Abstract
Abstract. The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.
Highlights
1.1 Handborne Infrared SpectrophotometryThe integration of the infrared (IR) spectral information has enabled to enlarge the reflectance signature of a variety of objects, in order to better detect them
The IR remote sensing has been successful for studying geology (Laftman, 1963), hydrology (Abdel-Hardy, 1970), ecology (Knipling, 1969), archeology (Estes, 1966) and meteorology (Roads, 1973)
Embedded into a scientific era featured with increasingly massive data and efficient machine learners, we propose a novel approach to predict the NIR reflectance response from RGB digital number (DN) explanators using various state-of-the-art regressors, from linear to non-linear regression methods
Summary
1.1 Handborne Infrared SpectrophotometryThe integration of the infrared (IR) spectral information has enabled to enlarge the reflectance signature of a variety of objects, in order to better detect them. By augmenting the electromagnetic spectrum of the traditional visible (red-greenblue, RGB) information, natural and anthropogenic features can be more discriminated given their specific spectral signature in such longer wavebands (Knipling, 1970). These pioneer research works consisted of the ground proof-of-concept studies, whose results were the rationale to embed IR sensors into top view platforms. Following the declassification of the IR imagery by Defence Ministers or Departments in various countries, a plethora of scientists have used this IR imagery as a stand-alone resource provided with increasing finer spectral resolution, topping with the Compact Airborne Hyperspectral Imager (Babey and Anger, 1989). These latter findings at hyperspatial resolution (close to the meter grain size) were constrained by a local spatial scene
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