to estimate erosion and sediment
yield by {MPSIAC} model},
journal = {Journal of Soil Science Society of Iran},
volume = {1},
number = {1},
reportid = {FZJ-2024-03378},
pages = {113-124},
year = {2022},
abstract = {Soil erosion and sediment yield in the downstream areas,
water transfer canals, and dams are the most serious
problems in the world today. Soil erosion threatens soil
resources, causes severe damage to infrastructures, and
imposes high costs on agriculture, watershed management, and
natural resources. Reducing these hazards and damages due to
soil erosion and sediment yield requires the use of
quantitative data to identify critical areas that require
immediate protection. Due to the high cost and time
consuming of conventional methods, the use of new remote
sensing technologies and satellite imagery is essential.
This study used the MPSIAC model, one of the most well-known
models for estimating soil erosion and sediment yield in
Iran, geographical information system (GIS), and satellite
image processing with object-oriented and pixel-based
methods. For this purpose, basic data were prepared using
base maps, Sentinel-2 satellite imagery, meteorological and
hydrometric data, and fieldwork. After establishing a
database, the score for each of the nine factors of the
MPSIAC model was determined using the three common,
object-oriented, and pixel-based processing methods. The
extent of soil erosion and sediment yield of the watershed
was determined within each hydrologic unit. Based on the
results, the soil erosion and sediment yield intensities of
the Lighvan watershed were classified as medium class (III).
However, the comparison of the specific soil erosion and
sediment yield values obtained from the three methods showed
that the use of object-oriented methods in determining the
values for land cover, land use, and current soil erosion
state increased the accuracy of the predictions (with the
estimated error of $12.18\%$ and $13.15\%$ for sediment
yield and erosion, respectively) compared to common (with
the estimated error of $15.73\%$ and $16.71\%)$ and
pixel-based (with the estimated error of $18.78\%$ and
$19.45\%)$ methods.},
cin = {IBG-3},
cid = {I:(DE-Juel1)IBG-3-20101118},
pnm = {2173 - Agro-biogeosystems: controls, feedbacks and impact
(POF4-217)},
pid = {G:(DE-HGF)POF4-2173},
typ = {PUB:(DE-HGF)16},
doi = {10.47176/jsssi.01.01.1020},
url = {https://juser.fz-juelich.de/record/1026327},
}