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Spectral resolution improved for habitat mapping of oyster reefs with additional spectral bands. High-spatial scale monitoring and mapping of turbid exposed intertidal rocky reefs presents unique challenges, but these challenges can be mitigated by targeting flights during ideal meteorological and oceanographic conditions with RPAS.

Keywords: oyster reefs, intertidal marine ecosystem, mapping, object-based image analysis OBIA , rule-based classification, low altitude remote sensing, remotely piloted aircraft system RPAS. Shellfish e. These habitats are economically and ecologically valuable, but they are depleted to numerous anthropogenic and natural hazards. All of which are important in the Anthropocene era.

Recent advances in space-borne e. Consequently, high-resolution imagery often translates into high-costs due to increased storage and processing time taken to orthorectify the imagery to the required accuracy.

Depending on the financial constraints high-resolution imagery may not be easily accessible for all researchers. Mapping biogenic intertidal rocky reefs in temperate waters during high tides is challenging due to turbidity and suspended sediment concentration SSC , which attenuates light penetration through the water column.

Reference source not found. Therefore, for the application of remote sensing techniques to effectively map and detect features, the sensor and platform must be appropriate to the feature being sensed. The issue of spectral band attenuation in the water column and spatial resolution can be solved by surveying intertidal biogenic habitats at low tide.

In Europe, during low tide, researchers successfully classified intertidal biogenic reefs, built by the honeycomb worm, from an RPAS derived multispectral red-edge RE and near-infrared NIR very-high-resolution 0. RE and NIR provided further opportunities to identify and map biogenic habitats based on their spectral characteristics e.

For classification of high-resolution e. RPAS in most diverse fields of science has established itself as a new tool, capable of providing unprecedented scientific applications. We show the benefits of enhanced spectral bands for remote sensing of biogenic habitats, and report on time taken and area covered from the RPAS flights. In addition, demonstrate the suitability of RPAS imagery and the accuracy of OBIA combined with rule-based classification for detecting and delineating oyster reefs from a heterogeneous intertidal rocky reef ecosystem.

Located at The harbor has been altered by human activities, pollution, urbanization, and it is a hotspot for non-indigenous species.

Figure 1 a Location of study site in Auckland, New Zealand. This reef structure withstands environmental and human impacts, such as discharge of effluent, trampling while setting fish nets during low tides and shell-fishing, and it is a popular spot for bird watchers, hikers and dog walkers.

One of the benefits of using this sensor is to perform a radiometric correction during processing in Pix4D 33 and using the following equation allows reflectance to be computed for all the bands:.

The images were stitched and rendered into absolute reflectance maps for each spectral band; pixel values ranged from To geo-reference the orthomosaic accurately, 10 ground control targets were laid out evenly across the study area.

These targets were made similar to a section of a checkerboard, printed on an A3 paper and laminated. To examine, the potential of high-resolution imagery and accuracy of the OBIA technique to extract targeted features from a heterogeneous ecosystem we decided to use only one RPAS flight. This section of the reef is covered with mixed vegetation and oysters are present along the north-west section of the reef. The benefit of this module is that the results of segmentation can be previewed and refined prior to the final segmentation.

The FEM consisted of two techniques 1 find object a segment, b merge, c refine, d compute attributes and 2 extract objects, a rule-based classification, b exporting results to a shapefile.

ENVI, feature extraction module uses an object-based approach to extract objects with spatial, spectral brightness and color , and textural characteristics from high-resolution multispectral imagery. The benefit of this module is that multiple objects can be extracted at a time, offers more flexibility in the types of objects being extracted and the classification results can be previewed and further refined through the ENVI Zoom preview portal.

The following land cover classes were defined: 1 mangroves and saltmarsh are vegetation that reflects most NIR spectrum, rule assigned using their mean spectral attribute in band 4 NIR ; 2 shadows, bare rocks and sediments appeared very dark in the NIR band, so spectral mean values attributes were used to create a rule for these objects and were merged together; 3 finally, using spatial and spectral attributes of oyster reefs in band 1 blue , rules were created based on, the mean spectral attribute, pattern of distribution, their shape and appearance light or dark.

One of the objectives of this study was to discriminate oyster reefs from multispectral orthomosaics from other land cover features. To achieve this, we capitalized on the spectral reflective properties of different objects on the ground.

The following remote sensing principles were followed: 1 most vegetation mangroves and saltmarsh absorbs red and blue light for photosynthesis and a plant with chlorophyll reflects near-infrared light, spectral reflectance: 0. Shadows, bare rock at a higher elevation than oyster reefs and sediment lower elevation than oyster reefs have dark texture and appearance, spectral reflectance 0.

Oyster reefs have spectral reflectance: 0. For accuracy assessment, used 2, random regions of interest ROI and an equalized stratified random sampling strategy to create ground truth ROI in the orthomosaic for each class in the total land cover area.

The reliability of ground truth points is greater with a high-resolution 3. We also conducted an on-foot survey on 2 May , to confirm the visual interpretation of oysters in the land cover from the classification map.

Here we zoomed in both imagery until they are blurred to show the scale at which we can view the imagery without any modification or classification. This study does not distinguish between different types of oysters, including different vertical height and refers to all oyster structures as oyster reefs including patch and string, clumps of oysters and those attached on mangrove.

The most basic need for oyster reef conservation is suitably scaled maps with sufficient information to identify ecological change. The orthomosaics has a spatial resolution of 3. The high level of detail in the generated map Figure 2 clearly shows some important features such as the extent of mangroves and spatial distribution of oyster reefs and provides useful data for mapping and monitoring intertidal biogenic habitats.

Indeed, in addition to the dominating features e. Since all the images captured were in the late afternoon, shadows from mangroves were reflected in the northeast direction and had no impact on oyster reefs located in the northwest section. Figure 2 High-resolution false-color orthomosaic 3. Key features from the land cover are clearly distinguishable from the imagery e.

The segmentation of the orthomosaic is a fundamental step during thematic map production through OBIA since the characteristics of many objects can be used in the classification process. In ENVI, segmentation separates objects into real-world features, and the results from segmentation determine the geometry of objects Figure 3. Another benefit of using this classification technique is that the final classification results and their associated geometry can be exported as vector shapefiles, which can be subsequently used for other statistical analyses Figure 4.

Figure 3 Segmentation results, showing features have been successfully segmented into different real-world e. Figure 4 The result of OBIA classification exported as vector shape files with their geometric properties.

However, despite these limiting factors, satisfactory classification accuracy was achieved following OBIA feature extraction and rule-based classification workflow. A classified map Figure 5 delineating oysters, demonstrated a good match between the classified land cover and the original RPAS imagery leading to an Overall Accuracy of This means that even though The reason for this reliability was the contributionof the high-resolution imagery 3.

Figure 5 A high-quality thematic map generated after segmentation and rule-based classification, classified into three main land cover classes. Table 2 Classified imagery accuracy assessment and absolute accuracy of imagery. Visual analysis was conducted to examine the potential of RPAS imagery to fill the gap in spatial resolution for remote sensing of oyster reefs.

The RPAS imagery has a 3. We interpreted the result using visual cues e. At a map scale of it was difficult to manually digitize and located oysters from the satellite imagery and at the image became pixelated Figure 6A , whereas from the RPAS imagery oysters were evident at a scale of Figure 6B.

Figure 6 A, Satellite imagery zoomed to a map scale of 1: 28 ; B, RPAS imagery zoomed to a map scale of ; For visual analysis of remotely sensed oyster reefs. Oysters are fundamental ecosystem engineers, where they also function as nutrient recyclers to filter sediments from water, increasing water clarity to thrive other biogenic habitats e.

Our result from RPAS derived imagery produced high-resolution orthomosaic with a ground sampling distance of 3. High spatial and temporal resolution using low altitude remote sensing with multispectral RPAS has the benefit of being able to detect changes preceding ecological collapse e.

However, a subsequent increase in the population of predatory oyster borer snails has reduced the population of both species of oysters. A lower flying altitude would improve the detail provided for directing mitigation measures or eradication of the predators.

Our results validated that RPAS platforms as a useful tool for the identification of intertidal marine biogenic habitats. In this study, a high resolution 3. In temperate marine environments, turbidity and SSC usually make remote sensing of marine habitats difficult. Although data capture was after the solar noon, the flights were planned so that shadows from vegetation had no impact on the targeted feature, oyster reefs.

To maintain biodiversity, pivotal habitats such as oyster reefs that provide quality breeding and feeding grounds for many fish and bird species need to be protected.

The results from segmentation of different features, such as oysters and mangroves, can be exported as vector shapefiles and used for other geo-analytics. RedEdge and NIR for enhanced target discrimination. This can be explored in future studies to classify oyster reefs into more categories including: shell density, mean size, alive versus dead because dead oysters are more reflective , reef complexity and oyster species.

As anthropogenic impacts including climate change continue to exert pressure on marine biogenic habitats, innovative methods for deriving useful information from multiple remote sensing imagery will be increasingly useful as a tool to monitor this change.

The technologies we have deployed in this study will be able to provide a critical source of information to marine managers for conservation and planning. RPAS remote sensing enables the conduct of surveying on-demand during low tides and over a broad spatial scale.

Flying low enables the capture of high-resolution imagery ranging from 50 to 4 mm. Despite limitations for classifying features of similar spectral reflectance OBIA for segmentation with a rule-based classification was sufficient for object identification and delineation of oyster reefs from other habitats.

Our study showed that the deployment of RPAS coupled with a multispectral sensor for mapping and identification of oyster reefs in a heterogeneous marine environment greatly increased the classification accuracy. Overall accuracy of Our method enables long-term monitoring of marine environments at lower cost to ground-based methods, at higher accuracy to other remote sensing methods and is therefore a valuable tool for conservation and restoration management.

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