Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing
Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) dat...
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ftunivsouthcar:oai:scholarcommons.sc.edu:geog_facpub-1222 2024-05-19T07:45:23+00:00 Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing Huang, Xiao Wang, Cuizhen Lu, Junyu 2019-10-01T07:00:00Z application/pdf https://scholarcommons.sc.edu/geog_facpub/220 https://doi.org/10.5194/nhess-19-2141-2019; https://scholarcommons.sc.edu/context/geog_facpub/article/1222/viewcontent/nhess_19_2141_2019__1_.pdf English eng Scholar Commons https://scholarcommons.sc.edu/geog_facpub/220 doi: https://doi.org/10.5194/nhess-19-2141-2019 https://scholarcommons.sc.edu/context/geog_facpub/article/1222/viewcontent/nhess_19_2141_2019__1_.pdf © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Faculty Publications Hurricane Hurricane-Prone United State Atlantic Gulf Coasts remote sensing Geography text 2019 ftunivsouthcar https://doi.org/10.5194/nhess-19-2141-2019;10.5194/nhess-19-2141-2019 2024-04-30T23:59:54Z Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience. Text North Atlantic University of South Carolina Libraries: Scholar Commons |
institution |
Open Polar |
collection |
University of South Carolina Libraries: Scholar Commons |
op_collection_id |
ftunivsouthcar |
language |
English |
topic |
Hurricane Hurricane-Prone United State Atlantic Gulf Coasts remote sensing Geography |
spellingShingle |
Hurricane Hurricane-Prone United State Atlantic Gulf Coasts remote sensing Geography Huang, Xiao Wang, Cuizhen Lu, Junyu Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
topic_facet |
Hurricane Hurricane-Prone United State Atlantic Gulf Coasts remote sensing Geography |
description |
Hurricanes, as one of the most devastating natural hazards, have posed a great threat to people in coastal areas. A better understanding of the spatiotemporal dynamics of human settlement in hurricane-prone areas largely benefits sustainable development. This study uses the nighttime light (NTL) data from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) to examine human settlement development in areas with different levels of hurricane proneness from 1992 to 2013. The DMSP/OLS NTL data from six satellites were intercalibrated and desaturated with the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) optical imagery to derive the Vegetation Adjusted NTL Urban Index (VANUI), a popular index that quantifies human settlement intensity. The derived VANUI time series was examined with the Mann–Kendall test and Theil–Sen test to identify significant spatiotemporal trends. To link the VANUI product to hurricane impacts, four hurricane-prone zones were extracted to represent different levels of hurricane proneness. Aside from geographic division, a wind-speed-weighted track density function was developed and applied to historical storm tracks which originated in the North Atlantic Basin to better categorize the four levels of hurricane proneness. Spatiotemporal patterns of human settlement in the four zones were finally analyzed. The results clearly exhibit a north–south and inland–coastal discrepancy of human settlement dynamics. This study also reveals that both the zonal extent and zonal increase rate of human settlement positively correlate with hurricane proneness levels. The intensified human settlement in high hurricane-exposure zones deserves further attention for coastal resilience. |
format |
Text |
author |
Huang, Xiao Wang, Cuizhen Lu, Junyu |
author_facet |
Huang, Xiao Wang, Cuizhen Lu, Junyu |
author_sort |
Huang, Xiao |
title |
Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
title_short |
Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
title_full |
Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
title_fullStr |
Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
title_full_unstemmed |
Understanding the Spatiotemporal Development of Human Settlement in Hurricane-Prone Areas on the US Atlantic and Gulf Coasts Using Nighttime Remote Sensing |
title_sort |
understanding the spatiotemporal development of human settlement in hurricane-prone areas on the us atlantic and gulf coasts using nighttime remote sensing |
publisher |
Scholar Commons |
publishDate |
2019 |
url |
https://scholarcommons.sc.edu/geog_facpub/220 https://doi.org/10.5194/nhess-19-2141-2019; https://scholarcommons.sc.edu/context/geog_facpub/article/1222/viewcontent/nhess_19_2141_2019__1_.pdf |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Faculty Publications |
op_relation |
https://scholarcommons.sc.edu/geog_facpub/220 doi: https://doi.org/10.5194/nhess-19-2141-2019 https://scholarcommons.sc.edu/context/geog_facpub/article/1222/viewcontent/nhess_19_2141_2019__1_.pdf |
op_rights |
© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. |
op_doi |
https://doi.org/10.5194/nhess-19-2141-2019;10.5194/nhess-19-2141-2019 |
_version_ |
1799485421017628672 |