Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S.
Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ji, Wenjie [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution - Abdullah, N. ELSEVIER, 2016, an interdisciplinary journal, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:173 ; year:2016 ; pages:29-38 ; extent:10 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.rse.2015.11.017 |
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ELV024977063 |
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| 245 | 1 | 0 | |a Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. |
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| 520 | |a Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. | ||
| 520 | |a Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. | ||
| 700 | 1 | |a Wang, Le |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Abdullah, N. ELSEVIER |t Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution |d 2016 |d an interdisciplinary journal |g Amsterdam [u.a.] |w (DE-627)ELV013680773 |
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10.1016/j.rse.2015.11.017 doi GBVA2016024000003.pica (DE-627)ELV024977063 (ELSEVIER)S0034-4257(15)30205-4 DE-627 ger DE-627 rakwb eng 050 550 050 DE-600 550 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Ji, Wenjie verfasserin aut Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Wang, Le oth Enthalten in Elsevier Science Abdullah, N. ELSEVIER Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution 2016 an interdisciplinary journal Amsterdam [u.a.] (DE-627)ELV013680773 volume:173 year:2016 pages:29-38 extent:10 https://doi.org/10.1016/j.rse.2015.11.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 173 2016 29-38 10 045F 050 |
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10.1016/j.rse.2015.11.017 doi GBVA2016024000003.pica (DE-627)ELV024977063 (ELSEVIER)S0034-4257(15)30205-4 DE-627 ger DE-627 rakwb eng 050 550 050 DE-600 550 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Ji, Wenjie verfasserin aut Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Wang, Le oth Enthalten in Elsevier Science Abdullah, N. ELSEVIER Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution 2016 an interdisciplinary journal Amsterdam [u.a.] (DE-627)ELV013680773 volume:173 year:2016 pages:29-38 extent:10 https://doi.org/10.1016/j.rse.2015.11.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 173 2016 29-38 10 045F 050 |
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10.1016/j.rse.2015.11.017 doi GBVA2016024000003.pica (DE-627)ELV024977063 (ELSEVIER)S0034-4257(15)30205-4 DE-627 ger DE-627 rakwb eng 050 550 050 DE-600 550 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Ji, Wenjie verfasserin aut Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Wang, Le oth Enthalten in Elsevier Science Abdullah, N. ELSEVIER Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution 2016 an interdisciplinary journal Amsterdam [u.a.] (DE-627)ELV013680773 volume:173 year:2016 pages:29-38 extent:10 https://doi.org/10.1016/j.rse.2015.11.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 173 2016 29-38 10 045F 050 |
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10.1016/j.rse.2015.11.017 doi GBVA2016024000003.pica (DE-627)ELV024977063 (ELSEVIER)S0034-4257(15)30205-4 DE-627 ger DE-627 rakwb eng 050 550 050 DE-600 550 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Ji, Wenjie verfasserin aut Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Wang, Le oth Enthalten in Elsevier Science Abdullah, N. ELSEVIER Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution 2016 an interdisciplinary journal Amsterdam [u.a.] (DE-627)ELV013680773 volume:173 year:2016 pages:29-38 extent:10 https://doi.org/10.1016/j.rse.2015.11.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 173 2016 29-38 10 045F 050 |
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10.1016/j.rse.2015.11.017 doi GBVA2016024000003.pica (DE-627)ELV024977063 (ELSEVIER)S0034-4257(15)30205-4 DE-627 ger DE-627 rakwb eng 050 550 050 DE-600 550 DE-600 660 VZ 660 VZ 530 600 670 VZ 51.00 bkl Ji, Wenjie verfasserin aut Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. 2016transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. Wang, Le oth Enthalten in Elsevier Science Abdullah, N. ELSEVIER Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution 2016 an interdisciplinary journal Amsterdam [u.a.] (DE-627)ELV013680773 volume:173 year:2016 pages:29-38 extent:10 https://doi.org/10.1016/j.rse.2015.11.017 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_40 51.00 Werkstoffkunde: Allgemeines VZ AR 173 2016 29-38 10 045F 050 |
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Enthalten in Polysulfone/hydrous ferric oxide ultrafiltration mixed matrix membrane: Preparation, characterization and its adsorptive removal of lead (II) from aqueous solution Amsterdam [u.a.] volume:173 year:2016 pages:29-38 extent:10 |
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phenology-guided saltcedar (tamarix spp.) mapping using landsat tm images in western u.s. |
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Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. |
| abstract |
Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. |
| abstractGer |
Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. |
| abstract_unstemmed |
Accurate distribution maps are essential to understand further and control the fast expansion of the introduced saltcedar (Tamarix spp.) in the western U.S. Remote sensing imagery, due to its broad spatial and temporal coverage, is well-suited for this task. More specifically, images acquired during late fall and early winter are used for saltcedar identification as the plant's foliage turns a unique orange-yellow color (coloration) during this time of a year, which makes them easily distinguishable from other plant species. However, saltcedar coloration usually lasts for only several weeks, and the timing of this phenological event varies across space. Therefore, without prior knowledge of saltcedar phenology, it is often challenging to select the remote sensing image collected at the optimal time of the year to capture the distinctive spectral signature of the coloring saltcedar. Moreover, the timing of saltcedar coloration also varies within a single remote sensing scene making it inappropriate to use an entire scene as the mapping unit. In our study, we found that the timing of saltcedar peak coloration and leaf drop were linearly correlated with each other and we were able to model this relationship using the MODIS End of Season–Time (EoST) product and Landsat TM images. Guided by a per-pixel model estimation of saltcedar coloration date, we constructed composite Landsat images at two study sites along the Rio Grande River such that each pixel in the composite images was acquired during saltcedar peak coloration. Classification results showed that the phenology-informed composite image was better for saltcedar identification than the single scene image as it was able to capture the within-scene variations in saltcedar phenology. In addition, given our lack of training data for the model, the successful classification results obtained at the Middle Rio Grande River site also demonstrated the potential of our proposed method to be applied for large-scale saltcedar mapping. |
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Phenology-guided saltcedar (Tamarix spp.) mapping using Landsat TM images in western U.S. |
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