Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data
Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in contex...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Srivastava, Prashant K. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Springer Nature B.V. 2020 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Environment, development and sustainability - Springer Netherlands, 1999, 23(2020), 4 vom: 07. Juli, Seite 5504-5519 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2020 ; number:4 ; day:07 ; month:07 ; pages:5504-5519 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10668-020-00827-6 |
|---|
| Katalog-ID: |
OLC2124856685 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2124856685 | ||
| 003 | DE-627 | ||
| 005 | 20230505093331.0 | ||
| 007 | tu | ||
| 008 | 230505s2020 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10668-020-00827-6 |2 doi | |
| 035 | |a (DE-627)OLC2124856685 | ||
| 035 | |a (DE-He213)s10668-020-00827-6-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 333.7 |q VZ |
| 084 | |a 74.60$jRaumordnung$jStädtebau: Allgemeines |2 bkl | ||
| 084 | |a 74.60$jRaumordnung$jStädtebau: Allgemeines |2 bkl | ||
| 084 | |a 83.46$jEntwicklungsökonomie |2 bkl | ||
| 100 | 1 | |a Srivastava, Prashant K. |e verfasserin |0 (orcid)0000-0002-4155-630X |4 aut | |
| 245 | 1 | 0 | |a Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Nature B.V. 2020 | ||
| 520 | |a Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. | ||
| 650 | 4 | |a Hyperspectral Radiometry | |
| 650 | 4 | |a Vegetation indices | |
| 650 | 4 | |a Sensitivity analysis | |
| 650 | 4 | |a Neural network | |
| 650 | 4 | |a Chlorophyll | |
| 700 | 1 | |a Gupta, Manika |4 aut | |
| 700 | 1 | |a Singh, Ujjwal |4 aut | |
| 700 | 1 | |a Prasad, Rajendra |4 aut | |
| 700 | 1 | |a Pandey, Prem Chandra |4 aut | |
| 700 | 1 | |a Raghubanshi, A. S. |4 aut | |
| 700 | 1 | |a Petropoulos, George P. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Environment, development and sustainability |d Springer Netherlands, 1999 |g 23(2020), 4 vom: 07. Juli, Seite 5504-5519 |w (DE-627)247370592 |w (DE-600)1438730-X |w (DE-576)27365103X |x 1387-585X |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2020 |g number:4 |g day:07 |g month:07 |g pages:5504-5519 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10668-020-00827-6 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-GGO | ||
| 912 | |a SSG-OLC-WIW | ||
| 912 | |a SSG-OLC-FOR | ||
| 912 | |a GBV_ILN_26 | ||
| 936 | b | k | |a 74.60$jRaumordnung$jStädtebau: Allgemeines |q VZ |0 106413708 |0 (DE-625)106413708 |
| 936 | b | k | |a 74.60$jRaumordnung$jStädtebau: Allgemeines |q VZ |0 106413708 |0 (DE-625)106413708 |
| 936 | b | k | |a 83.46$jEntwicklungsökonomie |q VZ |0 106414925 |0 (DE-625)106414925 |
| 951 | |a AR | ||
| 952 | |d 23 |j 2020 |e 4 |b 07 |c 07 |h 5504-5519 | ||
| author_variant |
p k s pk pks m g mg u s us r p rp p c p pc pcp a s r as asr g p p gp gpp |
|---|---|
| matchkey_str |
article:1387585X:2020----::estvtaayioatfcanuantokoclrpylrdci |
| hierarchy_sort_str |
2020 |
| bklnumber |
74.60$jRaumordnung$jStädtebau: Allgemeines 83.46$jEntwicklungsökonomie |
| publishDate |
2020 |
| allfields |
10.1007/s10668-020-00827-6 doi (DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Srivastava, Prashant K. verfasserin (orcid)0000-0002-4155-630X aut Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature B.V. 2020 Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll Gupta, Manika aut Singh, Ujjwal aut Prasad, Rajendra aut Pandey, Prem Chandra aut Raghubanshi, A. S. aut Petropoulos, George P. aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 23(2020), 4 vom: 07. Juli, Seite 5504-5519 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 https://doi.org/10.1007/s10668-020-00827-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 23 2020 4 07 07 5504-5519 |
| spelling |
10.1007/s10668-020-00827-6 doi (DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Srivastava, Prashant K. verfasserin (orcid)0000-0002-4155-630X aut Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature B.V. 2020 Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll Gupta, Manika aut Singh, Ujjwal aut Prasad, Rajendra aut Pandey, Prem Chandra aut Raghubanshi, A. S. aut Petropoulos, George P. aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 23(2020), 4 vom: 07. Juli, Seite 5504-5519 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 https://doi.org/10.1007/s10668-020-00827-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 23 2020 4 07 07 5504-5519 |
| allfields_unstemmed |
10.1007/s10668-020-00827-6 doi (DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Srivastava, Prashant K. verfasserin (orcid)0000-0002-4155-630X aut Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature B.V. 2020 Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll Gupta, Manika aut Singh, Ujjwal aut Prasad, Rajendra aut Pandey, Prem Chandra aut Raghubanshi, A. S. aut Petropoulos, George P. aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 23(2020), 4 vom: 07. Juli, Seite 5504-5519 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 https://doi.org/10.1007/s10668-020-00827-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 23 2020 4 07 07 5504-5519 |
| allfieldsGer |
10.1007/s10668-020-00827-6 doi (DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Srivastava, Prashant K. verfasserin (orcid)0000-0002-4155-630X aut Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature B.V. 2020 Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll Gupta, Manika aut Singh, Ujjwal aut Prasad, Rajendra aut Pandey, Prem Chandra aut Raghubanshi, A. S. aut Petropoulos, George P. aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 23(2020), 4 vom: 07. Juli, Seite 5504-5519 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 https://doi.org/10.1007/s10668-020-00827-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 23 2020 4 07 07 5504-5519 |
| allfieldsSound |
10.1007/s10668-020-00827-6 doi (DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Srivastava, Prashant K. verfasserin (orcid)0000-0002-4155-630X aut Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data 2020 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Nature B.V. 2020 Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll Gupta, Manika aut Singh, Ujjwal aut Prasad, Rajendra aut Pandey, Prem Chandra aut Raghubanshi, A. S. aut Petropoulos, George P. aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 23(2020), 4 vom: 07. Juli, Seite 5504-5519 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 https://doi.org/10.1007/s10668-020-00827-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 23 2020 4 07 07 5504-5519 |
| language |
English |
| source |
Enthalten in Environment, development and sustainability 23(2020), 4 vom: 07. Juli, Seite 5504-5519 volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 |
| sourceStr |
Enthalten in Environment, development and sustainability 23(2020), 4 vom: 07. Juli, Seite 5504-5519 volume:23 year:2020 number:4 day:07 month:07 pages:5504-5519 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll |
| dewey-raw |
333.7 |
| isfreeaccess_bool |
false |
| container_title |
Environment, development and sustainability |
| authorswithroles_txt_mv |
Srivastava, Prashant K. @@aut@@ Gupta, Manika @@aut@@ Singh, Ujjwal @@aut@@ Prasad, Rajendra @@aut@@ Pandey, Prem Chandra @@aut@@ Raghubanshi, A. S. @@aut@@ Petropoulos, George P. @@aut@@ |
| publishDateDaySort_date |
2020-07-07T00:00:00Z |
| hierarchy_top_id |
247370592 |
| dewey-sort |
3333.7 |
| id |
OLC2124856685 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124856685</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505093331.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10668-020-00827-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124856685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10668-020-00827-6-p</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">333.7</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.46$jEntwicklungsökonomie</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Srivastava, Prashant K.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4155-630X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Nature B.V. 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral Radiometry</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation indices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitivity analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chlorophyll</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gupta, Manika</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Ujjwal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prasad, Rajendra</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pandey, Prem Chandra</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Raghubanshi, A. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Petropoulos, George P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environment, development and sustainability</subfield><subfield code="d">Springer Netherlands, 1999</subfield><subfield code="g">23(2020), 4 vom: 07. Juli, Seite 5504-5519</subfield><subfield code="w">(DE-627)247370592</subfield><subfield code="w">(DE-600)1438730-X</subfield><subfield code="w">(DE-576)27365103X</subfield><subfield code="x">1387-585X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:4</subfield><subfield code="g">day:07</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:5504-5519</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10668-020-00827-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">106413708</subfield><subfield code="0">(DE-625)106413708</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">106413708</subfield><subfield code="0">(DE-625)106413708</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.46$jEntwicklungsökonomie</subfield><subfield code="q">VZ</subfield><subfield code="0">106414925</subfield><subfield code="0">(DE-625)106414925</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2020</subfield><subfield code="e">4</subfield><subfield code="b">07</subfield><subfield code="c">07</subfield><subfield code="h">5504-5519</subfield></datafield></record></collection>
|
| author |
Srivastava, Prashant K. |
| spellingShingle |
Srivastava, Prashant K. ddc 333.7 bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie misc Hyperspectral Radiometry misc Vegetation indices misc Sensitivity analysis misc Neural network misc Chlorophyll Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| authorStr |
Srivastava, Prashant K. |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)247370592 |
| format |
Article |
| dewey-ones |
333 - Economics of land & energy |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1387-585X |
| topic_title |
333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data Hyperspectral Radiometry Vegetation indices Sensitivity analysis Neural network Chlorophyll |
| topic |
ddc 333.7 bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie misc Hyperspectral Radiometry misc Vegetation indices misc Sensitivity analysis misc Neural network misc Chlorophyll |
| topic_unstemmed |
ddc 333.7 bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie misc Hyperspectral Radiometry misc Vegetation indices misc Sensitivity analysis misc Neural network misc Chlorophyll |
| topic_browse |
ddc 333.7 bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie misc Hyperspectral Radiometry misc Vegetation indices misc Sensitivity analysis misc Neural network misc Chlorophyll |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Environment, development and sustainability |
| hierarchy_parent_id |
247370592 |
| dewey-tens |
330 - Economics |
| hierarchy_top_title |
Environment, development and sustainability |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X |
| title |
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| ctrlnum |
(DE-627)OLC2124856685 (DE-He213)s10668-020-00827-6-p |
| title_full |
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| author_sort |
Srivastava, Prashant K. |
| journal |
Environment, development and sustainability |
| journalStr |
Environment, development and sustainability |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
300 - Social sciences |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
txt |
| container_start_page |
5504 |
| author_browse |
Srivastava, Prashant K. Gupta, Manika Singh, Ujjwal Prasad, Rajendra Pandey, Prem Chandra Raghubanshi, A. S. Petropoulos, George P. |
| container_volume |
23 |
| class |
333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl |
| format_se |
Aufsätze |
| author-letter |
Srivastava, Prashant K. |
| doi_str_mv |
10.1007/s10668-020-00827-6 |
| normlink |
(ORCID)0000-0002-4155-630X 106413708 106414925 |
| normlink_prefix_str_mv |
(orcid)0000-0002-4155-630X 106413708 (DE-625)106413708 106414925 (DE-625)106414925 |
| dewey-full |
333.7 |
| title_sort |
sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| title_auth |
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| abstract |
Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. © Springer Nature B.V. 2020 |
| abstractGer |
Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. © Springer Nature B.V. 2020 |
| abstract_unstemmed |
Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status. © Springer Nature B.V. 2020 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR GBV_ILN_26 |
| container_issue |
4 |
| title_short |
Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data |
| url |
https://doi.org/10.1007/s10668-020-00827-6 |
| remote_bool |
false |
| author2 |
Gupta, Manika Singh, Ujjwal Prasad, Rajendra Pandey, Prem Chandra Raghubanshi, A. S. Petropoulos, George P. |
| author2Str |
Gupta, Manika Singh, Ujjwal Prasad, Rajendra Pandey, Prem Chandra Raghubanshi, A. S. Petropoulos, George P. |
| ppnlink |
247370592 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10668-020-00827-6 |
| up_date |
2024-07-04T01:37:21.864Z |
| _version_ |
1803610532729585664 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2124856685</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230505093331.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230505s2020 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10668-020-00827-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2124856685</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10668-020-00827-6-p</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">333.7</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.46$jEntwicklungsökonomie</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Srivastava, Prashant K.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4155-630X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sensitivity analysis of artificial neural network for chlorophyll prediction using hyperspectral data</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Nature B.V. 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hyperspectral Radiometry</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vegetation indices</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitivity analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chlorophyll</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gupta, Manika</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Ujjwal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Prasad, Rajendra</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pandey, Prem Chandra</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Raghubanshi, A. S.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Petropoulos, George P.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Environment, development and sustainability</subfield><subfield code="d">Springer Netherlands, 1999</subfield><subfield code="g">23(2020), 4 vom: 07. Juli, Seite 5504-5519</subfield><subfield code="w">(DE-627)247370592</subfield><subfield code="w">(DE-600)1438730-X</subfield><subfield code="w">(DE-576)27365103X</subfield><subfield code="x">1387-585X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:4</subfield><subfield code="g">day:07</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:5504-5519</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10668-020-00827-6</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-FOR</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">106413708</subfield><subfield code="0">(DE-625)106413708</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">74.60$jRaumordnung$jStädtebau: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">106413708</subfield><subfield code="0">(DE-625)106413708</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.46$jEntwicklungsökonomie</subfield><subfield code="q">VZ</subfield><subfield code="0">106414925</subfield><subfield code="0">(DE-625)106414925</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2020</subfield><subfield code="e">4</subfield><subfield code="b">07</subfield><subfield code="c">07</subfield><subfield code="h">5504-5519</subfield></datafield></record></collection>
|
| score |
7.399374 |