By Marco Nocita
Abstract
In the subtropical thicket biome of the Eastern Cape province of South Africa, heavy browsing by goats, which remove shrub biomass more rapidly than it is replaced, transforms the dense closed-canopy shrubland into an open savanna-like system. This transformation causes a lot of changes, among which, soil fertility depletion.
This document presents a project dealing with organic carbon (OC), iron oxides, and clay content assessment, in the degraded thicket biome, through the combination of soil spectroscopy and partial least square regression (PLSR) techniques. The study area is a transect crossing in direction south east-north west the Eastern Cape province of South Africa, from latitude -33.57 to -32.59 and longitude 25.38 (eastern extreme) to 25.26 (western extreme). The study area has been selected based on a GIS analyses, realized overlaying vegetation type, rainfall and topography data sets. A total of 113 points have been visited over a distance of 130 km. At every point field spectroscopy measurements were realized and soil samples of the first cm (topsoil) and of the 0-20 cm have been collected. The soil samples have been chemically and spectrally analyzed. The present study models the relationships between soil spectral reflectances, measured in situ and in the laboratory, and the soil parameters taken in consideration. The PLSR models developed with laboratory and field spectra gave good predictions of OC, with a root mean square error of validation (RMSEV) <0.6, and sufficient results for the iron oxides prediction (RMSEV always >0.55). The clay content prediction models didn’t produce enough accuracy. Results indicated that soil stoniness is an important variable to consider for the creation of soil properties prediction models.. The up-scaling process of the OC laboratory and field spectroscopy prediction models to the 232 EnMAP channels gave high level of accuracy, also including the noise component (signal to noise ratio=100). The promising results of this research study will serve as base for the future up-scaling processes of the obtained ground-based regression models to air-borne and space-borne hyperspectral data, in order to cover all the subtropical thicket biome of South Africa.