A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country
In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral informat...
Ausführliche Beschreibung
Autor*in: |
Hatem Taifour [verfasserIn] Kyle G. Dexter [verfasserIn] Jawad Al-Bakri [verfasserIn] Anthony Miller [verfasserIn] Sophie Neale [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2022 |
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Übergeordnetes Werk: |
In: Conservation - MDPI AG, 2021, 2(2022), 1, Seite 174-194 |
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Übergeordnetes Werk: |
volume:2 ; year:2022 ; number:1 ; pages:174-194 |
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DOI / URN: |
10.3390/conservation2010012 |
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DOAJ045668086 |
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10.3390/conservation2010012 doi (DE-627)DOAJ045668086 (DE-599)DOAJ594fd7f030de4cf3ac7768836bce17e1 DE-627 ger DE-627 rakwb eng QH540-549.5 Hatem Taifour verfasserin aut A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. remote sensing Jordan land cover land use vegetation map vegetation type Ecology Kyle G. Dexter verfasserin aut Jawad Al-Bakri verfasserin aut Anthony Miller verfasserin aut Sophie Neale verfasserin aut In Conservation MDPI AG, 2021 2(2022), 1, Seite 174-194 (DE-627)176815211X 26737159 nnns volume:2 year:2022 number:1 pages:174-194 https://doi.org/10.3390/conservation2010012 kostenfrei https://doaj.org/article/594fd7f030de4cf3ac7768836bce17e1 kostenfrei https://www.mdpi.com/2673-7159/2/1/12 kostenfrei https://doaj.org/toc/2673-7159 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 2 2022 1 174-194 |
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10.3390/conservation2010012 doi (DE-627)DOAJ045668086 (DE-599)DOAJ594fd7f030de4cf3ac7768836bce17e1 DE-627 ger DE-627 rakwb eng QH540-549.5 Hatem Taifour verfasserin aut A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. remote sensing Jordan land cover land use vegetation map vegetation type Ecology Kyle G. Dexter verfasserin aut Jawad Al-Bakri verfasserin aut Anthony Miller verfasserin aut Sophie Neale verfasserin aut In Conservation MDPI AG, 2021 2(2022), 1, Seite 174-194 (DE-627)176815211X 26737159 nnns volume:2 year:2022 number:1 pages:174-194 https://doi.org/10.3390/conservation2010012 kostenfrei https://doaj.org/article/594fd7f030de4cf3ac7768836bce17e1 kostenfrei https://www.mdpi.com/2673-7159/2/1/12 kostenfrei https://doaj.org/toc/2673-7159 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 2 2022 1 174-194 |
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10.3390/conservation2010012 doi (DE-627)DOAJ045668086 (DE-599)DOAJ594fd7f030de4cf3ac7768836bce17e1 DE-627 ger DE-627 rakwb eng QH540-549.5 Hatem Taifour verfasserin aut A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. remote sensing Jordan land cover land use vegetation map vegetation type Ecology Kyle G. Dexter verfasserin aut Jawad Al-Bakri verfasserin aut Anthony Miller verfasserin aut Sophie Neale verfasserin aut In Conservation MDPI AG, 2021 2(2022), 1, Seite 174-194 (DE-627)176815211X 26737159 nnns volume:2 year:2022 number:1 pages:174-194 https://doi.org/10.3390/conservation2010012 kostenfrei https://doaj.org/article/594fd7f030de4cf3ac7768836bce17e1 kostenfrei https://www.mdpi.com/2673-7159/2/1/12 kostenfrei https://doaj.org/toc/2673-7159 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 2 2022 1 174-194 |
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A State-of-the-Art Vegetation Map for Jordan: A New Tool for Conservation in a Biodiverse Country |
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In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. |
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In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. |
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In many countries, including Jordan, the updating of vegetation maps is required to aid in formulating development and management plans for agriculture, forest, and rangeland sectors. Remote sensing data contributes widely to vegetation mapping at different scales by providing multispectral information that can separate and identify different vegetation groups at reasonable accuracy and low cost. Here, we implemented state-of-the-art approaches to develop a vegetation map for Jordan, as an example of how such maps can be produced in regions of high vegetation complexity. Specifically, we used a reciprocal illumination technique that combines extensive ground data (640 vegetation inventory plots) and Sentinel-2 satellite images to produce a categorical vegetation map (scale 1:50,000). Supervised classification was used to translate the spectral characteristics into vegetation types, which were first delimited by the clustering analyses of species composition data from the plots. From the satellite image interpretation, two maps were created: an unsupervised land cover/land use map and a supervised map of present-day vegetation types, both consisting of 18 categories. These new maps should inform ecosystem management and conservation planning decisions in Jordan over the coming years. |
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|
score |
7.400218 |