Monitoring of Canopy Reflectance Change Based on Flowering Rate

Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained f...
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Gespeichert in:

Autor*in:	Karakoç, Ahmet [verfasserIn] Karabulut, Murat [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Plant reflectance Flower coverage Hyperspectral remote sensing Grasslands

Übergeordnetes Werk:	Enthalten in: Journal of the Indian Society of Remote Sensing - Neu Delhi : Springer India, 2008, 48(2020), 8 vom: Aug., Seite 1159-1168
Übergeordnetes Werk:	volume:48 ; year:2020 ; number:8 ; month:08 ; pages:1159-1168

Links:	Volltext

DOI / URN:	10.1007/s12524-020-01142-3

Katalog-ID:	SPR040957004

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245	1	0	\|a Monitoring of Canopy Reflectance Change Based on Flowering Rate
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520			\|a Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.
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matchkey_str	article:09743006:2020----::oioigfaoyelcaccagbs
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publishDate	2020
allfields	10.1007/s12524-020-01142-3 doi (DE-627)SPR040957004 (SPR)s12524-020-01142-3-e DE-627 ger DE-627 rakwb eng 550 ASE Karakoç, Ahmet verfasserin aut Monitoring of Canopy Reflectance Change Based on Flowering Rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy. Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213 Karabulut, Murat verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 48(2020), 8 vom: Aug., Seite 1159-1168 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:48 year:2020 number:8 month:08 pages:1159-1168 https://dx.doi.org/10.1007/s12524-020-01142-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR 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_65 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_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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2020 8 08 1159-1168
spelling	10.1007/s12524-020-01142-3 doi (DE-627)SPR040957004 (SPR)s12524-020-01142-3-e DE-627 ger DE-627 rakwb eng 550 ASE Karakoç, Ahmet verfasserin aut Monitoring of Canopy Reflectance Change Based on Flowering Rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy. Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213 Karabulut, Murat verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 48(2020), 8 vom: Aug., Seite 1159-1168 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:48 year:2020 number:8 month:08 pages:1159-1168 https://dx.doi.org/10.1007/s12524-020-01142-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR 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_65 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_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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2020 8 08 1159-1168
allfields_unstemmed	10.1007/s12524-020-01142-3 doi (DE-627)SPR040957004 (SPR)s12524-020-01142-3-e DE-627 ger DE-627 rakwb eng 550 ASE Karakoç, Ahmet verfasserin aut Monitoring of Canopy Reflectance Change Based on Flowering Rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy. Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213 Karabulut, Murat verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 48(2020), 8 vom: Aug., Seite 1159-1168 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:48 year:2020 number:8 month:08 pages:1159-1168 https://dx.doi.org/10.1007/s12524-020-01142-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR 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_65 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_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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2020 8 08 1159-1168
allfieldsGer	10.1007/s12524-020-01142-3 doi (DE-627)SPR040957004 (SPR)s12524-020-01142-3-e DE-627 ger DE-627 rakwb eng 550 ASE Karakoç, Ahmet verfasserin aut Monitoring of Canopy Reflectance Change Based on Flowering Rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy. Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213 Karabulut, Murat verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 48(2020), 8 vom: Aug., Seite 1159-1168 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:48 year:2020 number:8 month:08 pages:1159-1168 https://dx.doi.org/10.1007/s12524-020-01142-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR 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_65 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_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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2020 8 08 1159-1168
allfieldsSound	10.1007/s12524-020-01142-3 doi (DE-627)SPR040957004 (SPR)s12524-020-01142-3-e DE-627 ger DE-627 rakwb eng 550 ASE Karakoç, Ahmet verfasserin aut Monitoring of Canopy Reflectance Change Based on Flowering Rate 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy. Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213 Karabulut, Murat verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 48(2020), 8 vom: Aug., Seite 1159-1168 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:48 year:2020 number:8 month:08 pages:1159-1168 https://dx.doi.org/10.1007/s12524-020-01142-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR 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_65 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_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_2008 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48 2020 8 08 1159-1168
language	English
source	Enthalten in Journal of the Indian Society of Remote Sensing 48(2020), 8 vom: Aug., Seite 1159-1168 volume:48 year:2020 number:8 month:08 pages:1159-1168
sourceStr	Enthalten in Journal of the Indian Society of Remote Sensing 48(2020), 8 vom: Aug., Seite 1159-1168 volume:48 year:2020 number:8 month:08 pages:1159-1168
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Plant reflectance Flower coverage Hyperspectral remote sensing Grasslands
dewey-raw	550
isfreeaccess_bool	false
container_title	Journal of the Indian Society of Remote Sensing
authorswithroles_txt_mv	Karakoç, Ahmet @@aut@@ Karabulut, Murat @@aut@@
publishDateDaySort_date	2020-08-01T00:00:00Z
hierarchy_top_id	573088853
dewey-sort	3550
id	SPR040957004
language_de	englisch
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author	Karakoç, Ahmet
spellingShingle	Karakoç, Ahmet ddc 550 misc Plant reflectance misc Flower coverage misc Hyperspectral remote sensing misc Grasslands Monitoring of Canopy Reflectance Change Based on Flowering Rate
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topic_title	550 ASE Monitoring of Canopy Reflectance Change Based on Flowering Rate Plant reflectance (dpeaa)DE-He213 Flower coverage (dpeaa)DE-He213 Hyperspectral remote sensing (dpeaa)DE-He213 Grasslands (dpeaa)DE-He213
topic	ddc 550 misc Plant reflectance misc Flower coverage misc Hyperspectral remote sensing misc Grasslands
topic_unstemmed	ddc 550 misc Plant reflectance misc Flower coverage misc Hyperspectral remote sensing misc Grasslands
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title	Monitoring of Canopy Reflectance Change Based on Flowering Rate
ctrlnum	(DE-627)SPR040957004 (SPR)s12524-020-01142-3-e
title_full	Monitoring of Canopy Reflectance Change Based on Flowering Rate
author_sort	Karakoç, Ahmet
journal	Journal of the Indian Society of Remote Sensing
journalStr	Journal of the Indian Society of Remote Sensing
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author_browse	Karakoç, Ahmet Karabulut, Murat
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author-letter	Karakoç, Ahmet
doi_str_mv	10.1007/s12524-020-01142-3
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title_sort	monitoring of canopy reflectance change based on flowering rate
title_auth	Monitoring of Canopy Reflectance Change Based on Flowering Rate
abstract	Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.
abstractGer	Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.
abstract_unstemmed	Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. Evidence was found that the basic factor governing this effect was the fact that the flowers formed an obstacle to the detection of chlorophyll content by covering a certain part of the canopy.
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title_short	Monitoring of Canopy Reflectance Change Based on Flowering Rate
url	https://dx.doi.org/10.1007/s12524-020-01142-3
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doi_str	10.1007/s12524-020-01142-3
up_date	2024-07-03T19:20:54.649Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR040957004</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111132748.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12524-020-01142-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR040957004</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12524-020-01142-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Karakoç, Ahmet</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Monitoring of Canopy Reflectance Change Based on Flowering Rate</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hyperspectral remote sensing data have certain disadvantages as well as being a widely used tool for investigating biophysical and biochemical characteristics in grasslands due to its many advantages. Most importantly, some external influences have negative effects on the signals obtained from the canopy. Studies conducted in recent years have revealed that one of these negative externalities is the flowering on the canopy. The purpose of this study is to show how spectral reflectance readings are affected in samples with different flowering rates. The following procedure in the given order was carried out, and this procedure was repeated for a total of 46 measurements from within 10 quadrats: (1) placing quadrats of 50 × 50 cm on selected sampling areas, (2) performing spectral measurements in the quadrats, (3) measuring the chlorophyll content, (4) taking photographs of the quadrats and (5) subtilization of some of the flowers in the quadrats. Vegetation indices were also generated from collected spectral data during the data processing stage, and the flowering rate in each canopy was determined by the supervised classification method. Relations between flowering rate and spectral data were analyzed by Pearson's correlation coefficient and linear regression models. The results show that there was a linear relationship between the flowering rate and the spectral reflectance in the red and green regions, whereas there was no statistically significant relationship with the reflectance in the NIR region. Moreover, all vegetation indices, especially REP, were affected from flowering variations. This effect was found to be lower in heterogeneous samples and higher in homogeneous samples. 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Monitoring of Canopy Reflectance Change Based on Flowering Rate

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