Habitat map of seagrass cover derived from a supervised moderate-spatial-resolution multi-spectral satellite image, integrated with manual delineation and coincident field data, Moreton Bay, 2011, with link to shapefile@en

A supervised classification was applied to a Landsat TM5 image. This image was acquired 9:40 am, on the 27th July 2011 (5.14 am low tide at Brisbane Bar). The image classification was applied on areas of clear waters up to three metres depth and for exposed regions of Moreton Bay. Field validation data was collected at 4797 survey sites by UQ. GPS referenced field data were used as training areas for the image classification process. For this training the substrate DN signatures were extracted from the Landsat 5 TM image for field survey locations of known substrate cover, enabling a characteristic \"spectral reflectance signature\" to be defined for each target. The Landsat TM image, containing only those pixels in water < 3.0m deep, was then subject to minimum distance to means algorithm to group pixels with similar DN signatures (assumed to correspond to the different substrata). This process enabled each pixel to be assigned a label of either seagrass cover (0, 1-25 %, 25-50 %, 50-75 % and 75-100 %). The resulting raster data was then converted into a vector polygon file. Species information was added based on the field data and expert knowledge. Both polygon files were joined by overlaying features of remote sensing files with the EHMP field data to produce an output theme that contains the attributes and full extent of both themes. If polygons of remote sensing were within polygons of field data the assumption was made that the remote sensing polygon was showing more detail and the underlying field polygon was deleted.本研究针对陆地卫星5号主题制图仪（Landsat TM5）影像开展监督分类任务。该影像采集于2011年7月27日上午9:40（布里斯班航道（Brisbane Bar）当日凌晨5:14出现低潮）。本次影像分类的覆盖范围为水深不超过3米的清澈水域，以及摩顿湾（Moreton Bay）的裸露区域。昆士兰大学（University of Queensland，UQ）在4797个调查点位采集了野外验证数据，带有全球定位系统（GPS）定位信息的野外实测数据被用作影像分类流程的训练样本区域。在此训练流程中，研究人员从Landsat 5 TM影像中提取已知底质覆盖类型的野外调查点位的底质数字值（Digital Number，DN）特征，进而为每一类目标地物定义专属的"spectral reflectance signature"（光谱反射特征）。随后，仅保留水深小于3.0米水域像素的Landsat TM影像，被应用最小距离均值算法（Minimum Distance to Means Algorithm），以将具有相似DN特征的像素聚类，该聚类结果假定对应不同的底质类型。通过该流程，每个像素均可被赋予海草覆盖度标签，具体类别为：无海草覆盖、1%-25%覆盖、25%-50%覆盖、50%-75%覆盖以及75%-100%覆盖。生成的栅格数据随后被转换为矢量多边形文件。基于野外实测数据与专家经验，为该矢量文件添加了物种相关信息。通过将遥感影像的矢量多边形与EHMP野外数据的矢量要素进行叠加，将两类多边形文件进行融合，最终生成同时包含两类数据属性与完整空间范围的输出图层。若遥感矢量多边形完全包含于野外实测矢量多边形之内，则默认遥感多边形包含更精细的细节信息，并将其对应的野外实测多边形予以删除。