TY - JOUR
T1 - The first 10-m China’s national-scale sandy beach map in 2022 derived from Sentinel-2 imagery
AU - Ni, Ming
AU - Xu, Nan
AU - Ou, Yifu
AU - Yao, Jiaqi
AU - Li, Zhichao
AU - Mo, Fan
AU - Huang, Conghong
AU - Xin, Huichao
AU - Xu, Hao
PY - 2024/12
Y1 - 2024/12
N2 - Sandy beaches are at the frontline of resisting continuous sea level rise associated with anthropogenic climate change. However, accurate and comprehensive spatial information for monitoring, utilizing, and protecting sandy beaches is still lacking at the national or above scales. This study, for the first time, addresses this gap by collecting cloud-free, low-tide Sentinel-2 images in 2022 to map 10-m sandy beaches across China using the image classification method. We adopted the Support Vector Machine to derive the spatial distribution of sandy beaches, assess accuracy, and analyze spatial characteristics. Our results demonstrate the efficiency of the SVM model in mapping sandy beaches (User's accuracy: 96%, Kappa coefficient: 0.93). We identified 3,444 beaches in China, with a total length of 3,187.57 km, an average width of 69.93 meters, and a total area of 217.43 km², constituting 24.16% of the national coastline. Notably, Guangdong, Taiwan, and Hainan provinces are rich in beach resources, whereas Macao, Shanghai, Tianjin, and Jiangsu provinces have relatively fewer beach resources. Further, our results outperform the existing OpenStreetMap beach dataset. Our developed 10-m beach database is crucial for analyzing potential beach risks, uncovering socioeconomic values of beach resources, and promoting the sustainable coastal zone development in China.
AB - Sandy beaches are at the frontline of resisting continuous sea level rise associated with anthropogenic climate change. However, accurate and comprehensive spatial information for monitoring, utilizing, and protecting sandy beaches is still lacking at the national or above scales. This study, for the first time, addresses this gap by collecting cloud-free, low-tide Sentinel-2 images in 2022 to map 10-m sandy beaches across China using the image classification method. We adopted the Support Vector Machine to derive the spatial distribution of sandy beaches, assess accuracy, and analyze spatial characteristics. Our results demonstrate the efficiency of the SVM model in mapping sandy beaches (User's accuracy: 96%, Kappa coefficient: 0.93). We identified 3,444 beaches in China, with a total length of 3,187.57 km, an average width of 69.93 meters, and a total area of 217.43 km², constituting 24.16% of the national coastline. Notably, Guangdong, Taiwan, and Hainan provinces are rich in beach resources, whereas Macao, Shanghai, Tianjin, and Jiangsu provinces have relatively fewer beach resources. Further, our results outperform the existing OpenStreetMap beach dataset. Our developed 10-m beach database is crucial for analyzing potential beach risks, uncovering socioeconomic values of beach resources, and promoting the sustainable coastal zone development in China.
KW - Coastal
KW - China
KW - remote sensing
KW - national-scale
UR - https://www.scopus.com/pages/publications/85209645471
U2 - 10.1080/17538947.2024.2425163
DO - 10.1080/17538947.2024.2425163
M3 - Article
SN - 1753-8947
VL - 17
JO - International Journal of Digital Earth
JF - International Journal of Digital Earth
IS - 1
M1 - 2425163
ER -