Ecological assessments such as species distribution modelling and benchmarking site quality towards regulations often rely on full spatial coverage information of site factors such as soil acidity, moisture regime or nutrient availability. To determine if remote sensing (RS) is a viable alternative to traditional data sources of site factor estimates, we analysed the accuracy (using ground truth validation measurements) of traditional and RS sources of pH and mean spring groundwater level (MSL, in m) estimates. Traditional sources were a soil map and hydrological model. RS estimates were obtained using vegetation indicator values (IVs) from a Dutch national system as an intermediate between site factors and spectral response. IVs relate to those site factors that dictate vegetation occurrence, whilst also providing a robust link to canopy spectra. For pH, the soil map and the RS estimate were nearly as accurate. For MSL, the RS estimates were much closer to the observed groundwater levels than the hydrological model, but the error margin of the estimates still exceeded the tolerance range of moisture sensitive vegetation. The relatively high accuracy of the RS estimates was made possible by the availability of local calibration points and large environmental gradients in the study site. In addition, the error composition of the RS estimates could be analysed step-by-step, whereas the traditional sources had to be accepted 'as-is'. Also considering that RS offers high spatial and temporal resolution at low costs, RS offered advantages over traditional sources. This will likely hold true for any other situation where prerequisites of accurate RS estimates have been met.
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|Publication status||Published - 2015|
Roelofsen, H. D., van Bodegom, P. M., Kooistra, L., van Amerongen, J. J., & Witte, J. P. M. (2015). An evaluation of remote sensing derived soil pH and average spring groundwater table for ecological assessments. International Journal of Applied Earth Observation and Geoinformation, 43(2015), 149-159. https://doi.org/10.1016/j.jag.2015.05.005