Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

海草的时空动态已在从叶片到斑块（100平方米）的尺度上开展了相关研究。但在海草生态学领域，景观尺度（>100平方千米）的海草种群动态仍未得到充分解析。此前的遥感研究手段要么时空分辨率不足，要么缺乏适配生态学需求的制图方案，无法完全解决这一难题。本研究提出一种稳健的半自动面向对象影像分析（object-based image analysis）方法，可结合野外实测时间序列数据与同步高空间分辨率卫星影像，实现优势海草物种、盖度百分比及地上生物量的制图。本次研究的研究区域为澳大利亚摩顿湾东滩一片面积142平方千米的浅清水域海草生境。研究团队利用2004年至2013年间获取的9组数据集，通过整合海草样带摄影野外调查数据，以及经过大气校正与几何校正的高空间分辨率卫星影像数据（WorldView-2、IKONOS及Quickbird-2），并采用面向对象影像分析方法，生成了海草物种与盖度百分比分布图。地上生物量分布图则通过针对各海草物种的原位地上生物量实测数据训练得到的经验模型生成。绘制的分布图与汇总图表揭示了海草物种组成、盖度百分比及地上生物量的年际与年内变化。本研究的方法体系涵盖了严谨的野外与影像数据采集及预处理流程、用于提取海草物种与盖度分布图并评估精度的半自动方案，以及后续海草生物量的经验建模流程。最终生成的分布图为理解浅水环境下的景观尺度海草动态提供了基础数据集。本研究的发现为利用遥感海草产品的时间序列分析开展海草生态学研究与管理工作提供了概念验证。