Soil-landscape interplays at Harmony Point, Nelson Island, Maritime Antarctica: Chemistry, mineralogy and classification

Rodrigues, William Fortes; Oliveira, Fábio Soares de; Schaefer, Carlos Ernesto Gonçalves Reynaud; Leite, Mariângela Garcia Praça; Machado, Michel Ferreira; Bockheim, James G.; Putzke, Jair

doi:10.1016/j.geomorph.2019.03.030

Cited by 30 publications

(20 citation statements)

References 51 publications

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“…The coastal domain is composed of three levels of marine terraces in the eastern sector, two levels of marine terraces in western sector, and by several rock outcrops with or without penguin rookeries. Upper platform is composed of several landforms, such as cryoplanated surface (CS), patterned ground fields (PG), debris slopes (DL), waterlogged depressions (WD), and felsenmeers (FE) (Rodrigues et al 2019). Both landscape units, CD and UP, can be occupied by marine birds (petrels, skuas, and penguins), which results in widespread ornithogenic activity and well-developed soils and vegetation.…”

Section: Study Areamentioning

confidence: 99%

“…Both landscape units, CD and UP, can be occupied by marine birds (petrels, skuas, and penguins), which results in widespread ornithogenic activity and well-developed soils and vegetation. According to Rodrigues et al (2019), in marine terrace levels, at coastal domain, occur Sanionia uncinata, Sanionia georgicouncinata, Brachythecium autrosalebrosu, Acarospora macrocyclus, and Caloplaca spp, and Prasiola crispa. In the upper platform, a complex vegetation, composed of lichens (Usnea spp., Sphaerophorus globosus, Stereocaulon spp.…”

Section: Study Areamentioning

confidence: 99%

“…and Brachythecium austrosalebrosum. If the humidity extends far into the carpet, it is mainly formed by Warnsdorfia sarmentosa, but if the depression is dry, Sanionia uncinata is dominant (Rodrigues et al 2019). The patterned grounds are covered by mosses, with Sanionia uncinata or Sanionia georgicouncinata in dry areas, and Warnsdorfia sarmentosa in wet areas (Rodrigues et al 2019).…”

Section: Study Areamentioning

confidence: 99%

“…A total of 14 soil profiles (Table 1) were collected, of which seven from coastal domain (HP-3, HP-5, HP-7, HP-9, HP-24, HP-26, and HP-27) and seven from upper platform (HP-10, . These soils are representatives of the main landforms of upper platform domain and resulted from the physical weathering of local andesitic rocks (Rodrigues et al 2019) each landscape unit (see Fig. 1).…”

Section: Sampling and Analysesmentioning

confidence: 99%

“…Despite this, information about the influence of chemical weathering in Antarctica soils is still scarce and a few studies were carried out (e.g., Haus et al 2016;Schaefer et al 2017;Lopes et al 2019). It is necessary to perform further studies on chemical reactions, weathering rates, soil formation processes (as ornithogenesis and phosphatization), and organic matter accumulation and decomposition (Lee et al 2004;Michel et al 2006;Schaefer et al 2008;Simas et al 2008Simas et al , 2015Simas et al , 2015Navas et al 2008Navas et al , 2018Pereira et al 2013a;Daher et al 2019;Rodrigues et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Geochemistry of Antarctic periglacial soils from Harmony Point, Nelson Island

et al. 2021

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Little is known about the geochemical baseline of Antarctic soils in different environments. We investigated the soil geochemistry of the two main landscape units of Harmony Point (Nelson Island, Maritime Antarctica): the coastal domain and the upper platform. Fourteen soil samples (seven in each landscape unit) were divided according to depth (hA for surface and hC for subsurface horizons) and characterized by their major, trace elements and REE concentrations. The concentration of major elements (SiO 2 , TiO 2 , Al 2 O3, Fe 2 O 3 , MgO, CaO, MnO, and K 2 O) were determined by X-ray fluorescence, whereas trace elements were quantified by inductively coupled plasma-optical emission spectrometry (Co, Ni, As, Cd, Pb, Ba, Cr, Cu, V, Zn, and Zr) and REE by inductively coupled plasma mass spectrometry. The results showed geochemical variation with depth, either related to pedological processes (phosphatization, humification, podzolization, and cryoturbation), and parent material constitution (andesitic basalt in upper platform and mixed volcanic sediments in coastal domain). The main chemical aspects distinguishing Harmony Point soils from other Maritime Antarctic soils from the vicinity are: (i) higher CIA index; (ii) P 2 O 5 enrichment due to bird guano and enhanced pedogenesis; (iii) REE retention; (iv) enrichment in Fe 2 O 3 and S concentrations. The REE concentration was influenced by weathering processes combined with allochthonous inputs, such as volcanic ashes and iceberg-transported granitic boulders at the coastal domain. The Harmony Point soils are little subjected to anthropic impacts, so they can be used as a basis for environmental monitoring programs in the Maritime Antarctica region.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Section: Sampling and Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geochemistry of Antarctic periglacial soils from Harmony Point, Nelson Island

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Evaluation of machine learning algorithms to classify and map landforms in Antarctica

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The detailed geomorphology of ice‐free landscapes of Antarctica is key to understanding how their highly fragile environments respond to climate change, at different temporal and spatial scales. Despite the recent advances in geomorphological studies of ice‐free areas, machine learning applications to produce landform maps are still scarce on the Antarctic continent. In this study, we evaluated the predictive performance of different supervised machine learning algorithms to produce digital geomorphological maps in Vega Island—Antarctic Peninsula region. We tested six different models: average artificial neural networks, C5.0 decision tree, random forest, support vector machine, supervised self‐organizing map and weighted k‐nearest neighbours. We used an initial set of 54 geomorphometric and spectral predictors, from which redundant variables with Pearson correlation coefficient >|0.95| were removed, and only the most important predictors for each model were selected using recursive feature elimination. For training, we ran each model 100 times and predictions were assessed by the kappa and global accuracy values. The best predictors were the Red Edge 6 and SWIR 11 bands, roughness concentration index, elevation and drainage density. The decision trees C5.0 and random forest had the best performance, with average validation kappa of 0.85 ± 0.03 and 0.84 ± 0.03, respectively, evidencing excellent prediction. Despite the similar performance, random forest showed greater uncertainty degree and accuracy when classifying complex landforms, attesting to its great robustness. From sensitivity and specificity values, we observed that the glaciers and talus showed higher accuracy, whereas cryoplanated platforms and scree slopes had the worst classification. The presented methodology optimized the classification by selecting the most important predictors, assessing accuracy and evaluating uncertainty. The results indicated that machine learning methods have great potential to produce geomorphological mappings in the Antarctic ice‐free areas, as a promising tool to provide detailed information on remote, harsh polar environments.

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