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首页> 外文期刊>Journal of Experimental Marine Biology and Ecology >Kelp-bed dynamics across scales: Enhancing mapping capability with remote sensing and GIS
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Kelp-bed dynamics across scales: Enhancing mapping capability with remote sensing and GIS

机译：跨尺度的海藻床动力学：通过遥感和GIS增强制图能力

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摘要

Kelp are important drivers of productivity and biodiversity patterns in cold-water and nutrient-rich rocky reefs. Scuba- and boat-based methods are routinely used to study submerged kelp beds. However, these time-consuming and labor-intensive methods enable monitoring of beds or the factors and processes that control their distribution over only small spatial (few 100s of m(2)) and temporal (< 5 years) scales. Remote sensing and geographic information system (GIS) technologies are increasingly used to compare marine species distribution over multiple spatiotemporal scales. However, there is currently no clear framework and limited demonstration of their potential for studies of broad-scale changes in completely submerged kelp beds. The present study aims to establish the foundation of a simple, accessible, and robust set of remote sensing and GIS-based methods to address key questions about the stability of subtidal kelp beds across multiple spatial and temporal scales. It tests the suitability of conventional image classification methods for mapping kelp from digital aerial (acquired on board a helicopter) and satellite (SPOT 7) imagery of similar to 250 ha of seabed around four islands in the Mingan Archipelago (northern Gulf of St. Lawrence, Canada). Three classification methods are compared: 1) a software-led unsupervised classification in which pixels are grouped into clusters based on similarity in spectral signature among pixels; 2) a software-led supervised classification in which pixels are assigned to categories based on similarity in the spectral signature of pixels and that of reference data from each category; and 3) a visual classification carried out by a trained observer. Supervised classification of satellite imagery and visual classification of aerial imagery were the top methods to map kelp, with overall accuracies of 89% and 90%, respectively. Unsupervised classification of both types of imagery showed poor discrimination between kelp and non-kelp benthic classes. Kelp bed edges were more difficult to identify on satellite than aerial imagery because the former presented poorer contrasts and a lower spatial resolution. Kelp bed edges identified with visual classification appeared artificially jagged for both types of imagery, mainly because of the coarse (225-m(2)) spatial units used for this classification. Kelp bed edges were smoother on maps created with the unsupervised and supervised classifications, which used 1-m-pixel images. The present study demonstrates that conventional remote sensing and GIS methods can accurately map submerged kelp beds over large spatial domains in the Mingan Archipelago or in other benthic systems with similar oceanic conditions and a largely dichotomous (kelp-barrens) biological makeup.
机译：海带是冷水和营养丰富的礁石中生产力和生物多样性模式的重要驱动力。潜水和船载方法通常用于研究水下海藻床。但是，这些耗时且劳动强度大的方法仅在很小的空间范围（几百个m（2））和时间范围（小于5年）内监控床或控制其分布的因素和过程。越来越多地使用遥感和地理信息系统（GIS）技术来比较多个时空尺度上的海洋物种分布。但是，目前尚无明确的框架，并且有限地证明了它们在研究完全淹没海带床中大规模变化方面的潜力。本研究旨在建立一套简单，可访问且功能强大的遥感和基于GIS的方法的基础，以解决有关潮下海带在多个时空尺度上稳定性的关键问题。它测试了常规图像分类方法是否适用于从数字航空（在直升机上获取）和卫星图像（SPOT 7）绘制海带图，该图像类似于在民丹群岛（圣劳伦斯湾北部）的四个岛周围250公顷海底，加拿大）。比较了三种分类方法：1）软件主导的无监督分类，其中，基于像素之间光谱特征的相似性将像素分组为群集； 2）由软件主导的监督分类，其中，根据像素的光谱特征和来自每个类别的参考数据的光谱特征的相似性，将像素分配给各个类别； 3）由训练有素的观察者进行的视觉分类。卫星图像的监督分类和航空图像的视觉分类是绘制海带的最佳方法，总体准确度分别为89％和90％。两种图像的无监督分类显示了海带和非海带底栖动物类别之间的区别。与卫星图像相比，海带床边缘在卫星上更难识别，因为前者对比度较差且空间分辨率较低。对于这两种图像，用视觉分类识别的海带床边缘均出现了人为锯齿状的锯齿，这主要是因为用于该分类的粗糙（225-m（2））空间单位。在使用无监督和有监督分类（使用1米像素图像）创建的地图上，海带床边缘更加平滑。本研究表明，传统的遥感和GIS方法可以在明安群岛或其他具有类似海洋条件和大量二分（海藻贫瘠）生物构成的底栖系统中的大型空间区域上准确地映射淹没海藻海床。

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