Keywords
Vegetation heterogeneity; soil variability, soil moisture patterns; electromagnetic induction (EMI); Helicopter-borne electromagnetic (HEM)
Location
Session B3: Integrated Hydrodynamic, Hydrological, Water Quality, and Ecological Models
Start Date
18-6-2014 9:00 AM
End Date
18-6-2014 10:20 AM
Abstract
Soil moisture patterns are key parameters when it comes to controlling and managing process-pattern interactions in processes relating to soil, vegetation, landscape, climate and the ecosystem. Soil pattern heterogeneity is hard to determine in European landscapes using direct procedures, which are used on soil with little or no vegetation, because the soil is often covered with vegetation all year round. The goal of this study is therefore to develop indirect procedures to analyze soil moisture patterns, which "use the biochemical-biophysical characteristics of plants as sensors and indicators" for soil moisture heterogeneity. For this research, geoelectrical methods which include electromagnetic induction (EMI) via a mobile geoplatform with a tractor, and the helicopter electromagnetic method (HEM) are used in the two test areas to quantify model information for soil moisture patterns. At the same time, in both study areas the suitability of optical airborne and satellite remote sensing data (hyperspectral AISA-DUAL, Modis, Landsat TM) will be examined to predict the connection between the spectral response of biochemical-biophysical vegetation characteristics and underlying soil moisture patterns. The first results show the best univariate models for predicting electrical conductivity for the vertical dipole EM38DD V with an R2=0.54 based on the spectral information NPCI (Normalized Pigments Reflectance Index). To predict the horizontal dipole EM38DD H with the spectral index NPCI an R2-0.65 was achieved. The combination of variables including the geographical elevation was tested as the input for a multivariate regression analysis. An improvement could be made to explain the variance of EMI measurement signals by combining elevation and spectral information.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Analysis of vegetation heterogeneity as sensor for soil moisture patterns using remote sensing
Session B3: Integrated Hydrodynamic, Hydrological, Water Quality, and Ecological Models
Soil moisture patterns are key parameters when it comes to controlling and managing process-pattern interactions in processes relating to soil, vegetation, landscape, climate and the ecosystem. Soil pattern heterogeneity is hard to determine in European landscapes using direct procedures, which are used on soil with little or no vegetation, because the soil is often covered with vegetation all year round. The goal of this study is therefore to develop indirect procedures to analyze soil moisture patterns, which "use the biochemical-biophysical characteristics of plants as sensors and indicators" for soil moisture heterogeneity. For this research, geoelectrical methods which include electromagnetic induction (EMI) via a mobile geoplatform with a tractor, and the helicopter electromagnetic method (HEM) are used in the two test areas to quantify model information for soil moisture patterns. At the same time, in both study areas the suitability of optical airborne and satellite remote sensing data (hyperspectral AISA-DUAL, Modis, Landsat TM) will be examined to predict the connection between the spectral response of biochemical-biophysical vegetation characteristics and underlying soil moisture patterns. The first results show the best univariate models for predicting electrical conductivity for the vertical dipole EM38DD V with an R2=0.54 based on the spectral information NPCI (Normalized Pigments Reflectance Index). To predict the horizontal dipole EM38DD H with the spectral index NPCI an R2-0.65 was achieved. The combination of variables including the geographical elevation was tested as the input for a multivariate regression analysis. An improvement could be made to explain the variance of EMI measurement signals by combining elevation and spectral information.
