Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset
While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forag...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523002046 |
| _version_ | 1797390381846888448 |
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| author | Lwando Royimani Onisimo Mutanga John Odindi |
| author_facet | Lwando Royimani Onisimo Mutanga John Odindi |
| author_sort | Lwando Royimani |
| collection | DOAJ |
| description | While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods. |
| first_indexed | 2024-03-08T23:09:55Z |
| format | Article |
| id | doaj.art-e19f9d8d7f054295886c5db75f1b805b |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-03-08T23:09:55Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-e19f9d8d7f054295886c5db75f1b805b2023-12-15T07:27:18ZengElsevierSmart Agricultural Technology2772-37552023-12-016100377Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed datasetLwando Royimani0Onisimo Mutanga1John Odindi2Corresponding author.; Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaDiscipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaDiscipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaWhile remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.http://www.sciencedirect.com/science/article/pii/S2772375523002046Broadband multispectralOptimal waveband positionsRandom forestRemote sensingSenescent grass |
| spellingShingle | Lwando Royimani Onisimo Mutanga John Odindi Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset Smart Agricultural Technology Broadband multispectral Optimal waveband positions Random forest Remote sensing Senescent grass |
| title | Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| title_full | Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| title_fullStr | Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| title_full_unstemmed | Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| title_short | Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| title_sort | identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset |
| topic | Broadband multispectral Optimal waveband positions Random forest Remote sensing Senescent grass |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523002046 |
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