Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey

Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Eroğlu, Habip [verfasserIn] Çakır, Günay [verfasserIn] Sivrikaya, Fatih [verfasserIn] Akay, Abdullah E. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Bare soil erosion Forest road Risk Remote sensing GIS IKONOS

Übergeordnetes Werk:	Enthalten in: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 24(2009), 5 vom: 04. Nov., Seite 699-704
Übergeordnetes Werk:	volume:24 ; year:2009 ; number:5 ; day:04 ; month:11 ; pages:699-704

Links:	Volltext

DOI / URN:	10.1007/s00477-009-0356-5

Katalog-ID:	SPR006400132

Internformat


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245	1	0	\|a Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
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520			\|a Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.
650		4	\|a Bare soil erosion \|7 (dpeaa)DE-He213
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700	1		\|a Çakır, Günay \|e verfasserin \|4 aut
700	1		\|a Sivrikaya, Fatih \|e verfasserin \|4 aut
700	1		\|a Akay, Abdullah E. \|e verfasserin \|4 aut
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
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912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
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912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
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912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2070
912			\|a GBV_ILN_2086
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2116
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
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912			\|a GBV_ILN_4035
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912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4242
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912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4277
912			\|a GBV_ILN_4305
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912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4324
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912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
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912			\|a GBV_ILN_4393
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Indexfelder

author_variant	h e he g ç gç f s fs a e a ae aea
matchkey_str	article:14363259:2009----::snhgrsltoiaeadlvtodticasfigrsorssfaeolraiteaia
hierarchy_sort_str	2009
bklnumber	43.03 58.50
publishDate	2009
allfields	10.1007/s00477-009-0356-5 doi (DE-627)SPR006400132 (SPR)s00477-009-0356-5-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Eroğlu, Habip verfasserin aut Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology. Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213 Çakır, Günay verfasserin aut Sivrikaya, Fatih verfasserin aut Akay, Abdullah E. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 24(2009), 5 vom: 04. Nov., Seite 699-704 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:24 year:2009 number:5 day:04 month:11 pages:699-704 https://dx.doi.org/10.1007/s00477-009-0356-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 24 2009 5 04 11 699-704
spelling	10.1007/s00477-009-0356-5 doi (DE-627)SPR006400132 (SPR)s00477-009-0356-5-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Eroğlu, Habip verfasserin aut Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology. Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213 Çakır, Günay verfasserin aut Sivrikaya, Fatih verfasserin aut Akay, Abdullah E. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 24(2009), 5 vom: 04. Nov., Seite 699-704 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:24 year:2009 number:5 day:04 month:11 pages:699-704 https://dx.doi.org/10.1007/s00477-009-0356-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 24 2009 5 04 11 699-704
allfields_unstemmed	10.1007/s00477-009-0356-5 doi (DE-627)SPR006400132 (SPR)s00477-009-0356-5-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Eroğlu, Habip verfasserin aut Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology. Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213 Çakır, Günay verfasserin aut Sivrikaya, Fatih verfasserin aut Akay, Abdullah E. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 24(2009), 5 vom: 04. Nov., Seite 699-704 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:24 year:2009 number:5 day:04 month:11 pages:699-704 https://dx.doi.org/10.1007/s00477-009-0356-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 24 2009 5 04 11 699-704
allfieldsGer	10.1007/s00477-009-0356-5 doi (DE-627)SPR006400132 (SPR)s00477-009-0356-5-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Eroğlu, Habip verfasserin aut Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology. Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213 Çakır, Günay verfasserin aut Sivrikaya, Fatih verfasserin aut Akay, Abdullah E. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 24(2009), 5 vom: 04. Nov., Seite 699-704 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:24 year:2009 number:5 day:04 month:11 pages:699-704 https://dx.doi.org/10.1007/s00477-009-0356-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 24 2009 5 04 11 699-704
allfieldsSound	10.1007/s00477-009-0356-5 doi (DE-627)SPR006400132 (SPR)s00477-009-0356-5-e DE-627 ger DE-627 rakwb eng 550 ASE 43.03 bkl 58.50 bkl Eroğlu, Habip verfasserin aut Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology. Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213 Çakır, Günay verfasserin aut Sivrikaya, Fatih verfasserin aut Akay, Abdullah E. verfasserin aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 24(2009), 5 vom: 04. Nov., Seite 699-704 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:24 year:2009 number:5 day:04 month:11 pages:699-704 https://dx.doi.org/10.1007/s00477-009-0356-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.03 ASE 58.50 ASE AR 24 2009 5 04 11 699-704
language	English
source	Enthalten in Stochastic environmental research and risk assessment 24(2009), 5 vom: 04. Nov., Seite 699-704 volume:24 year:2009 number:5 day:04 month:11 pages:699-704
sourceStr	Enthalten in Stochastic environmental research and risk assessment 24(2009), 5 vom: 04. Nov., Seite 699-704 volume:24 year:2009 number:5 day:04 month:11 pages:699-704
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Bare soil erosion Forest road Risk Remote sensing GIS IKONOS
dewey-raw	550
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Eroğlu, Habip @@aut@@ Çakır, Günay @@aut@@ Sivrikaya, Fatih @@aut@@ Akay, Abdullah E. @@aut@@
publishDateDaySort_date	2009-11-04T00:00:00Z
hierarchy_top_id	27160235X
dewey-sort	3550
id	SPR006400132
language_de	englisch
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author	Eroğlu, Habip
spellingShingle	Eroğlu, Habip ddc 550 bkl 43.03 bkl 58.50 misc Bare soil erosion misc Forest road misc Risk misc Remote sensing misc GIS misc IKONOS Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
authorStr	Eroğlu, Habip
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topic_title	550 ASE 43.03 bkl 58.50 bkl Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey Bare soil erosion (dpeaa)DE-He213 Forest road (dpeaa)DE-He213 Risk (dpeaa)DE-He213 Remote sensing (dpeaa)DE-He213 GIS (dpeaa)DE-He213 IKONOS (dpeaa)DE-He213
topic	ddc 550 bkl 43.03 bkl 58.50 misc Bare soil erosion misc Forest road misc Risk misc Remote sensing misc GIS misc IKONOS
topic_unstemmed	ddc 550 bkl 43.03 bkl 58.50 misc Bare soil erosion misc Forest road misc Risk misc Remote sensing misc GIS misc IKONOS
topic_browse	ddc 550 bkl 43.03 bkl 58.50 misc Bare soil erosion misc Forest road misc Risk misc Remote sensing misc GIS misc IKONOS
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title	Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
ctrlnum	(DE-627)SPR006400132 (SPR)s00477-009-0356-5-e
title_full	Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
author_sort	Eroğlu, Habip
journal	Stochastic environmental research and risk assessment
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author_browse	Eroğlu, Habip Çakır, Günay Sivrikaya, Fatih Akay, Abdullah E.
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class	550 ASE 43.03 bkl 58.50 bkl
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author-letter	Eroğlu, Habip
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title_sort	using high resolution images and elevation data in classifying erosion risks of bare soil areas in the hatila valley natural protected area, turkey
title_auth	Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
abstract	Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.
abstractGer	Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.
abstract_unstemmed	Abstract Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.
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container_issue	5
title_short	Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey
url	https://dx.doi.org/10.1007/s00477-009-0356-5
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author2	Çakır, Günay Sivrikaya, Fatih Akay, Abdullah E.
author2Str	Çakır, Günay Sivrikaya, Fatih Akay, Abdullah E.
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doi_str	10.1007/s00477-009-0356-5
up_date	2024-07-03T22:49:33.181Z
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In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ERDAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1.43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. 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Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey

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