Coral Reef Habitat Mapping Research Articles

Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites

High spatial resolution benthic habitat information is essential for coral reef protection and coastal environmental management. Satellite-based shallow benthic composition mapping offers a more efficient approach than traditional field measurements, especially given the advancements in high spatial and temporal resolution satellite imagery. The Planet Dove satellite constellation now has more than 150 instruments in orbit that offer daily coverage at high spatial resolution (3.7 m). The Dove constellation provides regularly updated imagery that can minimize cloud in tropical oceans where dense cloud cover persists. Daily image acquisition also provides an opportunity to detect time-sensitive changes in shallow benthic habitats following coral bleaching events, storms, and other disturbances. We developed an object-based coral reef habitat mapping approach for Dove and similar multispectral satellites that provides bathymetry estimation, bottom reflectance retrieval, and object-based classification to identify different benthic compositions in shallow coastal environments. We tested our approach in three study sites in the Dominican Republic using 18 Dove images. Benthic composition classification results were validated by field measurements (overall accuracy = 82%). Bathymetry and bottom reflectance significantly contributed to identifying benthic habitat classes with similar surface reflectance. This new object-based approach can be effectively applied to map and manage coral reef habitats.

Coral reef habitat mapping: A combination of object-based image analysis and ecological modelling

Despite being one of the most important and well-studied coral reefs in the world, the full extent of coral habitat of the Great Barrier Reef (GBR) is not well mapped and there is no current and comprehensive map of the GBR's geomorphic zonation or benthic composition. This paper demonstrates an approach that integrates ecological coral habitat mapping with empirical modelling to map the geomorphic zonation and benthic composition of the “shallow offshore reefs” of the GBR, using the Capricorn Bunker Group (CBG) as a case study. The approach combined environmental data sets and geo-ecological rule sets to identify geomorphic zones. The benthic composition of individual geomorphic zones was mapped for: shallow reef flat zones, using object-based image analysis with context driven rules based on coral reef ecology; and reef slope zones, using levels of wave exposure to predict the distribution of coral types. The environmental data sets used were field-based benthic composition data, Landsat 8 OLI satellite image-derived bottom reflectance, water depth and slope (15 m × 15 m pixel size) data, reef impact data, and modelled wave exposure. The study showed that the combination of geomorphic-ecological rules and models with remote sensing imagery provided robust mapping results over a large (~2500 km2) reef system, of which 245 km2 was mapped as shallow coral reefs and 88 km2 of that was mapped as areas containing coral. Most importantly, the method produced defined the geomorphic zones and benthic composition of a study area that is significantly larger than the majority of coral reef remote sensing mapping projects previously published. With some modifications, the methods presented have the potential to be applied to the full extent of the shallow offshore reefs of the GBR, or any large reef globally. Monitoring and management of coral reefs for conservation and other purposes, at regional to global scales will benefit from the ability to produce and use this type of essential information on a regular basis.

A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples

ABSTRACTThe preparation of control data is a primary concern in many supervised classification schemes. In coral reef mapping, this issue becomes more severe for three reasons: (1) control samples, located beneath the water, are quite difficult and costly to access; (2) because of the high spatial variability of coral reef habitats, it is very difficult to obtain high-quality samples; and (3) pure training samples are also hardly achievable. These issues, namely quantity, quality, and impurity challenges, are the main focus of this study. Three classification algorithms, including Maximum Likelihood Classifier (MLC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), are comprehensively evaluated, and their requirements for control data are determined. To accomplish this, rich field data, collected from diving off of Lizard Island in eastern Australia, and Landsat-8 images are used as the input data. With respect to accuracy, ANN is best, as it can deal with the complexity of coral reef environments; however, it requires a higher number of training samples (i.e. ANN cannot manage the quantity challenge). On the other hand, SVM shows the best resistance against the quantity and impurity challenges. Being aware of these points, a coral reef map is produced, for the first time, of the northern Persian Gulf, a coral habitat with very special environmental conditions. In this region, SVM achieved 68.42% overall accuracy, even though a very limited field work campaign was conducted to provide the control data.

Object-based Image Analysis for Coral Reef Benthic Habitat Mapping with Several Classification Algorithms

Object-based classification technique (OBIA) in Landsat image analysis is assumed to be able to increase classification accuracy result compared with a pixel-based classification technique. Most of the previous research was conducted using conventional (pixel-based) classification technique such as maximum likelihood algorithm. The objective of this research was to determine accuracy values of coral reef habitat classification based on OBIA algorithms such as Support Vector Machine (SVM), Random Tree, Decision Tree (DT), Bayesian, and k-Nearest Neighbour (KNN). The Landsat 8 OLI satellite imagery acquired on 17 October 2013 on coral reef area in Morotai island, North Maluku Province, Indonesia was analysed in this study. Field data was obtained in October 2012 using transect photo technique. The field data was then classified employing OBIA classification within 7 (seven) classes i.e., sand-rubble, rubble, coral, sand, sand-algae, seagrass, and rubble-sand. Results showed that the highest values of overall accuracy and Kappa on coral reef habitat mapping was produced by SVM algorithm which reached 73% and 0.64 followed by RT, KNN, Bayesian, and DT algorithms of 68% and 0.59, 67% and 0.56, 66% and 0.53, and 56% and 0.46, respectively. Those results showed that the classification methods based OBIA approach within > 6 classes produced better accuracy than the pixel based classification technique.

MAPPING CORAL REEF HABITAT WITH AND WITHOUT WATER COLUMN CORRECTION USING QUICKBIRD IMAGE

Remote sensing from space offers an effective approach to solve the limitation of field sampling, in particular to monitor the reefs in remote sites. Moreover, using the achieved remotely sensed data, it is even possible to monitor the historic status of the coral reef environment. The capabilities of satellite remote sensing techniques combined with the field data collection have been assessed for generating coral reef habitat mapping of the Derawan Island. A very high spatial resolution multi-spectral QuickBird image (October 2003) has been used. The capability of QuickBird image to generate a coral reef habitat map with the water column correction by applying the Lyzenga method, and also without the water column correction by the applying maximum likelihood method, have been assessed. The classification accuracy of the coral reef habitat map increased after the improvement of the water column effects. The classification of QuickBird image for coral reef habitat mapping increased up to 22% by applying a water column correction. Keywords : Coral Reef, Quickbird, Water Column Correction

Coral reef habitat mapping using remote sensing: A user vs producer perspective. implications for research, management and capacity building

Remote sensing is a tool that shows its real power and value for large‐scale work. However, published coral reef applications often still focus on spatially limited pilot sites which are investigated for research purposes, while synoptic, reliable and thematically rich habitat maps are in growing demand everywhere around the Planet for a variety of applications. The observed failure in widespread generalization is primarily due to differences in the criteria that scientists (producers) and managers (users) have to obey in their respective professional activities. To enhance the situation and promote mass production of habitat maps, especially in a capacity building context, the traditional producer's habitat mapping flow chart is challenged and compared with a user's flow chart that has been repeatedly applied to high resolution images for a variety of recent management projects. The user's flow chart is a drastic simplification of the producer's chart. It conforms to different principles, in the search for representativity and specificity and thus exhaustivity. The habitat maps donerespecting these principles provide a spectacular increase in the ratio habitat complexity/accuracy that was previously the norm. However, in addition to basic knowledge on a limited set of image processing techniques, the user's chart implies that practitioners in both developed and developing countries also acquire expertise in recognizing coral reef structures to be able to take advantage of the empirical but efficient a priori contextual editing techniques. The proposed user's flow chart should be promoted by funding agencies for capacity building in order to quickly fill the current gaps in reliable geographical data.

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5–11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15–20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4–5 classes, 71% for 7–8 classes, 65% in 9–11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5–10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications.

A comparison of Landsat ETM+, SPOT HRV, Ikonos, ASTER, and airborne MASTER data for coral reef habitat mapping in South Pacific islands

The performances of the Landsat-7 ETM+, ASTER, SPOT HRV, and Ikonos satellite sensors and the airborne MASTER (MODIS‐ASTER simulator) were compared for coral reef habitat mapping in South Pacific reefs. This unique image data set provided different spatial resolution (4 m for Ikonos to 30 m for Landsat-7 ETM+), spectral resolution (two visible bands for SPOT-HRV to five visible bands for MASTER) and digitization (8‐16 bits). We focused on two islands (Tahiti and Moorea, French Polynesia) with barrier and fringing structures representative of reefs of South Pacific volcanic islands. Five levels of benthic habitat complexity were defined (with three, four, five, seven, and nine classes). Using a supervised maximum likelihood algorithm, the comparisons suggested several trends in sensor performances. Overall accuracies of Landsat-7 ETM+ compared well with sensors with higher spatial (Ikonos) or spectral (MASTER) resolution for low or moderate habitat complexity mapping. For high-complexity mapping (nine classes), Ikonos performed best, suggesting that high spatial resolution is important. For low- and moderate-complexity mapping, MASTER performed best, suggesting that spectral resolution and digitization seem more critical. However, these trends must be discussed cautiously in the light of various factors before any generalization can be made. These factors include issues in reconciling-scaling ground-truth data at multiple spatial and thematic scale, reefs specificities, and environmental conditions during image acquisition.

Coral reef habitat mapping: how much detail can remote sensing provide?

The capability of satellite and airborne remote-sensing methods for mapping Caribbean coral reefs is evaluated. Reef habitats were categorised into coarse, intermediate and fine detail, using hierarchical classification of field data (percent cover in 1 m quadrats and seagrass standing-crop). Habitats were defined as assemblages of benthic macro-organisms and substrata and were mapped using the satellite sensors Landsat MSS, Landsat TM, SPOT XS, SPOT Pan and merged Landsat TM/SPOT Pan. Habitats were also mapped using the high-resolution digital airborne sensor, CASI (compact airborne spectrographic imager). To map areas >60 km in any direction with coarse detail, Landsat TM was the most accurate and cost-effective satellite sensor (SPOT XS when 9 reef classes) had a maximum accuracy of 37% when based on satellite imagery, but aerial photography and CASI achieved accuracies of 67 and 81%, respectively. Commissioning of new aerial photography does not appear to be a cost-effective option; satellites are cheaper for coarse habitat-mapping, and detailed habitat-mapping can be conducted more accurately and cheaply with CASI. The results will guide practitioners in matching survey objectives to appropriate remote-sensing methods.