High-resolution Airborne Imagery Research Articles

Mapping oak wilt disease from space using land surface phenology

Protecting the future of forests relies on our ability to observe changes in forest health. Thus, developing tools for sensing diseases in a timely fashion is critical for managing threats at broad scales. Oak wilt —a disease caused by a pathogenic fungus (Bretziella fagacearum)— is threatening oaks, killing thousands yearly while negatively impacting the ecosystem services they provide. Here we propose a novel workflow for mapping oak wilt by targeting temporal disease progression through symptoms using land surface phenology (LSP) from spaceborne observations. By doing so, we hypothesize that phenological changes in pigments and photosynthetic activity of trees affected by oak wilt can be tracked using LSP metrics derived from the Chlorophyll/Carotenoid Index (CCI). We used dense time-series observations from Sentinel-2 to create Analysis Ready Data across Minnesota and Wisconsin and to derive three LSP metrics: the value of CCI at the start and end of the growing season, and the coefficient of variation of the CCI during the growing season. We integrate high-resolution airborne imagery in multiple locations to select pixels (n = 3872) from the most common oak tree health conditions: healthy, symptomatic for oak wilt, and dead. These pixels were used to train an iterative Partial Least Square Discriminant (PLSD) model and derive the probability of an oak tree (i.e., pixel) in one of these conditions and the associated uncertainty. We assessed these models spatially and temporally on testing datasets revealing that it is feasible to discriminate among the three health conditions with overall accuracy between 80 and 82%. Within conditions, our models suggest that spatial variations among three CCI-derived LSP metrics can identify healthy (Area Under the Curve (AUC) = 0.98), symptomatic (AUC = 0.89), and dead (AUC = 0.94) oak trees with low false positive rates. The model performance was robust across different years as well. The predictive maps were used to guide local stakeholders to locate disease hotspots for ground verification and subsequent decision-making for treatment. Our results highlight the capabilities of LSP metrics from dense spaceborne observations to map diseases and to monitor large-scale change in biodiversity.

The Education and Research 3D Radiative Transfer Toolbox (EaR3T) – towards the mitigation of 3D bias in airborne and spaceborne passive imagery cloud retrievals

Abstract. We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T, pronounced []) for quantifying and mitigating artifacts in atmospheric radiation science algorithms due to spatially inhomogeneous clouds and surfaces and show the benefits of automated, realistic radiance and irradiance generation along extended satellite orbits, flight tracks from entire aircraft field missions, and synthetic data generation from model data. EaR3T is a modularized Python package that provides high-level interfaces to automate the process of 3D radiative transfer (3D-RT) calculations. After introducing the package, we present initial findings from four applications, which are intended as blueprints to future in-depth scientific studies. The first two applications use EaR3T as a satellite radiance simulator for the NASA Orbiting Carbon Observatory 2 (OCO-2) and Moderate Resolution Imaging Spectroradiometer (MODIS) missions, which generate synthetic satellite observations with 3D-RT on the basis of cloud field properties from imagery-based retrievals and other input data. In the case of inhomogeneous cloud fields, we show that the synthetic radiances are often inconsistent with the original radiance measurements. This lack of radiance consistency points to biases in heritage imagery cloud retrievals due to sub-pixel resolution clouds and 3D-RT effects. They come to light because the simulator's 3D-RT engine replicates processes in nature that conventional 1D-RT retrievals do not capture. We argue that 3D radiance consistency (closure) can serve as a metric for assessing the performance of a cloud retrieval in presence of spatial cloud inhomogeneity even with limited independent validation data. The other two applications show how airborne measured irradiance data can be used to independently validate imagery-derived cloud products via radiative closure in irradiance. This is accomplished by simulating downwelling irradiance from geostationary cloud retrievals of Advanced Himawari Imager (AHI) along all the below-cloud aircraft flight tracks of the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex, NASA 2019) and comparing the irradiances with the colocated airborne measurements. In contrast to case studies in the past, EaR3T facilitates the use of observations from entire field campaigns for the statistical validation of satellite-derived irradiance. From the CAMP2Ex mission, we find a low bias of 10 % in the satellite-derived cloud transmittance, which we are able to attribute to a combination of the coarse resolution of the geostationary imager and 3D-RT biases. Finally, we apply a recently developed context-aware Convolutional Neural Network (CNN) cloud retrieval framework to high-resolution airborne imagery from CAMP2Ex and show that the retrieved cloud optical thickness fields lead to better 3D radiance consistency than the heritage independent pixel algorithm, opening the door to future mitigation of 3D-RT cloud retrieval biases.

Automatic Detection of Specific Constructions on a Large Scale Using Deep Learning in Very High Resolution Airborne Imagery

In the High Modernism period, from around 1914 to 1970, many system halls in steel construction were manufactured to meet the increasing demand in industry, commerce, and agriculture, among other areas. However, these types of buildings have not been the focus of any research in the field of construction history, generating a lack of knowledge regarding their construction types, distribution, and related context to enable statements on the ability and worthiness of historical monument listings. This paper proposes a methodology for the automatic detection of these buildings using aerial imagery. For this purpose, Deep Learning techniques for two tasks are evaluated: semantic segmentation and object detection. Different state-of-the-art software architectures are extensively reviewed and assessed through a series of experiments to determine which features and hyper-parameters produce the best results. Based on our experiments, the height information from nDSM improved the results by refining the detections and reducing the number of false negatives and false positives. Moreover, the Focal Loss helped boost the detections by tuning its hyper-parameter gamma, where object detection algorithms showed high sensitivity to this value. Semantic segmentation models outperformed their counterparts for object detection, with U-Net and EfficientNet B3 as the backbone, the one with the best results with a Detection Rate of up to 93%.

Cooling Effects of Urban Vegetation: The Role of Golf Courses

Increased heat in urban environments, from the combined effects of climate change and land use/land cover change, is one of the most severe problems confronting cities and urban residents worldwide, and requires urgent resolution. While large urban green spaces such as parks and nature reserves are widely recognized for their benefits in mitigating urban heat islands (UHIs), the benefit of urban golf courses is less established. This is the first study to combine remote sensing of golf courses with Morphological Spatial Pattern Analysis (MSPA) of vegetation cover. Using ArborCamTM multispectral, high-resolution airborne imagery (0.3 × 0.3 m), this study develops an approach that assesses the role of golf courses in reducing urban land surface temperature (LST) relative to other urban land-uses in Perth, Australia, and identifies factors that influence cooling. The study revealed that urban golf courses had the second lowest LST (around 31 °C) after conservation land (30 °C), compared to industrial, residential, and main road land uses, which ranged from 35 to 37 °C. They thus have a strong capacity for summer urban heat mitigation. Within the golf courses, distance to water bodies and vegetation structure are important factors contributing to cooling effects. Green spaces comprising tall trees (>10 m) and large vegetation patches have strong effects in reducing LST. This suggests that increasing the proportion of large trees, and increasing vegetation connectivity within golf courses and with other local green spaces, can decrease urban LST, thus providing benefits for urban residents. Moreover, as golf courses are useful for biodiversity conservation, planning for new golf course development should embrace the retention of native vegetation and linkages to conservation corridors.

A general deep learning model for bird detection in high-resolution airborne imagery.

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it requires large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increases these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

Mapping Pervious Surfaces and Canopy Cover Using High-Resolution Airborne Imagery and Digital Elevation Models to Support Urban Planning

Urban green infrastructure (UGI) has a key role in improving human and environmental health in cities and contributes to several services related to climate adaptation. Accurate localization and quantification of pervious surfaces and canopy cover are envisaged to implement UGI, address sustainable spatial planning, and include adaptation and mitigation strategies in urban planning practices. This study aims to propose a simple and replicable process to map pervious surfaces and canopy cover and to investigate the reliability and the potential planning uses of UGI maps. The proposed method combines the normalized difference vegetation index (NDVI), extracted from high-resolution airborne imagery (0.20 m), with digital elevation models to map pervious surfaces and canopy cover. The approach is tested in the Municipality of Trento, Italy, and, according to a random sampling validation, has an accuracy exceeding 80%. The paper provides a detailed map of green spaces in the urban areas, describing quantity and distribution, and proposes a synthesis map expressed as a block-level degree of pervious surfaces and canopy cover to drive urban transformations. The proposed approach constitutes a useful tool to geovisualize critical areas and to compare levels of pervious surfaces and canopy cover in the municipal area. Acknowledging the role of green areas in the urban environment, the paper examines the potential applications of the maps in the policy cycle, such as land use management and monitoring, and in climate-related practices, and discusses their integration into the current planning tools to shift towards performative rather than prescriptive planning.

Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method).

A Preliminary Assessment of a Newly-Defined Multispectral Hue Space for Retrieving River Depth with Optical Imagery and In Situ Calibration Data

Bathymetry is a key element in the modeling of river systems for flood mapping, geomorphology, or stream habitat characterization. Standard practices rely on the interpolation of in situ depth measurements obtained with differential GPS or total station surveys, while more advanced techniques involve bathymetric LiDAR or acoustic soundings. However, these high-resolution active techniques are not so easily applied over large areas. Alternative methods using passive optical imagery present an interesting trade-off: they rely on the fact that wavelengths composing solar radiation are not attenuated at the same rates in water. Under certain assumptions, the logarithm of the ratio of radiances in two spectral bands is linearly correlated with depth. In this study, we go beyond these ratio methods in defining a multispectral hue that retains all spectral information. Given n coregistered bands, this spectral invariant lies on the (n−2)-sphere embedded in Rn−1, denoted Sn−2 and tagged ‘hue hypersphere’. It can be seen as a generalization of the RGB ‘color wheel’ (S1) in higher dimensions. We use this mapping to identify a hue-depth relation in a 35 km reach of the Garonne River, using high resolution (0.50 m) airborne imagery in four bands and data from 120 surveyed cross-sections. The distribution of multispectral hue over river pixels is modeled as a mixture of two components: one component represents the distribution of substrate hue, while the other represents the distribution of ‘deep water’ hue; parameters are fitted such that membership probability for the ‘deep’ component correlates with depth.

Mapping Impervious Surfaces Precisely—a GIS-Based Methodology Combining Vector Data and High-Resolution Airborne Imagery

Impervious surfaces are fragments of the earth where the natural soil function has been altered considerably, for instance, regarding the infiltration of water. The methodology proposed in this study combines vector datasets and aerial imagery to map impervious areas precisely with a spatial resolution of 10 cm and focuses on the practicality and simplicity of the approach. The study is carried out in the German city of Mannheim. Vegetation is classified by NDVI calculation from airborne imagery, while vector data, based on municipal cadastres and OpenStreetMap, is used to superimpose impervious surfaces, such as buildings and traffic areas. Only data that is usually available and free of charge to municipal administrations is used to ensure the method’s repeatability. This data combination is proposed to benefit from the individual advantages of each data source. The uncertainties that arise are discussed, emphasizing areas that are particularly challenging to analyse. According to an extensive validation, the achieved results of the proposed method are valid with a correctness of 93%. Therefore, the proposed methodology can be suggested for statistical comparison purposes as a standardized practise.

Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R2 = 0.81, RMSE = 1.09 kg m−2) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R2 = 0.74, RMSE = 1.26 kg m−2), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts. m−2) to the G-LiHT SfM point cloud (500–2000 pts. m−2) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery

Automatic car extraction (ACE) from high-resolution airborne imagery (i.e., true-orthophoto) has been a hot research topic in the field of photogrammetry and machine learning. ACE from high-resolution airborne imagery is the most suitable method for control and monitoring practices in large cities such as traffic management. The use of deep learning–based feature extraction methods, such as convolutional neural networks, have been providing state-of-the-art performance in the last few years, particularly, these techniques have been successfully applied to automatic object extraction from images. In this paper, we proposed a novel hybrid method to take advantage of the semantic segmentation of high-resolution airborne imagery to ACE that is realized based on the combination of deep convolutional neural networks and restricted Boltzmann machine (RBM). This hybrid method is called RBMDeepNet. We trained and tested our model on the ISPRS Potsdam and Vaihingen benchmark datasets (non-big data) which is more challenging for ACE. Here, Potsdam data is a true-color dataset, and Vaihingen data is a false-color dataset. The results obtained in the present study showed that the proposed method for ACE from high-resolution airborne imagery achieves a 7% improvement in accuracy with about 10% improvement in processing time compared to similar methods.

Fuzzy ontology-based foreshore identification from digital terrain models and very high-resolution airborne imagery through GEOBIA multi-scale analysis

ABSTRACTCoastal mapping provides information for coastal zone protection. Land foreshore-border mapping is important, since it is related to land and property management, raising the need for an automated foreshore delineation framework. This research develops a multi-scale GEographic Object-Based Image Analysis method for foreshore identification from single-date very high-resolution imagery and DTM. Foreshore interpretation criteria were provided by the Greek Cadastral Office; however, they defined the foreshore-borders in an ill-defined manner. Thus, an ontology was designed and implemented to formalize the implicit spectral, geometric, and spatial relationships described in the interpretation criteria, and employ them during identification. The final ontology was delivered after three design phases: specification, conceptualization, and knowledge formalization. Imagery preprocessing through anisotropic morphological levelings reduced undesired spatial detail and noise. A three-level segmentation and ontological classification approach was developed to compute proper objects and assign them into semantic categories. Class definitions were created through fuzzy restrictions, representing value ranges of the employed properties. Evaluation of the extracted foreshore area with human-interpreted reference data showed a 77.9% quality, which was satisfactory. Evaluation by comparing the extracted foreshore border to the reference data showed an average deviation of 2.4 m, which is within Greek Cadastral Office specifications.

Sharpening land use maps and predicting the trends of land use change using high resolution airborne image: A geostatistical approach

High quality land use/land cover (LULC) data with fine spatial resolution and frequent temporal coverage are indispensable for revealing detail information of the Earth’s surface, characterizing LULC of the area, predicting the plausible land use changes, and assessing the viability and impacts of any development plans. While airborne imagery has high spatial resolution, it only provides limited temporal coverage over time. The LULC data from historical remote sensing images, such as those from Landsat, have frequent coverages over a long temporal period, but their spatial resolutions are low.This paper presents a spatio-temporal Cokriging method to sharpen LULC data and predict the trends of land use change. A set of time-series coarse resolution LULC maps and one frame of high spatial resolution airborne imagery of the Upper Mill Creek Watershed were used to illustrate the utility of our method. By explicitly describing the spatio-temporal dependence within and between different datasets, modelling the Anderson classification codes using spatial, temporal, and cross-covariance structures, and transforming the Anderson integer classification code to class probability, our method was able to resolve the differences between multi-source spatio-temporal LULC data, generate maps with sharpened and detailed land features, characterize the spatial and temporal LULC changes, reveal the trend of LULC change, and create a quality dataset invaluable for monitoring, assessing, and modelling LULC changes.

Lead Detection in Polar Oceans—A Comparison of Different Classification Methods for Cryosat-2 SAR Data

In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.

COHERENT CHANGE DETECTION FOR REPEATED-PASS INTERFEROMETRIC SAR IMAGES: AN APPLICATION TO EARTHQUAKE DAMAGE ASSESSMENT ON BUILDINGS

Abstract. During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Ametrine and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service – European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts.

Characterizing Forest Succession Stages for Wildlife Habitat Assessment Using Multispectral Airborne Imagery

In this study, we demonstrate the potential of using high spatial resolution airborne imagery to characterize the structural development stages of forest canopies. Four forest succession stages were adopted: stand initiation, young multistory, understory reinitiation, and old growth. Remote sensing metrics describing the spatial patterns of forest structures were derived and a Random Forest learning algorithm was used to classify forest succession stages. These metrics included texture variables from Gray Level Co-occurrence Measures (GLCM), range and sill from the semi-variogram, and the fraction of shadow and its spatial distribution. Among all the derived variables, shadow fractions and the GLCM variables of contrast, mean, and dissimilarity were the most important for characterizing the forest succession stages (classification accuracy of 89%). In addition, a LiDAR (Light Detection and Ranging) derived forest structural index (predicted Lorey’s height) was employed to validate the classification result. The classification using imagery spatial variables was shown to be consistent with the LiDAR derived variable (R2 = 0.68 and Root Mean Square Error (RMSE) = 2.39). This study demonstrates that high spatial resolution imagery was able to characterize forest succession stages with promising accuracy and may be considered an alternative to LiDAR data for this kind of application. Also, the results of stand development stages build a framework for future wildlife habitat mapping.

METRIC EVALUATION PIPELINE FOR 3D MODELING OF URBAN SCENES

Abstract. Publicly available benchmark data and metric evaluation approaches have been instrumental in enabling research to advance state of the art methods for remote sensing applications in urban 3D modeling. Most publicly available benchmark datasets have consisted of high resolution airborne imagery and lidar suitable for 3D modeling on a relatively modest scale. To enable research in larger scale 3D mapping, we have recently released a public benchmark dataset with multi-view commercial satellite imagery and metrics to compare 3D point clouds with lidar ground truth. We now define a more complete metric evaluation pipeline developed as publicly available open source software to assess semantically labeled 3D models of complex urban scenes derived from multi-view commercial satellite imagery. Evaluation metrics in our pipeline include horizontal and vertical accuracy and completeness, volumetric completeness and correctness, perceptual quality, and model simplicity. Sources of ground truth include airborne lidar and overhead imagery, and we demonstrate a semi-automated process for producing accurate ground truth shape files to characterize building footprints. We validate our current metric evaluation pipeline using 3D models produced using open source multi-view stereo methods. Data and software is made publicly available to enable further research and planned benchmarking activities.

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.

Riverscapes downstream of hydropower dams: Effects of altered flows and historical land-use change

Dams strongly impair the fluvial environment by altering downstream flows. We analysed riverscapes downstream of three dams and hypothesized that different dam types in rivers with diverse history of land-use and land cover (LULC) change have significant riparian cover differences at diverse biogeomorphic units (banks, riverbanks, islands). We performed a temporal comparison using pre-dam (1965) and post-dam (2013) high-resolution airborne imagery. A new approach was devised to correct the spatial offset between historical and contemporary imagery. Riparian vegetation and LULC (200m-buffer) were mapped in three rivers of Portugal regulated by the dams Touvedo (run-of-river), Vilarinho das Furnas and Fronhas (storage reservoirs). Five landscape metrics, measures of shape complexity, area and edge effect of riparian patches were computed, including the Weighted Class Area, a metric developed to better interpret the landscape variation. Our findings provide support for the hypothesis of highly altered riverscapes in the post-dam period. For all case studies riparian patches are presently larger, but with less complex shapes and smaller edges. In the present study riparian patches encroach into the river channel, occupy more area and are larger at the two reservoir rivers than at the run-of-river setting. Riparian growth trajectory at the latter is mainly outwards from the active channel and non-vegetated areas in riverbanks and banks are significantly larger; likely due to the washing flows. Redundancy analyses indicated that riparian change was driven by both LULC (agricultural land abandonment and unmanaged forests) and hydrological alterations that jointly determine the structure and spatial trajectories of riparian expansion.

Using remote sensing to understand Pinot noir vineyard variability in Ontario

The study objective was to determine whether multispectral high spatial resolution airborne imagery could be used to segregate zones in Pinot noir vineyards to target highest quality fruit for premium winemaking. We hypothesized that remotely sensed data would be correlated with vine size and leaf water potential (ψ), and by extension with yield and berry composition. In 2008–2009, multispectral (blue, green, red, near-infrared) airborne images were acquired from four Ontario Pinot noir vineyards (four dates, 2008; three, 2009), with the final flight date near veraison. A process was developed to extract information from sentinel vine locations, and to calculate normalized difference vegetation index (NDVI). Data were extracted at 1 × 1, 3 × 3, and 5 × 5 pixel re-sampling rates to test for ideal image resolution. A method was developed to mask non-vine pixels to simplify qualitative assessment of images. The 3 × 3 pixel re-sampling provided most useful information. In 2008, 3 × 3 re-sampling NDVI correlated with (r-values; p < 0.0001): berry pH (−0.48), soluble solids (−0.43), vine size (0.46), anthocyanins (−0.65), colour (−0.58), and soil clay and sand content (−0.55, 0.55). In 2009, mean 3 × 3 re-sampling NDVI correlated with (r-values; p < 0.0001): anthocyanins (0.49), soil moisture (−0.89), and soil clay and silt content (−0.75, 0.83). No clear trends in correlations existed between vegetation indices vs. vine size, anthocyanins, phenolics, or soil moisture throughout the growing season in either vintage. Masked images proved effective for viewing spatial trends in airborne images without full data extraction. Qualitative similarities existed between maps of vineyard and grape composition variables vs. maps of extracted data and masked images. Remote sensing may be useful to determine colour or phenolic potential of grapes, in addition to vine water status, yield, and vine size. This study was unique by employing remote sensing in cover-cropped vineyards and thereafter using protocols for excluding spectral reflectance contributed by inter-row vegetation.