References of "Van Wesemael, Bas"
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See detailBiological indicators of soil quality over landscape spatial scales: a case study in Southern Belgium
Krüger, Inken ULg; Chartin, Caroline; Van Wesemael, Bas et al

Poster (2014, July)

Biological indicators are organisms or biological processes whose values give quantitative information on the capacity of a soil to function. Their fast dynamic allows to detect changes on short ... [more ▼]

Biological indicators are organisms or biological processes whose values give quantitative information on the capacity of a soil to function. Their fast dynamic allows to detect changes on short timescales. Five biological indicators (basal respiration, nitrogen mineralisation, microbial carbon and nitrogen, earthworm abundance and biomass, functional microbial diversity) as well as two ecophysiological indices (microbial quotient and metabolic quotient) were tested for their power to characterize the biological soil quality on a landscape level at 60 sites in two South-Belgian landscape units were investigated. All biological indicators differed significantly between the two landscape units showing the biological indicators to be discriminatory on a landscape level. Within each landscape unit, no relationships between biological indicators were found, underlining the need to measure multiple biological indicators. The results represent the first data for a South-Belgian monitoring network of biological soil quality. [less ▲]

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See detailSoil organic carbon assessment by field and airborne spectrometry in bare croplands: accounting for soil surface roughness
Denis, Antoine ULg; Stevens, Antoine; Van Wesemael, Bas et al

in Geoderma (2014), 226-227(August 2014), 94102

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC ... [more ▼]

Visible, Near and Short Wave Infrared (VNSWIR) diffuse reflectance spectroscopy (350 nm to 2500 nm) has been proven to be an efficient tool to determine the Soil Organic Carbon (SOC) content. SOC assessment (SOCa) is usually done by using calibration samples and multivariate models. However one of the major constraints of this technique, when used in field conditions is the spatial variation in surface soil properties (soil water content, roughness, vegetation residue) which induces a spectral variability not directly related to SOC and hence reduces the SOCa accuracy. This study focuses on the impact of soil roughness on SOCa by outdoor VIS-NIR-SWIR spectroscopy and is based on the assumption that soil roughness effect can be approximated by its related shadowing effect. A new method for identifying and correcting the effect of soil shadow on reflectance spectra measured with an Analytical Spectral Devices (ASD) spectroradiometer and an Airborne Hyperspectral Sensor (AHS-160) on freshly tilled fields in the Grand Duchy of Luxembourg was elaborated and tested. This method is based on the shooting of soil vertical photographs in the visible spectrum and the derivation of a shadow correction factor resulting from the comparison of “reflectance” of shadowed and illuminated soil areas. Moreover, the study of laboratory ASD reflectance of shadowed soil samples showed that the influence of shadow on reflectance varies according to wavelength. Consequently a correction factor in the entire [350–2500 nm] spectral range was computed to translate this differential influence. Our results showed that SOCa was improved by 27% for field spectral data and by 25% for airborne spectral data by correcting the effect of soil relative shadow. However, compared to simple mathematical treatment of the spectra (first derivative, etc.) able to remove variation in soil albedo due to roughness, the proposed method, leads only to slightly more accurate SOCa. [less ▲]

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See detailSoil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Bartholomeus, Harm; Kooistra, Lammert; Stevens, Antoine et al

in International Journal of Applied Earth Observation and Geoinformation (2011), 13(1), 81-88

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping ... [more ▼]

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation. [less ▲]

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See detailMEASURING SOIL ORGANIC CARBON IN CROPLANDS AT REGIONAL SCALE USING AIRBORNE IMAGING SPECTROSCOPY
Stevens, Antoine; Udelhoven, Thomas; Denis, Antoine ULg et al

in Geoderma (2010), 158

Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible ... [more ▼]

Conventional sampling techniques are often too expensive and time consuming to meet the amount of data required in soil monitoring or modelling studies. The emergence of portable and flexible spectrometers could provide the large amount of spatial data needed. In particular, the ability of airborne imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution represents an opportunity for improving the monitoring of soil characteristics and soil threats such as the decline of soil organic matter in the topsoil. However, airborne imaging spectroscopy has been generally applied over small areas with homogeneous soil types and surface conditions. Here, five hyperspectral images acquired with the AHS-160 sensor (430 nm–2540 nm) were analysed with the objective to map soil organic carbon (SOC) at a regional scale. The study area, covering a surface of ∼420 km2 and located in Luxembourg, is characterized by different soil types and a high variation in SOC contents. Reflectance data were related to surface SOC contents of bare croplands by means of 3 different multivariate calibration techniques: partial least square regression (PLSR), penalized-spline signal regression (PSR) and support vector machine regression (SVMR). The performance of these statistical tools was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to agro-geological zones, soil type and image number). Under global calibration, the Root Mean Square Error in the Predictions reached 5.3–6.2 g C kg−1. Under local calibrations, this error was reduced by a factor up to 1.9. SOC maps of bare agricultural fields were produced using the best calibration model. Two map excerpts were shown, which display intra- and inter-field variability of SOC contents possibly related to topography and land management. [less ▲]

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See detailIMPROVING SOIL ORGANIC CARBON (SOC) PREDICTION BY FIELD SPECTROMETRY IN BARE CROPLAND BY REDUCING THE DISTURBING EFFECT OF SOIL ROUGHNESS
Denis, Antoine ULg; Tychon, Bernard ULg; Stevens, Antoine et al

in Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009 (2009, July 17)

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are ... [more ▼]

The spatial estimation of Soil Organic Carbon (SOC) at large scale in outdoor condition is an important issue. It has been largely demonstrated that diffuse reflectance spectroscopic techniques, are efficient for SOC determination in field conditions. However these methods are influenced by disturbing factors such as soil water content, vegetation residues and surface roughness, the later being the object of this study. Our laboratory experiments showed that the accuracy of SOC prediction from shadowed soil samples with spectroscopy techniques decreases with increasing soil shadow. In this study a new methodology using a digital camera for identifying and correcting the effect of soil shadow on field reflectance spectra measured with an Analytical Spectral Devices (ASD) during field campaign in bare crop lands has been elaborated and tested. Results showed that the proposed shadow correction method enables improving significantly SOC prediction accuracy and performs better than traditionally used methods consisting in automatic signal processing. [less ▲]

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See detailMonitoring soil organic carbon in croplands using imaging spectroscopy (moca project)
Stevens, Antoine; van Wesemael, Bas; Tychon, Bernard ULg et al

Conference (2008, February 12)

The detection of changes in soil organic carbon (SOC) concentration is essential in both the assessment of SOC sequestration and soil quality. Within the EU soil thematic strategy the depletion of organic ... [more ▼]

The detection of changes in soil organic carbon (SOC) concentration is essential in both the assessment of SOC sequestration and soil quality. Within the EU soil thematic strategy the depletion of organic matter is mentioned as one of the major threats to the soil resource. As one of the first countries Luxemburg has taken the initiative to monitor the SOC concentration of individual fields to allow for eventual CO2 credits and as an indicator for good agro-ecological conditions (GAEC). The aim of this project is to develop an efficient and operational methodology to detect SOC changes in croplands using Imaging Spectroscopy and to map the SOC contents of croplands with high resolution and minimal calibration. [less ▲]

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See detailDetection of carbon stock change in agricultural soils using spectroscopic techniques
Stevens, Antoine; Van Wesemael, Bas; Vandenschrick, Grégoire et al

in Soil Science Society of America Journal (2006), 70(3, MAY-JUN), 844-850

Soil organic carbon (SOC) represents one of the major pools in the global C cycle. Therefore, even small changes in SOC stocks cause important CO, fluxes between terrestrial ecosystems and the atmosphere ... [more ▼]

Soil organic carbon (SOC) represents one of the major pools in the global C cycle. Therefore, even small changes in SOC stocks cause important CO, fluxes between terrestrial ecosystems and the atmosphere. However, SOC stocks are difficult to quantify accurately due to their high spatial variability. The aim of this paper is to evaluate the potential of Imaging Spectroscopy (IS) using the Compact Airborne Spectrographic Imager (CASI; 405-950 nm) and field spectroscopy with an Analytical Spectral Devices spectrometer (ASD; 350-2500 nm) to measure SOC content in heterogeneous agricultural soils. We used both stepwise and partial least square (PLS) regression analysis to relate spectral measurements to SOC contents. Standard Error of Prediction (SEP) for the ASD ranged from 2.4 to 3.3 g C kg(-1) depending on soil moisture content of the surface layer. Imaging spectroscopy performed poorly, mainly due to the narrow spectral range of the CASI. Tests using both the CASI and the Shortwave infrared Airborne Spectrographic Imager (SASI; 900-2500 nm) showed better results. The variation in soil texture and soil moisture content degrades the spectral response to SOC contents. Currently, SEP allows to detect a SOC stock change of 7.2-9.9 Mg C ha(-1) in the upper 30 cm of the soil, and is therefore still somewhat high in comparison with changes in SOC stocks as a result of management or land conversion (0.34.9 Mg C ha(-1) yr(-1)). A detailed SOC maps produced by IS reflected the patterns in SOC contents due to the recent conversion from grassland to cropland. [less ▲]

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