Xiaoyong Ming , Yichao Tian , Qiang Zhang , Yali Zhang , Jin Tao , Junliang Lin
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Therefore, we aim to rely entirely on Earth observation satellite platforms, combining satellite-based Light Detection and Ranging (LiDAR) and optical remote sensing to monitor extensive mangrove tidal flat topography. This methodology was rigorously applied and validated on China’s largest and most representative mangrove tidal flats, revealing a Root Mean Square Error (RMSE) not exceeding 7.5 cm and an R-squared value surpassing 0.89 when compared to airborne LiDAR data. We use the inundation frequency derived from the long-term Sentinel-2 image sequences and elevation data extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to establish a specific relationship between inundation frequency and ground elevation using both classical and generalized regression models, a mangrove tidal flat topography covering 76.9 km<sup>2</sup> was generated. 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We use the inundation frequency derived from the long-term Sentinel-2 image sequences and elevation data extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to establish a specific relationship between inundation frequency and ground elevation using both classical and generalized regression models, a mangrove tidal flat topography covering 76.9 km<sup>2</sup> was generated. 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引用次数: 0
摘要
潮滩是地球上最重要的生态系统之一,具有重要的生态价值,但这些地区也是最脆弱的生态系统之一。详细的潮滩地形调查对于探讨潮滩生态系统对环境变化的响应和预测形态变化,从而影响红树林生态系统的保护和恢复至关重要。然而,红树林潮滩地形的可用数据仍然缺乏,因为大多数测量主要依靠传统的制图方法或小规模调查。因此,我们的目标是完全依靠对地观测卫星平台,结合基于卫星的光探测与测距(LiDAR)和光学遥感来监测大面积的红树林潮滩地形。该方法在中国最大、最具代表性的红树林潮滩上得到了严格的应用和验证,与机载激光雷达数据相比,其均方根误差(RMSE)不超过7.5厘米,r平方值超过0.89。利用Sentinel-2卫星长期影像序列的淹没频率和ICESat-2卫星(Ice, Cloud, and Land elevation Satellite-2)的高程数据,利用经典回归模型和广义回归模型建立了淹没频率与地面高程的特定关系,生成了覆盖76.9 km2的红树林潮滩地形。研究结果确定了茂尾海红树林的适宜分布区,面积为18.2 km2。
Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves
Tidal flats represent one of the Earth’s most critical ecosystems characterized by substantial ecological value, but these areas are also among the most fragile ecosystems. A detailed topography survey of tidal flat is essential for exploring how tidal flat ecosystems respond to environmental changes and for predicting morphological shifts, thereby impacting the protection and restoration of mangrove ecosystems. However, there is still a dearth of data available for mangrove tidal flat topography, as the majority of measurements primarily rely on traditional cartographic methods or small-scale surveys. Therefore, we aim to rely entirely on Earth observation satellite platforms, combining satellite-based Light Detection and Ranging (LiDAR) and optical remote sensing to monitor extensive mangrove tidal flat topography. This methodology was rigorously applied and validated on China’s largest and most representative mangrove tidal flats, revealing a Root Mean Square Error (RMSE) not exceeding 7.5 cm and an R-squared value surpassing 0.89 when compared to airborne LiDAR data. We use the inundation frequency derived from the long-term Sentinel-2 image sequences and elevation data extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to establish a specific relationship between inundation frequency and ground elevation using both classical and generalized regression models, a mangrove tidal flat topography covering 76.9 km2 was generated. Our findings delineate suitable distribution areas for mangroves in the Maowei Sea, covering an expansive 18.2 km2.
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.