Hyperspectral analysis of clay minerals

Suresh, G.; Sreenivas, K.; Sivasamy, R.

doi:10.5194/isprsarchives-xl-8-443-2014

Cited by 11 publications

(1 citation statement)

References 10 publications

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“…Coblentz, 1906) and its applications in geology are well established (Lyon & Burns, 1963). More recently, near-infrared remote sensing has been applied in soil sciences for multiple purposes, including: mapping proportions of sand, silt, and clay content (Saleh, Belal & Arafat, 2013;Silva et al, 2016); determination of soil salinity (Feyziyev et al, 2016); monitoring soil moisture and capacity of water absorption (Ben-Dor et al, 2002;Whiting, Li & Ustin, 2004); estimate organic carbon content (Hu, Chau & Si, 2015;Viscarra Rossel & Hicks, 2015); differentiate types of clay minerals (Surech, Sreenivas & Sivasamy, 2014); and assessing soil contamination and detection of heavy metals (Mohamed et al, 2016(Mohamed et al, , 2018). Yet, until now, machine learning approaches in geospatial paleontology tended to achieve good results but as "black-boxes" that did not reveal how exactly they were interpreting a "fossiliferous signal", and thus the relative importance of specific spectral bands for remote fossil site detection has not previously been demonstrated.…”

Section: Ground-truthingmentioning

confidence: 99%

Unsupervised learning of satellite images enhances discovery of late Miocene fossil sites in the Urema Rift, Gorongosa, Mozambique

Coelho

Anemone

Carvalho

2021

PeerJ

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Background Paleoanthropological research focus still devotes most resources to areas generally known to be fossil rich instead of a strategy that first maps and identifies possible fossil sites in a given region. This leads to the paradoxical task of planning paleontological campaigns without knowing the true extent and likely potential of each fossil site and, hence, how to optimize the investment of time and resources. Yet to answer key questions in hominin evolution, paleoanthropologists must engage in fieldwork that targets substantial temporal and geographical gaps in the fossil record. How can the risk of potentially unsuccessful surveys be minimized, while maximizing the potential for successful surveys? Methods Here we present a simple and effective solution for finding fossil sites based on clustering by unsupervised learning of satellite images with the k-means algorithm and pioneer its testing in the Urema Rift, the southern termination of the East African Rift System (EARS). We focus on a relatively unknown time period critical for understanding African apes and early hominin evolution, the early part of the late Miocene, in an overlooked area of southeastern Africa, in Gorongosa National Park, Mozambique. This clustering approach highlighted priority targets for prospecting that represented only 4.49% of the total area analysed. Results Applying this method, four new fossil sites were discovered in the area, and results show an 85% accuracy in a binary classification. This indicates the high potential of a remote sensing tool for exploratory paleontological surveys by enhancing the discovery of productive fossiliferous deposits. The relative importance of spectral bands for clustering was also determined using the random forest algorithm, and near-infrared was the most important variable for fossil site detection, followed by other infrared variables. Bands in the visible spectrum performed the worst and are not likely indicators of fossil sites. Discussion We show that unsupervised learning is a useful tool for locating new fossil sites in relatively unexplored regions. Additionally, it can be used to target specific gaps in the fossil record and to increase the sample of fossil sites. In Gorongosa, the discovery of the first estuarine coastal forests of the EARS fills an important paleobiogeographic gap of Africa. These new sites will be key for testing hypotheses of primate evolution in such environmental settings.

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