A comparison of two techniques for the rapid assessment of marine habitat complexity

Abstract

Monitoring and assessment of the status and distribution of marine seabed habitats is needed to support existing and emerging environmental policy commitments. Traditional monitoring of habitats and associated species using grabs and trawls is costly and labour intensive and might usefully be complemented by cheaper and more readily automated methods that can be used at higher frequencies and/or on larger spatial scales. We develop and apply two methods to measure seabed habitat complexity and demonstrate how they can be used to describe impacts (e.g. fishing gear impacts) and monitor recovery. The first method relies on the analysis of deviations in a laser line projected on the seabed. The second method is based on the pixel value distribution in seabed photographs. We use both methods to quantify the complexity created by different substrates and habitat-forming species and to establish links between habitat complexity and faunal diversity (richness) and abundance. The habitat complexity index calculated with the laser line method provided a reliable index of complexity across a range of habitat types, showing a monotonic increase with coarseness of the substratum and the abundance of sessile epifauna. Pixel value distributions in the photographs did not reflect the increase in complexity due to sessile epifauna but only reflected substratum differences. Results suggested that the laser line method would be suitable for monitoring the effect of disturbance on habitats ranging from gravelly sands to rock, and their subsequent recovery. The photographic method would be better suited to assessing complexity and heterogeneity of the substratum. Both methods complement conventional biological sampling and can be used at higher frequencies and/or on larger spatial scales per unit cost. The laser line method has considerable potential to support demands for frequent monitoring of seabed habitats and human impacts at a range of spatial scales. It is less costly and labour intensive than existing approaches and can be deployed from vessels of many sizes. © 2012 British Ecological Society

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

“…In terrestrial ecosystems, structural complexity has been related with species diversity and abundance [5,6]. However, while evidence of the importance of the role habitat structural complexity plays in driving key processes in underwater ecosystems exists, the intricacies of the relationships between complexity and important ecological processes are not well understood, due to limitations in the application of current methods to quantify 3D features in underwater environments [7,8]. Thus, our current knowledge of underwater ecosystems and their trajectory is impaired by the lack of understanding of how structural complexity directly and indirectly influences important ecological processes [9].…”

“…While stereo-cameras enable data collection at the desired resolution and spatial scales, they preclude the quantification of Remote Sens. 2016, 8,113 3 of 21 structural complexity to the periods preceding their development, limiting their use to contemporary and future studies [39,42]. As most existing video/photo surveys to date have used monocular cameras, it is clear that there is a great need for a method to produce accurate 3D representations of underwater habitats using data collected by off-the-shelf, monocular cameras [18,19,42].…”

An evaluation of semi‐automated methods for collecting ecosystem‐level data in temperate marine systems

“…Technological developments in the last 2-3 decades have improved our ability to observe large areas of seabed in detail (Lambert et al, 2013), convert images into species data (Kohler & Gill, 2006), and analyse spatial patterns (Foster, Hosack, Hill, Barrett, & Lucieer, 2014). The use of images as a data source continues to grow in tandem with improvements in imaging systems (Beijbom et al, 2015).…”

Using object-based image analysis to determine seafloor fine-scale features and complexity

“…In benthic studies, it has been used with stationary cameras in the deep sea to detect specific organisms and track their movement (Aguzzi et al 2009(Aguzzi et al , 2011. Whole-scene pattern recognition has been used to classify images into discrete categories of habitats (Teixid o et al 2011;Seiler et al 2012;Lambert et al 2013).…”

“…Optical remote sensing of the seafloor generates a large quantity of information, as hours of video transects can easily translate into 1000s to 10,000s of still images. Since it is difficult to standardize the processing of optical imagery, new objective approaches, adapted from concepts of computer vision and image processing, are being developed to accelerate processing speed, remove subjectivity of interpretation and foster collaboration through the transfer of tools (Seiler et al ; Lambert et al ). Image processing methods can be broadly divided into two categories: feature detection and whole‐scene pattern recognition.…”