Hyperspectral close-range and remote sensing of soils and related plant associations – Spectroscopic applications in the boreal environment

Hyperspectral close-range and remote sensing techniques have been available to the research community since the 1980’s but applications have focused on forestry and land use. The objective of the study was to explore relevant applications of visible and short wavelength infrared spectroscopy (350−25...

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Bibliographic Details
Published in:International Journal of Remote Sensing
Main Author: Middleton, Maarit
Other Authors: Sutinen, Raimo, Doc., Geological Survey of Finland and University of Oulu, Finland, Insinööritieteiden korkeakoulu, School of Engineering, Maankäyttötieteiden laitos, Department of Real Estate, Planning and Geoinformatics, Haggrén, Henrik, Prof., Aalto University, Department of Real Estate, Planning and Geoinformatics, Finland, Aalto-yliopisto, Aalto University
Format: Doctoral or Postdoctoral Thesis
Language:English
Published: Geological Survey of Finland 2014
Subjects:
Online Access:https://aaltodoc.aalto.fi/handle/123456789/13634
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Summary:Hyperspectral close-range and remote sensing techniques have been available to the research community since the 1980’s but applications have focused on forestry and land use. The objective of the study was to explore relevant applications of visible and short wavelength infrared spectroscopy (350−2500 nm) for detection of physical and chemical properties of glacial till soils and plant species communities related to the soil properties in the boreal environment of northern Finland. Empirical single and multivariate regression techniques (MVR) were applied for predicting glacial till soil dielectric permittivity (ε, i.e. soil moisture) and till elemental concentrations from close-range spectrometry. Predictive kernel and neural network based fuzzy classification approaches were applied for classification of data acquired with AISA and HyMap airborne imaging spectrometers. Ordination techniques were used for revealing plant community structures and optimizing the thematic class hierarchal level. The till soil ε was well predicted from VSWIR spectra with the exponential single-spectral variate but also with MVR techniques. The most accurate results were gained with relevance vector machines. Prediction of till soil chemical element concentrations of Al, Ba, Co, Cr, Cu, Fe, Mg, Mn, Ni, V, and Zn was also statistically valid. Soil moisture based site suitability for Scots pine (Pinus sylvestris) from imaging spectroscopic data was moderately successful as the highest area under the receiver operating characteristics curve (AUC) value was 0.741. Site type mapping of aapa peatlands with support vector machines was highly successful with AUC values 0.946−0.999 for bog, sedge fen, and eutrophic fen. Understanding the ε-reflectance relationship would be evident when artificial regeneration to Scots pine, intolerant of wet soils, is considered on clear-cuts with high soil moisture variability. The site suitability on site prepared forest compartments could be predicted using exposed soil pixels in high spatial resolution ...