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Depth Estimation Paper Published

One step in the MORE-MAPS habitat mapping process is depth estimation. Existing methods were found to perform poorly in a temperate reef environment, so a new method was developed that focuses on flexibility and ease of implementation. Find out more here. Thanks to Auckland Council for funding this work, DigitalGlobe Foundation for granting me the WorldView-2 and WorldView-3 imagery, and to Leigh Marine Lab for providing such a great place to work.

Abstract: Existing empirical methods for estimation of bathymetry from multispectal satellite imagery are based on simplified radiative transfer models that assume that transformed radiance values will have a linear relationship with depth. However, application of these methods in temperate coastal waters of New Zealand demonstrates that this assumption does not always hold true and consequently existing methods perform poorly. A new purely empirical method based on a nonparametric nearest-neighbor regression is proposed and applied to WorldView-2 and WorldView-3 imagery of temperate reefs dominated by submerged kelp forests interspersed with other bottom types of varying albedo including reef devoid of kelp and large patches of sand. Multibeam sonar data are used to train and validate the model and results are compared with those from a widely used linear empirical method. Free and open source Python code was developed for the implementation of both methods and is presented for use. Given sufficient training data, the proposed method provided greater accuracy (0.8 m RMSE) than the linear empirical method (2.2 m RMSE) and depth errors were less dependent on bottom type. The proposed method has great potential as an efficient and inexpensive method for the estimation of high spatial resolution bathymetry over large areas in a wide range of coastal environments.

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Benthic Photo Survey Paper

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A paper was published in the Journal of Open Research Software about Benthic Photo Survey (BPS). BPS is the component of MORE-MAPS used to process raw field data into a useful format. You can read or download the paper here.

Abstract:

Photo survey techniques are common for resource management, ecological research, and ground truthing for remote sensing but current data processing methods are cumbersome and inefficient. The Benthic Photo Survey (BPS) software described here was created to simplify the data processing and management tasks associated with photo surveys of underwater habitats. BPS is free and open source software written in Python with a QT graphical user interface. BPS takes a GPS log and jpeg images acquired by a diver or drop camera and assigns the GPS position to each photo based on time-stamps (i.e. geotagging). Depth and temperature can be assigned in a similar fashion (i.e. depth-tagging) using log files from an inexpensive consumer grade depth / temperature logger that can be attached to the camera. BPS provides the user with a simple interface to assign quantitative habitat and substrate classifications to each photo. Location, depth, temperature, habitat, and substrate data are all stored with the jpeg metadata in Exchangeable image file format (Exif). BPS can then export all of these data in a spatially explicit point shapefile format for use in GIS. BPS greatly reduces the time and skill required to turn photos into usable data thereby making photo survey methods more efficient and cost effective. BPS can also be used, as is, for other photo sampling techniques in terrestrial and aquatic environments and the open source code base offers numerous opportunities for expansion and customization.