High-resolution Digital Imagery Research Articles

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.

Effects of Micro-Scale Environmental Factors on the Quantity of Questing Black-Legged Ticks in Suburban New York

The questing behaviors of blacklegged ticks (Ixodes scapularis) are largely regulated by environmental factors such as temperature, humidity, and vegetation. While this relationship is relatively clear at the macro- and meso-spatial scales, it is inadequately examined at the micro scale. Our field work in the New York City suburbs during 2017–2018 revealed significant local variations in the quantity of questing blacklegged ticks. The purpose of this study is to identify and test the environmental factors that impact the number of questing blacklegged ticks at the micro-spatial scale. In addition to the number of ticks, surface temperature, and relative humidity data collected in the field, geospatial technologies were leveraged to extract micro-scale spatial and environmental measures, including vegetation index, land cover, elevation, and ecotone, from high-resolution digital imagery and LiDAR data. Regression models were then built to identify the key factors that influence the spatiotemporal patterns of questing blacklegged ticks. The results largely align with the existing research but display characteristics of complexity such as multicollinearity, nonlinearity, and thresholds in relation to temperature, humidity, and vegetation composition at the micro scale, whereas mixed hardwood and dwarf shrubs tend to have higher numbers of questing ticks.

Multiple datasets confirm range extension of the sicklefin devil ray Mobula tarapacana in the western North Atlantic Ocean off the eastern USA

Abstract The sicklefin devil ray (Mobula tarapacana) is a large, pelagic ray which is listed as Endangered by the International Union for Conservation of Nature (IUCN). Mobula tarapacana is thought to have a circumglobal, yet patchy distribution, and has not been verified extant off the eastern USA. Here, we report 180 sightings of M. tarapacana with a total of 361 individuals, collated across five datasets from aerial survey operations and incidental sightings in the waters off the US East Coast and Gulf of Mexico, between 1996 and 2022. This study extends the northern range of M. tarapacana in the Gulf of Mexico to 29°N, and in the Atlantic to 40°N. Seasonal trends were observed off the north-eastern coast of the USA, with M. tarapacana only present in the summer months. Measurements from high resolution digital aerial imagery found M. tarapacana off the New York coast to be adults and subadults with an average disc width of 268 cm (±25, range 232–316 cm). This study provides important spatial and temporal data for management, as well as informing areas for future research on M. tarapacana in the western Atlantic.

An open access geospatial database for the sub-Antarctic Prince Edward Islands

Researchers of projects at the sub-Antarctic Prince Edward Islands are increasingly considering geospatial data as an essential component in answering scientific questions. A need exists for high-resolution geospatial data in both multi- and transdisciplinary research to better analyse fine-scale biotic–abiotic interactions of the Islands’ landscape and ecosystems within the context of climate change and the impacts of invasive species. However, much of the geospatial data that currently exist have limitations in spatial coverage and/or resolution, are outdated, or are not readily available. To address these issues, we present an online geospatial database for the Prince Edward Islands (both islands) produced from a high-resolution digital surface model and satellite imagery. This database contains vector files, raster data sets, and maps of topographical and hydrological parameters. It is freely available to download from Figshare – an open access data repository. We encourage the South African polar science community to make use of similar platforms for improved data sharing practices. Significance: A topographical and hydrological geospatial database – produced from a 1 m x 1 m digital surface model of the Prince Edward Islands – is provided. These fine-scale geospatial data allow for a more comprehensive assessment of biotic–abiotic interactions at an island scale. Also included are locality maps specifying place names and established long-term marine mammal monitoring beaches and coastal zones for improved cross-referencing. The dataset is downloadable from an open access data repository and intended to promote open science and data sharing practices. Open data set: https://doi.org/10.6084/m9.figshare.19248626

Radial Casting Algorithm for Extraction of Man-Made Features from High Resolution Digital Satellite Imagery

The extraction of man-made features from high resolution digital satellite imagery is an important step to underpin management of geo-information in any country. Man-made features and buildings in particular are required for various applications such as urban planning, creation of geographic information systems databases and generation of urban models. Manual extraction processes are expensive, labor intensive, need well trained personnel and cannot cope with high demand of geo-information and changing environment. This paper, presents a Radial Casting Algorithm (RCA) used to extract buildings from high resolution digital satellite imagery. The algorithm measures only a single point on an approximate center of the building on an image and the fine measurement is automatically determined. The algorithm is a modification from original snakes model developed by Kass et al whereby the external constraints energy term is removed which negatively affects the convergence properties of the contour to provide the ability of the snake contour to cope with high variability of buildings on an image. The algorithm was tested on three areas of different environment. The quantitative measures were employed to evaluate the accuracy, efficiency and capability of the algorithm which shows that the time of extracting a single building was reduced by 32 percent, the extraction rate was 92 percent and the Area coverage of extracted polygons was 98 percent.

Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

地震災害の被害想定のための高解像度衛星画像・GPSビデオカメラを用いた 建物インベントリー推定手法の確立

地震災害の被害想定のための高解像度衛星画像・GPSビデオカメラを用いた 建物インベントリー推定手法の確立

A Novel Approach for Deriving LAI of Salt Marsh Vegetation Using Structure From Motion and Multiangular Spectra

The leaf area index (LAI) is a structural parameter that plays a major role in modeling the exchange of energy, carbon dioxide, water, and other matter between plants and their surrounding environment. Direct means of measuring LAI are time-intensive and laborious, making indirect LAI retrieval methods desirable. In this study, we developed a method for retrieving structural metrics of erectophile plants that relies on open-source structure from motion software. We achieved this using a novel goniometer system to simultaneously retrieve high-resolution digital imagery and bidirectional spectroscopy measurements of Spartina alterniflora in a laboratory environment during both ${{ peak}}$ and ${{ late}}$ growing season. Retrieved LAI metrics correlated well with directional measurements of the Modified Chlorophyll Absorption Ratio Index 2 (MCARI2). Across all planting densities, the magnitude of the MCARI2 exhibited a high degree of variance, which depended on sensor viewing angle. MCARI2 values in the peak growing season were significantly higher. This result suggests that the MCARI2 metric is sensitive to seasonal changes in plant growth and senescence. We observed a high correlation between the $\text{LAI}_{\text{contact}}$ values and the Total MCARI2 metric for the ${{ peak}}$ growth state plantings of $R^2 = 0.9$ , suggesting that summing multiple directional measurements of MCARI2 potentially provides a more accurate assessment of LAI and compensates for observed variance in MCARI2 as a function of sensor orientation. Future work will focus on validating the observed effects across a broader range of plant species.

Anatomy of a late Quaternary carbonate island: Constraints on timing and magnitude of sea-level fluctuations, West Caicos, Turks and Caicos Islands, BWI

The data set of the island of West Caicos consists of a combination of high-resolution lidar and digital imagery, radiometric data, and amino acid racemization (AAR) D/L values as well as extraordinary preservation of sedimentary and bio-constructed deposits on the island. Together with a well-established regional stratigraphic framework from the neighboring Bahamian island chain and detailed field mapping, the West Caicos data set provides an opportunity to advance our understanding of the link between carbonate stratigraphy and the controlling parameters of sea level, sediment supply, tectonics, and climate. The exceptionally well-preserved stratigraphic record of the mid–late Pleistocene from West Caicos allows recognition of marine isotope stages (MIS) 9/11, 5e, and Holocene. The west coast of the island provides a continuous 8.4 km exposure of the MIS 5e record that enhances our understanding of the intra-MIS 5e bipartite succession in this region and supports a sea-level lowering that occurred during the last interglacial (LIG).West Caicos accreted as a series of eolian, foreshore–shoreface, barrier-reef, and fringing-reef facies. Shoreline positions for all but the MIS 7 and MIS 5a/c deposits are now captured in the sub-meter geodetic-quality topobathy lidar survey. The Star Town unit of MIS 9/11 age forms the southern margin of the island as well as the central karst-modified spine, with uranium–thorium (UTh) coral ages of between 368 and 365 thousand years ago (ka) and a D/L Glu average of >0.60. Early MIS 5e strata include the Railroad Ridge unit in the center of the island, which was previously identified as one of the older units. The Railroad Ridge unit is a north–south linear dune ridge that has a D/L Glu average of 0.52 and indicates the onset of sediment sources from the east associated with a flooded central Caicos Platform. Approximately synchronously, the early MIS 5e South Reef unit (+3.75–4 m sea-level elevation) nucleated on the western coast of West Caicos, attached to the middle Pleistocene Star Town unit. The South Reef corals yield an average coral UTh age of 126.5 ka, with an average D/L Glu from skeletal matrix of 0.46. This transitional barrier/fringing reef is unconformably overlain by the late MIS 5e Boat Cove unit. Corals in the basal transgressive phase of this upper MIS 5e Boat Cove unit yield an average 120.6 ka UTh and average D/L Glu of 0.45. Shallow-marine, foreshore, and eolian facies of the late MIS 5e rest on a wave-cut terrace that cuts the early MIS 5e South Reef unit by as much as 3 m down to a +0.5 m above present sea-level elevation. The upper MIS 5e Boat Cove unit displays a maximum of +4.5 m elevation above present-day sea level, measured from the middle of the foreshore. This estimate may be a minimum value as the exposures on the west coast are part of a forced regressive foreshore sequence. Last stages of the upper MIS 5e deposition are seen in a series of regressive dune-beach ridge deposits referred to as the Northeast Ridges unit, with sea-level positions ranging from +4.5 m early in the central part of West Caicos, to +0.5 m on the far northeast coast, eventually building down below present-day sea level. This sea-level history fits well with previously documented records in the Bahamas and globally, and the extensive erosion supports a short-lived (between 126 and 120 ka) intra-MIS 5e relative-sea-level fall. Holocene deposition on West Caicos is restricted to northern and eastern margins of the island as a combination of strandplain and dune ridge mixed skeletal–oolitic units.Using the high-resolution digital elevation model (DEM) and linked digital geologic map makes it possible to compare areas of the different Pleistocene and Holocene units quantitatively and to constrain volumetric assessments and accumulation rates on the island for different units. The peak sediment volume production/accumulation rate—between 6.5 and 8 × 106 m3 or between 2.2 and 2.7 × 106 m3 per ka—is observed in the upper MIS 5e unit. This rate is 5 times greater than the 0.5 × 106 m3 per ka estimated for the Holocene ooid dune-ridge/strandplain complex, suggesting that the combination of higher sea level, greater wave and wind energy, and warmer ocean temperatures may have fostered the higher rate of chemically precipitated sediment supply.

Student Led Campus Desire Path Evaluation Using Pictometry® Neighborhood Imagery

A student led evaluation of desire paths (e.g., paths created by pedestrians on an open landscape) across the Stephen F. Austin State University (SFASU) campus was performed within a senior level spatial science course in order to create a method for mitigation of desire paths and for campus beautification. Each desire path on campus was identified with the length of each path measured in the field and categorized by the condition of the path in order to assess and determine a necessary solution for each path identified. In addition, Pictometry® high spatial resolution digital imagery was used to determine if the categorization of the conditions of the desire paths, as well as the length of each desire path, could be identified and quantified without the need to measure each individual desire path in the field. Students, within an interactive hands-on classroom environment, compared in-field desire path measurements with Pictometry® on-screen measurements to determine the effectiveness of remotely sensed Pictometry® imagery to identify and quantify desire path location and length respectfully.

An Approach for High-Resolution Mapping of Hawaiian Metrosideros Forest Mortality Using Laser-Guided Imaging Spectroscopy

Rapid ‘Ōhi‘a Death (ROD) is a disease aggressively killing large numbers of Metrosideros polymorpha (‘ōhi‘a), a native keystone tree species on Hawaii Island. This loss threatens to deeply alter the biological make-up of this unique island ecosystem. Spatially explicit information about the present and past advancement of the disease is essential for its containment; yet, currently such data are severely lacking. To this end, we used the Carnegie Airborne Observatory to collect Laser-Guided Imaging Spectroscopy data and high-resolution digital imagery across >500,000 ha of Hawaii Island in June–July 2017. We then developed a method to map individual tree crowns matching the symptoms of both active (brown; desiccated ‘ōhi‘a crowns) and past (leafless tree crowns) ROD infection using an ensemble of two distinct machine learning approaches. Employing a very conservative classification scheme for minimizing false-positives, model sensitivity rates were 86.9 and 82.5, and precision rates were 97.4 and 95.3 for browning and leafless crowns, respectively. Across the island of Hawaii, we found 43,134 individual crowns suspected of exhibiting the active (browning) stage of ROD infection. Hotspots of potential ROD infection are apparent in the maps. The peninsula on the eastern side of Hawaii known as the Puna district, where the ROD outbreak likely originated, contained a particularly high density of brown crown detections. In comparison, leafless crown detections were much more numerous (547,666 detected leafless crowns in total) and more dispersed across the island. Mapped hotspots of likely ROD incidence across the island will enable scientists, administrators, and land managers to better understand both where and how ROD spreads and how to apply limited resources to limiting this spread.

Patterns of mortality in a montane mixed-conifer forest in San Diego County, California.

We examine spatial patterns of conifer tree mortality and their changes over time for the montane mixed-conifer forests of San Diego County. These forest areas have recently experienced extensive tree mortality due to multiple factors. A spatial contextual image processing approach was utilized with high spatial resolution digital airborne imagery to map dead trees for the years 1997, 2000, 2002, and 2005 for three study areas: Palomar, Volcan, and Laguna mountains. Plot-based fieldwork was conducted to further assess mortality patterns. Mean mortality remained static from 1997 to 2002 (4, 2.2, and 4.2trees/ha for Palomar, Volcan, and Laguna) and then increased by 2005 to 10.3, 9.7, and 5.2trees/ha, respectively. The increase in mortality between 2002 and 2005 represents the temporal pattern of a discrete disturbance event, attributable to the 2002-2003 drought. Dead trees are significantly clustered for all dates, based on spatial cluster analysis, indicating that they form distinct groups, as opposed to spatially random single dead trees. Other tests indicate no directional shift or spread of mortality over time, but rather an increase in density. While general temporal and spatial mortality processes are uniform across all study areas, the plot-based species and quantity distribution of mortality, and diameter distributions of dead vs. living trees, vary by study area. The results of this study improve our understanding of stand- to landscape-level forest structure and dynamics, particularly by examining them from the multiple perspectives of field and remotely sensed data.

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Quantification and Analysis of Icebergs in a Tidewater Glacier Fjord Using an Object-Based Approach.

Tidewater glaciers are glaciers that terminate in, and calve icebergs into, the ocean. In addition to the influence that tidewater glaciers have on physical and chemical oceanography, floating icebergs serve as habitat for marine animals such as harbor seals (Phoca vitulina richardii). The availability and spatial distribution of glacier ice in the fjords is likely a key environmental variable that influences the abundance and distribution of selected marine mammals; however, the amount of ice and the fine-scale characteristics of ice in fjords have not been systematically quantified. Given the predicted changes in glacier habitat, there is a need for the development of methods that could be broadly applied to quantify changes in available ice habitat in tidewater glacier fjords. We present a case study to describe a novel method that uses object-based image analysis (OBIA) to classify floating glacier ice in a tidewater glacier fjord from high-resolution aerial digital imagery. Our objectives were to (i) develop workflows and rule sets to classify high spatial resolution airborne imagery of floating glacier ice; (ii) quantify the amount and fine-scale characteristics of floating glacier ice; (iii) and develop processes for automating the object-based analysis of floating glacier ice for large number of images from a representative survey day during June 2007 in Johns Hopkins Inlet (JHI), a tidewater glacier fjord in Glacier Bay National Park, southeastern Alaska. On 18 June 2007, JHI was comprised of brash ice ( = 45.2%, SD = 41.5%), water ( = 52.7%, SD = 42.3%), and icebergs ( = 2.1%, SD = 1.4%). Average iceberg size per scene was 5.7 m2 (SD = 2.6 m2). We estimate the total area (± uncertainty) of iceberg habitat in the fjord to be 455,400 ± 123,000 m2. The method works well for classifying icebergs across scenes (classification accuracy of 75.6%); the largest classification errors occur in areas with densely-packed ice, low contrast between neighboring ice cover, or dark or sediment-covered ice, where icebergs may be misclassified as brash ice about 20% of the time. OBIA is a powerful image classification tool, and the method we present could be adapted and applied to other ice habitats, such as sea ice, to assess changes in ice characteristics and availability.

Attribution of Greenland's ablating ice surfaces on ice sheet albedo using unmanned aerial systems

Surface albedo, a primary control on the amount of energy available for melt, has considerable spatial heterogeneity across the Greenland ice sheet ablation area. However, the relative importance of distinct surface types on albedo remains unclear. In this study, the causes of mesoscale (10<sup>2</sup> to 10<sup>3</sup> m) albedo variability are assessed using high resolution (decimetre-scale) digital imagery and broadband albedo data acquired by a fixed-wing unmanned aerial system. We characterize the reflectance properties and terrain roughness associated with six distinct surface types identified from a 25 km longitudinal transect across the ablating dark region of the Kangerlussuaq sector. Principal component analysis of the fractional area of each surface type versus coincident Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data reveals the relative importance of each surface type. The highest correlation with mesoscale albedo was the fractional area of distributed impurities. Although not the darkest surface type, their extensive coverage meant that they could explain 65 % of the albedo variability across the survey transect including the presence of the dark region. In contrast, the 2 % mean surface water coverage across our survey transect could only explain 12 % of albedo variation and crevasses, only 17 %. Localised cryoconite patches have the lowest albedo signature but comprise less than 1 % of the survey area and do not appear to reduce mesoscale albedo. We anticipate further reduction in ablation area albedo under future warming as localized areas of distributed impurities, supraglacial water and crevassing increase in extent and conclude that current bare ice area albedo models may advance significantly by representing the evolution of the surface types identified in this study.

Fish identification from videos captured in uncontrolled underwater environments

There is an urgent need for the development of sampling techniques which can provide accurate and precise count, size, and biomass data for fish. This information is essential to support the decision-making processes of fisheries and marine conservation managers and scientists. Digital video technology is rapidly improving, and it is now possible to record long periods of high resolution digital imagery cost effectively, making single or stereo-video systems one of the primary sampling tools. However, manual species identification, counting, and measuring of fish in stereo-video images is labour intensive and is the major disincentive against the uptake of this technology. Automating species identification using technologies developed by researchers in computer vision and machine learning would transform marine science. In this article, a new paradigm of image set classification is presented that can be used to achieve improved recognition rates for a number of fish species. State-of-the-art image set construction, modelling, and matching algorithms from computer vision literature are discussed with an analysis of their application for automatic fish species identification. It is demonstrated that these algorithms have the potential of solving the automatic fish species identification problem in underwater videos captured within unconstrained environments.

MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY

Abstract. The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

Palaeoenvironment and provenance in the early Cape Basin of southwest Gondwana: sedimentology of the Lower Ordovician Piekenierskloof Formation, Cape Supergroup, South Africa

The lowermost unit of the Table Mountain Group (Cape Supergroup), the Ordovician Piekenierskloof Formation is a siliciclastic succession that formed during the initial stages of the Cape Basin development in southwest Gondwana. This contemporary sedimentological re-evaluation of the depositional history and provenance of the Piekenierskloof Formation provides new insights into the palaeoenvironment and tectonic setting of the early Cape Basin. Outcrop-based sedimentary facies analysis revealed distinctive depositional features, dominated by large trough cross-bedding, which suggest a shallow, perennial, sand-bed braided fluvial style with bedload transportation in roughly 1 to 2.5 m deep and relatively wide channels. This fluvial style was possibly influenced by the global absence of substrate stabilizing terrestrial flora in pre-Devonian rivers. Based on an integrated approach of clast count, framework petrography and palaeocurrent studies, the provenance analysis show that the source rocks of the Piekenierskloof Formation were likely situated in source areas characterized by two main rock types: (1) a dominant low metamorphic rank metasedimentary rock suite with parent rocks of variable grain-size, and (2) minor plutonic rocks. The data also suggest that the parent rocks were locally intruded by quartz veins and/or pegmatites. Consistent palaeoflow to the southeast and east further indicates that these source areas were located to the northwest and west of the clastic sediment depocentres in the Cape Basin of southwest Gondwana in the Early Ordovician. Accessible outcrops of the Piekenierskloof Formation are mostly laterally limited and sparsely distributed, which hinder stratigraphic correlations, and therefore inferences of tectonic activity during the sedimentation in the Early Ordovician cannot be convincingly made. For a better understanding of the Early Palaeozoic evolution of the Cape Basin in southwest Gondwana, systematic outcrop studies acquiring high-resolution digital imagery using unmanned aerial vehicles (UAVs) and terrestrial laser scanning, coupled with subsurface facies analysis should be undertaken. The Piekenierskloof Formation and the rest of the Table Mountain Group, with their exceptionally extensive, but rather inaccessible cliff faces, are excellent candidates for such high-resolution digital-based studies.

Identifying High Value Arboreal Habitat in forested areas using high-resolution digital imagery

Old-growth forest is recognised as a high-value habitat in conservation assessment programs because of its importance to hollow-dependent species. Previous mapping undertaken at regional scales does not map patches of old forest smaller than 5 ha. While small patches of old forest may not be as ecologically important as large areas they provide opportunities for connectivity and specific habitat resources for arboreal wildlife within a broader landscape matrix. Previously, smaller patches of old forest have been overlooked because the tools have not been available to map at finer scales. This study incorporates a methodology using recent advances in technology, including aerial photography, to map old forest at a fine scale for the purposes of land-use assessment and planning. The term ‘High Value Arboreal Habitat’ is introduced to convey the ecological importance of hollow-bearing trees as part of a wider identification and mapping of high-value habitats across the landscape. The assessment was undertaken across the forested areas of the Coffs Harbour Local Government Area using high-resolution digital imagery. It is anticipated that the High Value Arboreal Habitat mapping process will be adopted by a range of stakeholders and natural resource managers to better manage and conserve these old forests across the landscape whatever their size.