Monitoring recent changes of vegetation in Fildes Peninsula (King George Island, Antarctica) through satellite imagery guided by UAV surveys
Graphical abstract
Introduction
The vegetation associated to the active layer and the underlying permafrost (Cannone et al., 2006, Oliva et al., 2018) is a key environmental component of the terrestrial ecosystems in the Antarctic Peninsula (Guglielmin et al., 2014) and in all ice-free areas of the Antarctic continent (Lee et al., 2017). The rapid warming in the Antarctic Peninsula in the second half of the 20th century (Turner et al., 2005) and the cooling, or at least the absence of warming, in the first decade and a half of the 21st century (Turner et al., 2016, Oliva et al., 2017) associated to permafrost warming (Vieira et al., 2010, Biskaborn et al., 2019) has had a direct impact on vegetation growth/decline and its spatial distribution (Amesbury et al., 2017, Sancho et al., 2017). The increasing expansion of ice-free areas till the end of the current century (Lee et al., 2017) will provide new habits for colonization by native organisms but also very likely for invasive species (Siegert et al., 2019). This expansion in area of the terrestrial regions will be accompanied by a decrease in their number due to the coalescing of isolated areas and, possibly, by the decrease of biodiversity (Lee et al., 2017). The expansion of non-native species, which seems already to be higher in areas closer to human activity (Duffy and Lee, 2019), should also be linked to the spatial and temporal patterns of paraglacial activity (Ruiz-Fernández et al., 2019), to provide a more comprehensive evaluation. The potential use of biodiversity as an indicator of the effects of climate change (Green et al., 2011, Green et al., 2012, Robinson et al., 2018, Sancho et al., 2019) requires the mapping and monitoring of vegetation to be not only extensive but also accurate (Cannone, 2004, Pereira et al., 2018).
Spaceborne remote sensing is the only practical way to do it in the whole Antarctic Peninsula due to the repeatable extended coverage of the surfaces with multispectral imaging (Fretwell et al., 2011). The availability of satellite remotely sensed datasets in this region since the late 1970s also makes it the only possible way to create a comprehensive baseline describing the location and extent of vegetation in multitemporal evaluations. Some methodologies to classify vegetated areas in Antarctica based on remotely sensed imagery of multiple satellite platforms, mainly optical imagery but also radar, have already been developed. These supervised classifiers are methodologically diverse and, among others, are based on vegetation indexes (Fretwell et al., 2011), hierarchical procedures (Kim and Hong, 2012), spectral mixture analysis (Shin et al., 2014), statistical and machine learning methods (Vieira et al., 2014, Jawak et al., 2016, Schmid et al., 2017, Schmid et al., 2018), matched filtering (Casanovas et al., 2015) or on object-based image analysis (Andrade et al., 2018).
Although the spatial resolution of the images used in each situation is varied, from the higher and metric scales of more recent satellites (Ikonos, QuickBird or WorldView, for instance) to the lower 30 m/pixel of the most long-lasting satellite series (Landsat), all these methodological approaches share a common feature: the classifiers perform well only when the spectral mixing in each image pixel is low. Although the spectral signatures of the different Antarctic vegetation classes are distinct (Lovelock and Robinson, 2002, Malenovský et al., 2015, Calviño-Cancela and Martín-Herrero, 2016), their nature of spatial occurrence, mainly constituted by relatively small and sparse patches of lichens and mosses (Convey et al., 2014), may easily provide observations in satellite imagery with undesired degrees of spectral mixing with other classes (soils, rocks, water, snow, ice) and therefore being established in many locations with low levels of certainty. Although the importance of this issue decreases with the increase of spatial resolution, it is generically verified at all scales of observation. This uncertainty is obliging algorithms to be very conservative to minimize false identifications and, consequently, to incorrectly designate many vegetated areas as unvegetated (Casanovas et al., 2015). Moreover, most of these procedures are validated with data from few and specific sites with local oriented purposes, preventing robust and integrated extrapolations in space but also in time. In particular, the monitoring of the vegetation abundance and biodiversity, which is crucial for establishing plant growth rates together with associated meteorological and micro-climate data (Sancho and Pintado, 2004, Sancho et al., 2007, Li et al., 2014) could be much better performed if an intermediate scale of observation between field and satellite imagery is available.
A solution to overcome this issue and consequently produce more reliable thematic maps may be provided through the incorporation in the classification procedure of intermediate scales of observation between ground and satellite levels. This integration surely allows understanding the spatial characteristics of the vegetation in a more continuous way and how it relates for instance to topography and geomorphology (Ruiz-Fernández et al., 2017) and how it changes between scales of observation in relatively extended areas. Besides the traditional aerial surveys with airplanes or helicopters, Unmanned Aerial Vehicles (UAV) are becoming the strongest alternative, due to its portability, ease of use and comparatively low cost. The ultra-high resolution up to few mm/pixel of the images captured and the fairly large coverage that they can provide (when compared to traditional field observations), permits distinguishing many details related to the vegetation not evident in satellite imagery, namely about their communities (for instance, lichen or moss) but also about their spatial organization (texture, density, level of mixture with other surfaces) and the exact limits of occurrence. The incorporation of this local but very detailed information, together with the one collected at ground level (Pina et al., 2016), can greatly contribute to increase the level of detection in spaceborne imagery and produce more accurate and reliable mappings of the vegetation.
Therefore, the use of small UAV in terrestrial regions of Antarctica is increasing steadily, due to the possibility of gathering data of less accessible regions with unprecedented detail, although field operations are normally complicated due to weather conditions. The application topics are varied in a multitude of objectives, namely for magnetic anomalies detection (Funaki et al., 2014), atmospheric observations (Cassano, 2014), glacial retreat quantification (Pudełko et al., 2018), moraine sedimentological characterization (Westoby et al., 2015), periglacial landform mapping (Dabski et al., 2018), patterned ground identification (Pina et al., 2019), wildlife inventorying and monitoring (Goebel et al., 2015, Krause et al., 2017, Mustafa et al., 2018, Pfeifer et al., 2019, Korczak-Abshire et al., 2019) and monitoring coastal sea-ice (Li et al., 2019), among others.
In what concerns vegetation studies in Antarctica, UAVs are being extremely helpful in providing additional details for mapping procedures (Turner et al., 2014), in obtaining the micro-topography of moss beds (Lucieer et al., 2014), in assessing the stress (Malenovský et al., 2017) and health states of plants (Turner et al., 2018) and in their mapping in sites located some kilometres away from the operation centre (Zmarz et al., 2017). All these studies refer to diverse topics related to vegetation in relatively small areas with none being related to satellite imagery.
The increase of the level of detail that UAV datasets can provide in relatively large areas of the surface, when compared to traditional field observations, at a ultra-high resolution of few cm/pixel, allow the unequivocal discrimination of the main vegetation types (mosses from lichens) and their densities and textural arrangements to establish the amount and degree of mixing with background soil and rock.
The main objective of this work is to detect accurately recent changes on vegetation covers in Antarctica through remote sensing which, due to its nature of occurrence in small and sparse areas, requires a novel methodological approach. The procedure is guided by the details extracted from higher resolution images (UAV) to build and calibrate a robust classification model of the lower resolution images (satellite).
Section snippets
Study area
Fildes Peninsula (62°12′S, 58°58′W), located in King George Island (South Shetlands, Maritime Antarctica), is the selected study area (Fig. 1). It is one of the largest ice-free regions of the South Shetlands with about 29 km2 (Peter et al., 2008), being characterized by high biodiversity (Braun et al., 2012). Geologically, the peninsula consists of a sequence of Late Cretaceous volcanoclastic rocks (basalts, andesite to dacite), overlaid by limestones, tuffaceous conglomerates, sandstones and
Remotely sensed imagery
The remotely sensed data used in this study consists of four recent datasets of optical imagery captured between 2006 and 2017 through satellite and UAV platforms, as detailed in Table 1.
The spaceborne datasets are constituted by very high resolution (VHR) imagery covering the whole Fildes Peninsula from satellites QuickBird (QB) in 2006 and WorldView-2 (WV2) in 2011 and 2017 (Fig. S1). QB contains 4 multispectral bands (MS: B-blue, G-green, R-red and NIR-near-infrared) of 2.4 m/pixel and 1
Methodology
A methodology was developed to obtain accurate maps of vegetation that may be used with high certainty in multitemporal change detection assessments. It is constituted by two sequential phases: ‘Phase I’ consists of the calibration of a classification model of satellite imagery that greatly benefits from the integration of synchronous UAV imagery, followed by ‘Phase II’ that, based on the calibrated model, classifies satellite images of other dates where such detailed data is not available.
Results
The details of the application of the two phases of the methodology and the results obtained are presented in the following, being the experimental procedures done using ENVI software from Harris Geospatial.
Discussion
It is important to be aware that although the classification procedure is robust and achieves high performances, it contains some uncertainty related to each surface class. This issue, already evaluated in detail in the design and calibration of the classifier, can be analysed with more detail by taking into consideration the geometry of the areas that changed between two consecutive dates.
The maps of multitemporal changes (Fig. 5), that show a very high spatial coherence between dates,
Conclusions
The inclusion of an intermediate scale of observation between ground and satellite is a major contribution for guiding the supervised decision procedure and consequently obtaining robust classification performances of the vegetation in spaceborne imagery of an ice-free area in Fildes Peninsula. The centimetric resolution provided by the UAV-based products, unveiling many details in the vegetation organization and in their clear discrimination between lichen and moss formations, is also a point
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The research presented in this paper was funded by the Portuguese Polar Program (PROPOLAR) through research campaign projects CIRCLAR in 2016-2017 and SNOWCHANGE in 2011-2012 and the pluriannual funding projects of CERENA (UID/ECI/04028/2019) and CEG (UID/GEO/00295/2019) funded by FCT-Fundação para a Ciência e a Tecnologia, Portugal.
The support at Escudero station and field logistics in Fildes Peninsula provided by Instituto Antártico Chileno (INACH), and the logistical support by the
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