Wetland inundation monitoring by the synergistic use of ENVISAT/ASAR imagery and ancilliary spatial data
Wetland inundation monitoring by the synergistic use of ENVISAT/ASAR imagery and ancilliary spatial data
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DOI：
10.1016/j.rse.2013.07.028
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发表时间：
2013-12
影响因子：
13.5
通讯作者：
B. Martí-Cardona;J. Dolz-Ripollés;C. López-Martínez
中科院分区：
文献类型：
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作者：
B. Martí-Cardona;J. Dolz-Ripollés;C. López-Martínez
湿地是地球上最重要的生态系统之一，其可持续性在很大程度上取决于水资源。在气候变化和人为压力增加的情况下，对水资源的详细监测为评估生态系统健康和确定潜在威胁提供了一个基本工具。西班牙西南部的Doñana湿地每年夏天都会干涸，秋季和冬季会逐渐泛滥，最大面积达30，000公顷。在2006-2007年水文周期期间，通过21个Envisat/ASAR场景对湿地填充过程进行了详细监测，这些场景以不同的入射角获取，以最大限度地提高观测频率。由于Doñana覆盖后向散射的复杂几何特性，仅从两个不相关的ASAR通道数据进行洪水映射是不可行的。本研究探讨了协同利用ASAR数据与Doñana的数字高程模型和植被图，以实现洪水制图，过滤和聚类算法的自动生成Doñana洪水图从ASAR图像。采用适应于高程等高线的不规则滤波邻域，极大地改善了ASAR图像的滤波效果。由于自然边缘紧密跟随地形轮廓，因此边缘保护非常出色。假设各向同性邻域为单一类，并对其强度进行平均。结果，由于斑点和纹理在相同覆盖类型区域上的强度波动被显著地平滑化。聚类和分类算法在各个子盆地上操作，因为像素高程与其中的覆盖类别更准确地相关。植被和海拔地图加上知识的Doñana后向散射特性从以前的研究最初被用来选择种子像素的类成员具有高的信心。然后，采用区域生长算法，根据新像素与种子的平面邻接关系和后向散射马氏距离对种子区域进行扩展，在种子区域生长过程中，新像素的可能类别不受植被图的覆盖类型限制，从而能够捕捉植被空间分布的时间变化。比较所得的分类和并发地面实况产生了92%的洪水映射精度。洪水测绘方法适用于其他六个水文周期的Doñana现有ASAR图像。
Wetlands are among the most ecologically important ecosystems on Earth and their sustainability depends critically on the water resources. In a scenario of climate change and increased anthropogenic pressure, detailed monitoring of the water resources provides a fundamental tool to assess the ecosystem health and identify potential threats.Doñana wetlands, in Southwest Spain, dry out every summer and progressively flood in fall and winter to a maximum extent of 30,000 ha. The wetland filling up process was monitored in detail during the 2006–2007 hydrologic cycle by means of twenty-one Envisat/ASAR scenes, acquired at different incidence angles in order to maximize the observation frequency. Flood mapping from the two uncorrelated ASAR channel data alone was proved unfeasible due to the complex casuistic of Doñana cover backscattering. This study addresses the synergistic utilization of the ASAR data together with Doñana's digital elevation model and vegetation map in order to achieve flood mapping.Filtering and clustering algorithms were developed for the automated generation of Doñana flood maps from the ASAR images. The use of irregular filtering neighborhoods adapted to the elevation contours drastically improved the ASAR image filtering. Edge preservation was excellent, since natural edges closely follow terrain contours. Isotropic neighborhoods were assumed of a single class and their intensities were averaged. As a result, intensity fluctuations due to speckle and texture over areas of the same cover type were smoothed remarkably.The clustering and classification algorithm operate on individual sub-basins, as the pixel elevation is more accurately related to the cover classes within them. Vegetation and elevation maps plus knowledge of Doñana backscattering characteristics from preceding studies were initially used to select seed pixels with high confidence on their class membership. Next, a region growing algorithm extends the seed regions with new pixels based on their planimmetric adjacency and backscattering Mahalanobis distance to the seeds.During the seed region growth, new pixels' possible classes are not constrained to their cover type according to the vegetation map, so the algorithm is able to capture temporal changes in the vegetation spatial distribution. Comparison of the resultant classification and concurrent ground truth yielded 92% of flood mapping accuracy. The flood mapping method is applicable to the available ASAR images of Doñana from six other hydrologic cycles.