Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data
Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetati...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Maeda, Eduardo Eiji [verfasserIn] Hurskainen, Pekka [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical and applied climatology - Wien [u.a.] : Springer, 1948, 118(2014), 3 vom: 10. Jan., Seite 497-509 |
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| Übergeordnetes Werk: |
volume:118 ; year:2014 ; number:3 ; day:10 ; month:01 ; pages:497-509 |
| Links: |
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| DOI / URN: |
10.1007/s00704-013-1082-y |
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SPR007333935 |
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| 520 | |a Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. | ||
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10.1007/s00704-013-1082-y doi (DE-627)SPR007333935 (SPR)s00704-013-1082-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Maeda, Eduardo Eiji verfasserin aut Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 Hurskainen, Pekka verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 118(2014), 3 vom: 10. Jan., Seite 497-509 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:118 year:2014 number:3 day:10 month:01 pages:497-509 https://dx.doi.org/10.1007/s00704-013-1082-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 118 2014 3 10 01 497-509 |
| spelling |
10.1007/s00704-013-1082-y doi (DE-627)SPR007333935 (SPR)s00704-013-1082-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Maeda, Eduardo Eiji verfasserin aut Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 Hurskainen, Pekka verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 118(2014), 3 vom: 10. Jan., Seite 497-509 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:118 year:2014 number:3 day:10 month:01 pages:497-509 https://dx.doi.org/10.1007/s00704-013-1082-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 118 2014 3 10 01 497-509 |
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10.1007/s00704-013-1082-y doi (DE-627)SPR007333935 (SPR)s00704-013-1082-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Maeda, Eduardo Eiji verfasserin aut Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 Hurskainen, Pekka verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 118(2014), 3 vom: 10. Jan., Seite 497-509 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:118 year:2014 number:3 day:10 month:01 pages:497-509 https://dx.doi.org/10.1007/s00704-013-1082-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 118 2014 3 10 01 497-509 |
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10.1007/s00704-013-1082-y doi (DE-627)SPR007333935 (SPR)s00704-013-1082-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Maeda, Eduardo Eiji verfasserin aut Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 Hurskainen, Pekka verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 118(2014), 3 vom: 10. Jan., Seite 497-509 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:118 year:2014 number:3 day:10 month:01 pages:497-509 https://dx.doi.org/10.1007/s00704-013-1082-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 118 2014 3 10 01 497-509 |
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10.1007/s00704-013-1082-y doi (DE-627)SPR007333935 (SPR)s00704-013-1082-y-e DE-627 ger DE-627 rakwb eng 550 ASE 38.82 bkl Maeda, Eduardo Eiji verfasserin aut Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 Hurskainen, Pekka verfasserin aut Enthalten in Theoretical and applied climatology Wien [u.a.] : Springer, 1948 118(2014), 3 vom: 10. Jan., Seite 497-509 (DE-627)25490968X (DE-600)1463177-5 1434-4483 nnns volume:118 year:2014 number:3 day:10 month:01 pages:497-509 https://dx.doi.org/10.1007/s00704-013-1082-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GEO SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.82 ASE AR 118 2014 3 10 01 497-509 |
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Enthalten in Theoretical and applied climatology 118(2014), 3 vom: 10. Jan., Seite 497-509 volume:118 year:2014 number:3 day:10 month:01 pages:497-509 |
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Maeda, Eduardo Eiji @@aut@@ Hurskainen, Pekka @@aut@@ |
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Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. 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Maeda, Eduardo Eiji |
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Maeda, Eduardo Eiji ddc 550 bkl 38.82 misc Land Cover misc Normalize Difference Vegetation Index misc Land Surface Temperature misc Hemp misc Altitude Range Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data |
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550 ASE 38.82 bkl Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data Land Cover (dpeaa)DE-He213 Normalize Difference Vegetation Index (dpeaa)DE-He213 Land Surface Temperature (dpeaa)DE-He213 Hemp (dpeaa)DE-He213 Altitude Range (dpeaa)DE-He213 |
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ddc 550 bkl 38.82 misc Land Cover misc Normalize Difference Vegetation Index misc Land Surface Temperature misc Hemp misc Altitude Range |
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ddc 550 bkl 38.82 misc Land Cover misc Normalize Difference Vegetation Index misc Land Surface Temperature misc Hemp misc Altitude Range |
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Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data |
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Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data |
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Maeda, Eduardo Eiji Hurskainen, Pekka |
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spatiotemporal characterization of land surface temperature in mount kilimanjaro using satellite data |
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Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data |
| abstract |
Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. |
| abstractGer |
Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. |
| abstract_unstemmed |
Abstract Mount Kilimanjaro is considered the highest free-standing mountain in the world and a symbol of the African continent. Steep slopes and high altitudes are on the backdrop of unique biophysical characteristics, in which changes between savannas, tropical cloud forests, and subalpine vegetation can be observed in relatively small distances. In the context of this complex and heterogeneous landscape, describing the interactions between climatic variables and ecosystem functions is crucial for understanding the drivers of biodiversity. However, the characterization of climatic variables, especially surface temperature, still remains a critical bottleneck for a comprehensive understanding of habitats in Kilimanjaro. This study applies satellite-based estimates of land surface temperature (LST), from 2001 to 2011, to delineate a thorough characterization of the spatiotemporal patterns of surface temperature in Mount Kilimanjaro. The ample spatial coverage and continuous observations provided by the satellite measurements allowed the detailed description of characteristics so far poorly understood or not yet described in the literature. We demonstrate that the spatial patterns of LST in this region are rather complex, in the sense that it is characterized by non-linear behaviors and strong interactions with land cover and topography. Daytime observations (measured at 10:30 am) were shown to be strongly influenced by land cover characteristics, which is responsible for defining not only the spatial patterns (e.g., lapse rate) but also the seasonal signature of LST. At nighttime measurements (10:30 pm), the influence of land cover virtually disappears and the spatial patterns are mostly driven by altitude. Moreover, this study provides a brief assessment of LST trends observed within the analyzed period. |
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| container_issue |
3 |
| title_short |
Spatiotemporal characterization of land surface temperature in Mount Kilimanjaro using satellite data |
| url |
https://dx.doi.org/10.1007/s00704-013-1082-y |
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Hurskainen, Pekka |
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| doi_str |
10.1007/s00704-013-1082-y |
| up_date |
2024-07-04T02:52:06.724Z |
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| score |
7.4021244 |