Version 1
: Received: 30 June 2023 / Approved: 3 July 2023 / Online: 3 July 2023 (14:45:46 CEST)
How to cite:
Castrignanò, A.; Buttafuoco, G.; Conforti, M.; Maesano, M.; Moresi, F.V.; Mugnozza, G.S. Geostatistical Fusion of LiDAR and Hyperspectral Data to Improve the Spatial Prediction of Sand Content in a Forested Catchment of Southern Italy. Preprints2023, 2023070114. https://doi.org/10.20944/preprints202307.0114.v1
Castrignanò, A.; Buttafuoco, G.; Conforti, M.; Maesano, M.; Moresi, F.V.; Mugnozza, G.S. Geostatistical Fusion of LiDAR and Hyperspectral Data to Improve the Spatial Prediction of Sand Content in a Forested Catchment of Southern Italy. Preprints 2023, 2023070114. https://doi.org/10.20944/preprints202307.0114.v1
Castrignanò, A.; Buttafuoco, G.; Conforti, M.; Maesano, M.; Moresi, F.V.; Mugnozza, G.S. Geostatistical Fusion of LiDAR and Hyperspectral Data to Improve the Spatial Prediction of Sand Content in a Forested Catchment of Southern Italy. Preprints2023, 2023070114. https://doi.org/10.20944/preprints202307.0114.v1
APA Style
Castrignanò, A., Buttafuoco, G., Conforti, M., Maesano, M., Moresi, F.V., & Mugnozza, G.S. (2023). Geostatistical Fusion of LiDAR and Hyperspectral Data to Improve the Spatial Prediction of Sand Content in a Forested Catchment of Southern Italy. Preprints. https://doi.org/10.20944/preprints202307.0114.v1
Chicago/Turabian Style
Castrignanò, A., Federico Valerio Moresi and Giuseppe Scarascia Mugnozza. 2023 "Geostatistical Fusion of LiDAR and Hyperspectral Data to Improve the Spatial Prediction of Sand Content in a Forested Catchment of Southern Italy" Preprints. https://doi.org/10.20944/preprints202307.0114.v1
Abstract
Soil texture is a key property influencing most soil physical, chemical, and biological processes and its catchment-scale spatial variation may yields insights for soil management. Texture data are generally available only at a few locations, since sampling and laboratory analyses are time consuming. Therefore, it is essential to predict soil size particles texture variability using appropriate methods. Moreover, soil texture distribution across a catchment is influenced by erosion and sedimentation processes controlled by hillslope morphology, which can be quantified through some topographic attributes. The study was aimed to evaluate the ability of a multivariate approach based on non-stationary geostatistics to merge remotely sensed high-resolution LiDAR-derived topographic attributes with Vis-NIR diffuse reflectance spectroscopy and laboratory analysis to produce high-resolution maps of soil sand content and estimation uncertainty in a forested catchment in southern Italy. Moreover, the proposed approach was compared with the commonly used univariate approach of ordinary kriging. Soil samples were collected at 135 locations within a 139 ha-forest catchment with granitic parent material and subordinately alluvial deposits, where soils classified as Typic Xerumbrepts and Ultic Haploxeralf crop out. A number of linear trend models coupled with different auxiliary variables were compared and the best one resulted the model using clay content as auxiliary variable. The improvement of estimation from using only LiDAR data (elevation) compared to the univariate (no trend) model was rather marginal.
Keywords
Forest soils; data fusion; topographic attributes; soil spectroscopy; external drift
Subject
Environmental and Earth Sciences, Soil Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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