Bin Zhang , Ling Chang , Zhengbing Wang , Li Wang , Qinghua Ye , Alfred Stein
{"title":"利用多源遥感图像绘制十年期荷兰沿海动态地图","authors":"Bin Zhang , Ling Chang , Zhengbing Wang , Li Wang , Qinghua Ye , Alfred Stein","doi":"10.1016/j.jag.2025.104452","DOIUrl":null,"url":null,"abstract":"<div><div>Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and unclear boundaries between land, tidal flats, and water. Traditional waterline methods struggle under these conditions. In this study, we propose an Object-Based Image Segmentation (OBIS) method, specifically designed for SAR images, to extract waterlines and distinguish tidal flats and shorelines from water bodies. This method integrates SAR polarimetric feature analysis to select high-quality images and employs partition processing to preserve local feature statistics. Using 199 Sentinel-1 GRD, 132 Radarsat-2 SLC, and 157 Landsat images, we analyzed coastal dynamics in the Dutch Wadden Sea from 1986 to 2020. Our DEMs, validated against LiDAR data (2016–2019) and 58 ground anchor measuring stations (2011–2020), achieved an accuracy of 10–30 cm. Results show that coastal tidal flats and sandbanks expanded at rates of 0.107–0.324 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> yr<sup>−1</sup> and 0.010–0.073 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> yr<sup>−1</sup>, respectively, with a net intertidal volume increase of approximately <span><math><mrow><mn>8</mn><mo>.</mo><mn>6</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>7</mn></mrow></msup><mspace></mspace><msup><mrow><mtext>m</mtext></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>. The generated DEMs provide valuable insights for sediment budget evaluation and hydrodynamic modeling, supporting scientific research and coastal management. The proposed OBIS-based framework demonstrates its effectiveness for mapping national-scale tidal flats and sandbanks dynamics.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"138 ","pages":"Article 104452"},"PeriodicalIF":7.6000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-decadal Dutch coastal dynamic mapping with multi-source remote sensing imagery\",\"authors\":\"Bin Zhang , Ling Chang , Zhengbing Wang , Li Wang , Qinghua Ye , Alfred Stein\",\"doi\":\"10.1016/j.jag.2025.104452\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and unclear boundaries between land, tidal flats, and water. Traditional waterline methods struggle under these conditions. In this study, we propose an Object-Based Image Segmentation (OBIS) method, specifically designed for SAR images, to extract waterlines and distinguish tidal flats and shorelines from water bodies. This method integrates SAR polarimetric feature analysis to select high-quality images and employs partition processing to preserve local feature statistics. Using 199 Sentinel-1 GRD, 132 Radarsat-2 SLC, and 157 Landsat images, we analyzed coastal dynamics in the Dutch Wadden Sea from 1986 to 2020. Our DEMs, validated against LiDAR data (2016–2019) and 58 ground anchor measuring stations (2011–2020), achieved an accuracy of 10–30 cm. Results show that coastal tidal flats and sandbanks expanded at rates of 0.107–0.324 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> yr<sup>−1</sup> and 0.010–0.073 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> yr<sup>−1</sup>, respectively, with a net intertidal volume increase of approximately <span><math><mrow><mn>8</mn><mo>.</mo><mn>6</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>7</mn></mrow></msup><mspace></mspace><msup><mrow><mtext>m</mtext></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>. The generated DEMs provide valuable insights for sediment budget evaluation and hydrodynamic modeling, supporting scientific research and coastal management. The proposed OBIS-based framework demonstrates its effectiveness for mapping national-scale tidal flats and sandbanks dynamics.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"138 \",\"pages\":\"Article 104452\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225000998\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225000998","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
Multi-decadal Dutch coastal dynamic mapping with multi-source remote sensing imagery
Tidal flats and their associated sandbanks are dynamic environments crucial for ecological balance and biodiversity. Monitoring their evolutionary history and topographic changes is important to better understand their dynamic mechanisms and predict their future status. Accurately mapping their evolution, however, remains challenging due to highly dynamic currents, suspended sediment variability, and unclear boundaries between land, tidal flats, and water. Traditional waterline methods struggle under these conditions. In this study, we propose an Object-Based Image Segmentation (OBIS) method, specifically designed for SAR images, to extract waterlines and distinguish tidal flats and shorelines from water bodies. This method integrates SAR polarimetric feature analysis to select high-quality images and employs partition processing to preserve local feature statistics. Using 199 Sentinel-1 GRD, 132 Radarsat-2 SLC, and 157 Landsat images, we analyzed coastal dynamics in the Dutch Wadden Sea from 1986 to 2020. Our DEMs, validated against LiDAR data (2016–2019) and 58 ground anchor measuring stations (2011–2020), achieved an accuracy of 10–30 cm. Results show that coastal tidal flats and sandbanks expanded at rates of 0.107–0.324 km yr−1 and 0.010–0.073 km yr−1, respectively, with a net intertidal volume increase of approximately . The generated DEMs provide valuable insights for sediment budget evaluation and hydrodynamic modeling, supporting scientific research and coastal management. The proposed OBIS-based framework demonstrates its effectiveness for mapping national-scale tidal flats and sandbanks dynamics.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.