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- Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime AntarcticaPublication . Pina, Pedro; Vieira, Goncalo; Bandeira, Lourenço; Mora, CarlaThe ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The microscale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling.
- Evaluation of the use of very high resolution aerial imagery for accurate ice-wedge polygon mapping (Adventdalen, Svalbard)Publication . Lousada, Maura; Pina, Pedro; Vieira, Gonçalo; Bandeira, Lourenço; Mora, CarlaThe main objective of this paper is to verify the accuracy of delineating and characterizing ice-wedge polygonal networks with features exclusively extracted from remotely sensed images of very high resolution. This kind of mapping plays a key role for quantifying ice-wedge degradation in warming permafrost. The evaluation of mapping a network is performed in this study with two sets of aerial images that are compared to ground reference data determined by fieldwork on the same network, located in Adventdalen, Svalbard (78°N). One aerial dataset is obtained from a photogrammetric survey with RGB+NIR imagery of 20cm/pixel, the other from an UAV (Unmanned Aerial Vehicle) survey that acquired RGB images of 6cm/pixel of spatial resolution. Besides evaluating the degree of matching between the delineations, the morphometric and topological features computed for the differently mapped versions of the network are also confronted, to have a more solid basis of comparison. The results obtained are similar enough to admit that remotely sensed images of very high resolution are an adequate support to provide extensive characterizations and classifications of this kind of patterned ground.
- A trained Mask R-CNN model over PlanetScope imagery for very-high resolution surface water mapping in boreal forest-tundraPublication . Freitas, Pedro; Vieira, Gonçalo; Canário, João; Vincent, Warwick F.; Pina, Pedro; Mora, CarlaSmall water bodies (< 0.01 km2) showing diverse limnological properties occur in great abundance across the boreal forest and tundra landscapes of the Arctic and Subarctic. However, their classification, geographical distribution and collective importance for water, heat, nutrient, contaminant and carbon cycles are still poorly constrained. One important step for better understanding the role and evolution of small water bodies in the fast-changing northern landscapes is to develop image analysis protocols that allow their automatic remote sensing detection, delineation and inventory. In this study, we set an image analysis protocol (High Latitude Water – HLWATER V1.0) based on a trained supervised Mask R-CNN deep learning model over PlanetScope imagery for the automatic detection and delineation of small lakes and ponds that were absent in existing datasets. Most of our training dataset comprised water bodies smaller than 0.01 km2 (97%) and spanned a wide range of environmental and hydrological settings, from the sporadic to the continuous permafrost zones of Canada. The model was tested as a fully autonomous approach for eastern Hudson Bay, Nunavik (Subarctic Canada), a region that poses challenges for water remote sensing given the abundance and variety of small water bodies. These are mainly permafrost thaw and glacial basin ponds in the boreal forest-tundra in challenging optical settings influenced by vegetation or topography shadowing, or revealing peat water logging, fen and bog pond conditions. A multi-scale validation approach was developed using water body delineations from PlanetScope imagery and ultra-high resolution orthomosaics from Unoccupied Aerial Systems. This procedure allowed a sub-pixel assessment and identified the limitations and strengths of the trained model for detecting small and large water bodies. The results varied according to different landscape units, with mean Intersection over Union (IoU) 0.5 F1 Scores of 0.53 to 0.71 and mean F1 Scores of 0.62 to 0.95. Considering 166 m2 as the minimum pond size detection threshold, the IoU 0.5 F1 Scores were 0.7 to 0.91 and F1 Scores were 0.76 to 0.83, evaluated by comparing the model results with ultra-high resolution manual delineations. The image analysis protocol and trained model show high potential for extension to other boreal forest-tundra regions of the Arctic and Subarctic, allowing for detailed inventories of optically and morphologically diverse small water bodies over large areas of the circumpolar North.
- UAV-based very high resolution point cloud, digital surface model and orthomosaic of the Chã das Caldeiras lava fields (Fogo, Cabo Verde)Publication . Vieira, Gonçalo; Mora, Carla; Pina, Pedro; Ramalho, Ricardo; Fernandes, RuiFogo in the Cabo Verde archipelago off western Africa is one of the most prominent and active ocean island volcanoes on Earth, posing an important hazard both to local populations and at a regional level. The last eruption took place between 23 November 2014 and 8 February 2015 in the Chã das Caldeiras area at an elevation close to 1800 ma.s.l. The eruptive episode gave origin to extensive lava flows that almost fully destroyed the settlements of Bangaeira, Portela and Ilhéu de Losna. During December 2016 a survey of the Chã das Caldeiras area was conducted using a fixed-wing unmanned aerial vehicle (UAV) and real-time kinematic (RTK) global navigation satellite system (GNSS), with the objective of improving the terrain models and visible imagery derived from satellite platforms, from metric to decimetric resolution and accuracy. The main result is a very high resolution and quality 3D point cloud with a root mean square error of 0.08 m in X, 0.11 m in Y and 0.12 m in Z, which fully covers the most recent lava flows. The survey comprises an area of 23.9 km2 and used 2909 calibrated images with an average ground sampling distance of 7.2 cm. The dense point cloud, digital surface models and orthomosaics with 25 and 10 cm resolutions, a 50 cm spaced elevation contour shapefile, and a 3D texture mesh, as well as the full aerial survey dataset are provided. The delineation of the 2014/15 lava flows covers an area of 4.53 km2, which is smaller but more accurate than the previous estimates from 4.8 to 4.97 km2. The difference in the calculated area, when compared to previously reported values, is due to a more detailed mapping of the flow geometry and to the exclusion of the areas corresponding to kīpukas (outcrops surrounded by lava flows). Our study provides a very high resolution dataset of the areas affected by Fogo's latest eruption and is a case study supporting the advantageous use of UAV aerial photography surveys in disaster-prone areas. This dataset provides accurate baseline data for future eruptions, allowing for different applications in Earth system sciences, such as hydrology, ecology and spatial modelling, as well as to planning. The dataset is available for download at https://doi.org/10.5281/zenodo.4718520 (Vieira et al., 2021).
- Monitoring recent changes of vegetation in Fildes Peninsula (King George Island, Antarctica) through satellite imagery guided by UAV surveysPublication . Miranda, Vasco; Pina, Pedro; Heleno, Sandra; Vieira, Gonçalo; Mora, Carla; E.G.R. Schaefer, CarlosMapping accurately vegetation surfaces in space and time in the ice-free areas of Antarctica can provide important information to quantitatively describe the evolution of their ecosystems. Spaceborne remote sensing is the adequate way to map and evaluate multitemporal changes on the Antarctic vegetation at large but its nature of occurrence, in relatively small and sparse patches, makes the identification very challenging. The inclusion of an intermediate scale of observation between ground and satellite scales, provided by Unmanned Aerial Vehicles (UAV) imagery, is of great help not only for their effective classification, but also for discriminating their main communities (lichens and mosses). Thus, this paper quantifies accurately recent changes of the vegetated areas in Fildes Peninsula (King George Island, Antarctica) through a novel methodology based on the integration of multiplatform data (satellite and UAV). It consists of multiscale imagery (spatial resolution of 2 m and 2 cm) from the same period to create a robust classifier that, after intensive calibration, is adequately used in other dates, where field reference data is scarce or not available at all. The methodology is developed and tested with UAV and satellite data from 2017 showing overall accuracies of 96% and kappa equal to 0.94 with a SVM classifier. These high performances allow the extrapolation to a pair of previous dates, 2006 and 2013, when atmospherically clear very high-resolution satellite imagery are available. The classification allows verifying a loss of the total area of vegetation of 4.5% during the 11-year time period under analysis, which corresponds to a 10.3% reduction for Usnea sp. and 9.8% for moss formations. Nevertheless, the breakdown analysis by time period shows a distinct behaviour for each vegetation type which are evaluated and discussed, namely for Usnea sp. whose decline is likely to be related to changing snow conditions.
- A proxy for snow cover and winter ground surface cooling: mapping Usnea sp. communities using high resolution remote sensing imagery (Maritime Antarctica)Publication . Vieira, Goncalo; Mora, Carla; Pina, Pedro; Schaefer, Carlos E.R.Usnea sp. formations show a spatial distribution coinciding with wind-exposed locations on rock knobs or sedimentary bodies, while they are commonly absent from concave sites. Field collection of georeferenced ground truthing data in the Meseta Norte (Fildes Peninsula, Maritime Antarctica) and the application of supervised classification techniques over a summer high resolution QuickBird satellite scene showed excellent classification accuracy for the different landcover types. The results show that Usnea formation distribution maps are a viable proxy for areas with less snow during the cold season. Such an approach provides input for permafrost and active layer modelling since snow acts as a critical control on ground surface heat balance. Since snow mapping is extremely difficult in Maritime Antarctica our tested approach provides important added-value for empirical–statistical modelling of permafrost distribution.
- Evaluation of single-band snow-patch mapping using high-resolution microwave remote sensing: an application in the maritime AntarcticPublication . Mora, Carla; Jiménez Cuenca, Juan Javier; Pina, Pedro; Catalão, João; Vieira, GoncaloThe mountainous and ice-free terrains of the maritime Antarctic generate complex mosaics of snow patches, ranging from tens to hundreds of metres. These can only be accurately mapped using high-resolution remote sensing. In this paper we evaluate the application of radar scenes from TerraSAR-X in High Resolution SpotLight mode for mapping snow patches at a test area on Fildes Peninsula (King George Island, South Shetlands). Snow-patch mapping and characterization of snow stratigraphy were conducted at the time of image acquisition on 12 and 13 January 2012. Snow was wet in all studied snow patches, with coarse-grain and rounded crystals showing advanced melting and with frequent ice layers in the snow pack. Two TerraSAR-X scenes in HH and VV polarization modes were analysed, with the former showing the best results when discriminating between wet snow, lake water and bare soil. However, significant overlap in the backscattering signal was found. Average wet-snow backscattering was −18.0 dB in HH mode, with water showing −21.1 dB and bare soil showing −11.9 dB. Single-band pixel-based and object-oriented image classification methods were used to assess the classification potential of TerraSAR-X SpotLight imagery. The best results were obtained with an object-oriented approach using a watershed segmentation with a support vector machine (SVM) classifier, with an overall accuracy of 92 % and Kappa of 0.88. The main limitation was the west to north-west facing snow patches, which showed significant error, an issue related to artefacts from the geometry of satellite imagery acquisition. The results show that TerraSAR-X in SpotLight mode provides high-quality imagery for mapping wet snow and snowmelt in the maritime Antarctic. The classification procedure that we propose is a simple method and a first step to an implementation in operational mode if a good digital elevation model is available.
- Land cover classification using high‐resolution aerial photography in Adventdalen, SvalbardPublication . Mora, Carla; Vieira, Goncalo; Pina, Pedro; Lousada, Maura; Christiansen, Hanne H.A methodology was tested for high‐resolution mapping of vegetation and detailed geoecological patterns in the Arctic Tundra, based on aerial imagery from an unmanned aerial vehicle (visible wavelength – RGB, 6 cm pixel resolution) and from an aircraft (visible and near infrared, 20 cm pixel resolution). The scenes were fused at 10 and 20 cm to evaluate their applicability for vegetation mapping in an alluvial fan in dventdalen, Svalbard. Ground‐truthing was used to create training and accuracy evaluation sets. Supervised classification tests were conducted with different band sets, including the original and derived ones, such as and principal component analysis bands. The fusion of all original bands at 10 cm resolution provided the best accuracies. The best classifier was systematically the maximum neighbourhood algorithm, with overall accuracies up to 84%. Mapped vegetation patterns reflect geoecological conditioning factors. The main limitation in the classification was differentiating between the classes graminea, moss and Salix, and moss, graminea and Salix, which showed spectral signature mixing. Silty‐clay surfaces are probably overestimated in the south part of the study area due to microscale shadowing effects. The results distinguished vegetation zones according to a general gradient of ecological limiting factors and show that + high‐resolution imagery are excellent tools for identifying the main vegetation groups within the lowland fan study site of dventdalen, but do not allow for detailed discrimination between species.