Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery

S. Hamzeh, A.A. Naseria, S.K. Alavi Panah, B. Mojaradic, H. Bartholomeus, M. Herold

Research output: Contribution to conferencePaperAcademic

3 Citations (Scopus)

Abstract

Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7% and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.
Original languageEnglish
DOIs
Publication statusPublished - 2012
EventSPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK -
Duration: 24 Sep 201224 Sep 2012

Conference

ConferenceSPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK
Period24/09/1224/09/12

Fingerprint

satellite imagery
salinity
Hyperion
crop production
vegetation index
fieldwork
labor
remote sensing
soil salinity
method
support vector machine
analysis

Cite this

Hamzeh, S., Naseria, A. A., Alavi Panah, S. K., Mojaradic, B., Bartholomeus, H., & Herold, M. (2012). Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery. Paper presented at SPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK, . https://doi.org/10.1117/12.981655
Hamzeh, S. ; Naseria, A.A. ; Alavi Panah, S.K. ; Mojaradic, B. ; Bartholomeus, H. ; Herold, M. / Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery. Paper presented at SPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK, .
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title = "Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery",
abstract = "Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7{\%} and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.",
author = "S. Hamzeh and A.A. Naseria and {Alavi Panah}, S.K. and B. Mojaradic and H. Bartholomeus and M. Herold",
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Hamzeh, S, Naseria, AA, Alavi Panah, SK, Mojaradic, B, Bartholomeus, H & Herold, M 2012, 'Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery' Paper presented at SPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK, 24/09/12 - 24/09/12, . https://doi.org/10.1117/12.981655

Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery. / Hamzeh, S.; Naseria, A.A.; Alavi Panah, S.K.; Mojaradic, B.; Bartholomeus, H.; Herold, M.

2012. Paper presented at SPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK, .

Research output: Contribution to conferencePaperAcademic

TY - CONF

T1 - Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery

AU - Hamzeh, S.

AU - Naseria, A.A.

AU - Alavi Panah, S.K.

AU - Mojaradic, B.

AU - Bartholomeus, H.

AU - Herold, M.

PY - 2012

Y1 - 2012

N2 - Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7% and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.

AB - Soil salinity is a huge problem negatively affecting physiological and metabolic processes in plant life, ultimately diminishing growth and yield. An area with more than 70,000 ha sugarcane farming and its by-products are the major agricultural activities in the Khuzestan province, in the southwest of Iran. Therefore, mapping and identification of soil salinity is the most important issue to improve management of large scale crop production in this area. Besides labour intensive fieldwork, remote sensing is the most suitable technique to assess soil salinity for large areas. This study was carried out to investigate the capability of Hyperion spaceborne hyperspecteral data for mapping the salinity stress in the sugarcane fields and determine the best method to classify soil salinity into 3 classes (low, moderate and high salinity). For this purpose the capability of different classification methods like support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) in conjunction with different band combinations (all bands, principle component analysis (PCA), Vegetation Indices) as an input data was performed. Results indicated that best method for classification is SVM classifier when we use all bands or PCA(1-5) as an input data for classification with an overall accuracy and kappa coefficient of 78.7% and 0.68 respectively. Therefore, salinity stress can be classified in agricultural fields using Hyperion satellite imagery with good accuracy and salinity map can be very useful for management of agricultural activity and increase the crop production.

U2 - 10.1117/12.981655

DO - 10.1117/12.981655

M3 - Paper

ER -

Hamzeh S, Naseria AA, Alavi Panah SK, Mojaradic B, Bartholomeus H, Herold M. Mapping salinity stress in sugarcane fields with hyperspectral satellite imagery. 2012. Paper presented at SPIE 2012 Conference on Remote Sensing for Agriculture, Ecosystems and Hydrology XIV, Edinburgh, UK, . https://doi.org/10.1117/12.981655