Login
Toggle navigation
Home
ICPA
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Leadership
ISPA Leadership
Officers
Past Presidents
Officer Responsibilities
Country Representatives
Communities
Community Guidance
On-Farm Experimentation
Nitrogen
Latin America
Economics
African Association for Precision Agriculture
Membership
ISPA Member Benefits
Membership Form
Events
ISPA Events
ACPA
ACPA Proceedings
AfCPA
AfCPA Proceedings
CLAP
CLAP Proceedings
ECPA
ECPA Proceedings
ICPA
ISPA Webinars
OFE
AAPA
Latin American
Robotics and Automation Symposium
Event Overview
Registration
Program
Venue
Speakers
About ISPA
Newsletters
History
Jobs
Precision Ag Definition
Agriculture Course Database Submission
Publications
ICPA Proceedings
ECPA Proceedings
Contact Us
Members
Suggestion Form
Conference
Abstract Management
Abstract Topic Groups
Author Instructions
Registration
Registration Information
16th ICPA - Conference Registration
Registrants Map
Hotel and Travel Information
Tour
Workshops
Exhibit Hall Map
Sponsors
Conference Program
General Outline
Oral Program
Poster Program
Student Poster Awards
Keynote
Plenary Session
Awards
Photos
Conference Survey
Proceedings
Proceedings
Search
Authors
Topics
Years
Types
Find matching any:
Reset
» Add more years
Add filter to result:
Visible And Near-Infrared Spectroscopy For Monitoring Potentially Toxic Elements In Reclaimed Dumpsite Soils Of The Czech Republic
1
L. Borùvka,
2
M. Saberioon,
1
R. Vaát,
1
A. Gholizadeh
1. Czech University of Life Sciences
2. University Putra Malaysia
Due to rapid economic development, high levels of potentially harmful elements and heavy metals are continuously being released into the brown coal mining dumpsites of the Czech Republic. Elevated metal contents in soils not only dramatically impact the soil quality, but also due to their persistent nature and long biological half-lives, contaminant elements can accumulate in the food chain and can eventually endanger human health. Conventional methods for investigating potentially harmful element contamination of soil based on raster sampling and chemical analysis are time consuming and relatively expensive. Visible and Near-Infrared (Vis-NIR) diffuse reflectance spectroscopy provides a rapid and inexpensive tool to simultaneously and accurately predict various soil properties. In this study concentrations of Manganese (Mn), Copper (Cu), Cadmium (Cd), Zinc (Zn), Iron (Fe), Lead (Pb) and Arsenic (As) in soil samples from fields near the brow coal mining dumpsites in the Czech Republic were chemically analyzed and the suitability of Vis-NIR diffuse reflectance spectroscopy for predicting their occurrence was evaluated. Soil spectral reflectance was measured with an ASD FieldSpec 3 spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory conditions and the correlations between seven toxic elements and soil diffuse reflectance spectra were studied. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) models were constructed to relate soil contaminants data to the reflectance spectral data by applying first and second derivatives preprocessing strategies. Then, the performance of Vis-NIR calibration models was evaluated by Residual Prediction Deviation (RPD) and coefficients of determination (R
2
). Based on the correlation patterns with reflectance spectra, the seven studied potentially toxic elements were categorized into two or three groups. Moreover, according to the criteria of minimal RPD and maximal R
2
, the first derivative and SVMR models provided more accurate prediction models for soil contaminants than PLSR models which were more feasible to predict the toxic metal levels in agricultural soils. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable chemometric indicator could be a nondestructive alternative for monitoring of the soil environment. Because soil properties in contaminated areas generally show strong variation, a comparatively large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust Vis-NIR reflectance spectroscopy calibration models. Future studies with real-time remote sensing data and field measurements are also strongly recommended.Vis-NIR) diffuse reflectance spectroscopy provides a rapid and inexpensive tool to simultaneously and accurately predict various soil properties. In this study concentrations of Manganese (Mn), Copper (Cu), Cadmium (Cd), Zinc (Zn), Iron (Fe), Lead (Pb) and Arsenic (As) in soil samples from fields near the brow coal mining dumpsites in the Czech Republic were chemically analyzed and the suitability of Vis-NIR diffuse reflectance spectroscopy for predicting their occurrence was evaluated. Soil spectral reflectance was measured with an ASD FieldSpec 3 spectroradiometer (Analytical Spectral Devices, Inc., USA) under laboratory conditions and the correlations between seven toxic elements and soil diffuse reflectance spectra were studied. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) models were constructed to relate soil contaminants data to the reflectance spectral data by applying first and second derivatives preprocessing strategies. Then, the performance of Vis-NIR calibration models was evaluated by Root Mean Square Error (RMSE) and coefficients of determination in cross-validation (R
2
cv). Based on the correlation patterns with reflectance spectra, the seven studied potentially toxic elements were categorized into two groups. The prediction accuracy for group I (As, Cd, Fe, Cu, Zn) was higher than that for group II (Mn, Pb). Moreover, according to the criteria of minimal RMSE and maximal R
2
cv, the first derivative and SVMR models provided more accurate prediction models for soil contaminants than PLSR models which were more feasible to predict the toxic metal levels in agricultural soils. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable chemometric indicator could be a nondestructive alternative for monitoring of the soil environment. Because soil properties in contaminated areas generally show strong variation, a comparatively large number of calibrating samples, which are variable enough and uniformly distributed, are necessary to create more accurate and robust Vis-NIR reflectance spectroscopy calibration models. Future studies with real-time remote sensing data and field measurements are also strongly recommended.
Keyword
: Soil contamination, Visible and Near-Infrared, Partial least square regression, Support vector machine regression, Data preprocessing
L. Borùvka
M. Saberioon
R. Vaát
A. Gholizadeh
Proximal Sensing in Precision Agriculture
Oral
2014
Download paper