Mogens Humlekrog Greve

Digital mapping of soil organic carbon contents and stocks in Denmark.

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0−5, 5−15, 15−30, 30−60 and 60−100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg−1 was reported for 0−5 cm soil, whereas there was on average 2.2 g SOC kg−1 at 60−100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg−1 was found at 60−100 cm soil depth. Average SOC stock for 0−30 cm was 72 t ha−1 and in the top 1 m there was 120 t SOC ha−1. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark.

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas.

Generating a Danish raster-based topsoil property map combining choropleth maps and point information.

Abstract Geografisk Tidsskrift, Danish Journal of Geography 107(2):1–12, 2007 The Danish environmental authorities have posed a soil type dependent restriction on the application of nitrogen. The official Danish soil map is a choropleth topsoil map classifying the agricultural land into eight classes. The use of the soil map has shown that the maps have serious classification flaws. The objective of this work is to compile a continuous national topsoil texture map to replace the old topsoil map. Approximately 45,000 point samples were interpolated using ordinary kriging in 250 m x 250 m cells. To reduce variability and to obtain more homogeneous strata, the samples were stratified according to landscape types. Five new soil texture maps were compiled; one for each of the five textural classes, and a new categorical soil type map was compiled using the old classification system. Both the old choropleth map and the new continuous soil maps were compared to 354 independent soil samples. 48% of the 354 independent samples fell into the correct class in the old map; in the new map 60% fell into the correct class, which is a significant improvement. The verification also showed that 62% of the samples in the forest areas were correctly classified, although these areas were not classified in the old map. Furthermore, when both the old and the new map were compared cell-by-cell, 74% of the cells were classified as belonging to the same class. The new textural maps were tested against the textural properties of 631 independent samples, and the root mean squared error (RMSE) of this comparison was calculated and found to be in the range of 2.8 to 5.2.

Comparative Mapping of Soil Physical–Chemical and Structural Parameters at Field Scale to Identify Zones of Enhanced Leaching Risk

Preferential flow and particle-facilitated transport through macropores contributes significantly to the transport of strongly sorbing substances such as pesticides and phosphorus. The aim of this study was to perform a field-scale characterization of basic soil physical properties like clay and organic carbon content and investigate whether it was possible to relate these to derived structural parameters such as bulk density and conservative tracer parameters and to actual particle and phosphorus leaching patterns obtained from laboratory leaching experiments. Sixty-five cylindrical soil columns of 20-cm height and 20-cm diameter and bulk soil were sampled from the topsoil in a 15-m × 15-m grid in an agricultural loamy field. Highest clay contents and highest bulk densities were found in the northern part of the field. Leaching experiments with a conservative tracer showed fast 5% tracer arrival times and high tracer recovery percentages from columns sampled from the northern part of the field, and the leached mass of particles and particulate phosphorus was also largest from this area. Strong correlations were obtained between 5% tracer arrival time, tracer recovery, and bulk density, indicating that a few well-aligned and better connected macropores might change the hydraulic conductivity between the macropores and the soil matrix, triggering an onset of preferential flow at lower rain intensities compared with less compacted soil. Overall, a comparison mapping of basic and structural characteristics including soil texture, bulk density, dissolved tracer, particle and phosphorus transport parameters identified the northern one-third of the field as a zone with higher leaching risk. This risk assessment based on parameter mapping from measurements on intact samples was in good agreement with 9 yr of pesticide detections in two horizontal wells and with particle and phosphorus leaching patterns from a distributed, shallow drainage pipe system across the field.

Spatial soil zinc content distribution from terrain parameters: A GIS-based decision-tree model in Lebanon

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas.

Comparing Predictive Abilities of Three Visible-Near Infrared Spectrophotometers for Soil Organic Carbon and Clay Determination:

Due to advances in optical technology, a wide range of spectrometers is available. Recent interests in soil global libraries and sensor fusion presents a challenge with respect to combining data from different instrumentation. Little research, however, has been done on the comparison of visible-near infrared (vis-NIR) spectrometers for soil characterisation. There is a need for more work on the effects of scanning strategies and use of different soil instrumentation. We compared three vis-NIR spectrometers with varying resolution, signal-to-noise ratios and spectral range. Their performance was evaluated based on spectra collected from 194 Danish top soils and used to determine soil organic carbon (SOC) and clay content. Scanning procedures for the three spectrophotometers where done according to uniform laboratory protocols. Soil organic carbon and clay calibrations were performed using PLS regression. One third of the data set was used as an independent test set. A range of spectral preprocessing methods was applied in search of model improvement. Validation for SOC content using an independent data set derived from all three spectrophotometers provided values of RMSEP between 0.45% and 0.52%, r2=0.42–0.59 and RPD = 1.2–1.4. Clay content was predicted with a higher precision resulting in RMSEP values between 2.6% and 2.9%, r2 = 0.71–0.77 and RPD values in the range from 2.2 to 2.5. No substantial differences in the prediction accuracy were found for the three spectrometers, although there was a tendency that, in the tradeoff between noise and resolution, low noise was the more important for SOC and clay predictions. The application of different spectral preprocessing procedures did not generate important improvements of the calibration models either. Additionally, data simulation analysis, including resampling to a coarser resolution and addition of noise, was performed. No, or very little, effect of sampling resolution and additional noise on the performance of the spectrophotometers was reported. The results from this study showed that, as long as strict laboratory scanning protocols were followed, no significant differences in constituent determination were found, despite differences in spectral range, spectral resolution, spectral sampling intervals and sample presentation methods. The differences in predictive abilities between the spectrometers were mostly due to differences in spectral range.

Determining and representing width of soil boundaries using electrical conductivity and MultiGrid

Abstract In classical soil mapping, map unit boundaries are considered crisp even though all experienced survey personnel are aware of the fact, that soil boundaries really are transition zones of varying width. However, classification of transition zone width on site is difficult in a practical survey. The objective of this study is to present a method for determining soil boundary width and a way of representing continuous soil boundaries in GIS. A survey was performed using the non-contact conductivity meter EM38 from Geonics Inc., which measures the bulk Soil Electromagnetic Conductivity (SEC). The EM38 provides an opportunity to classify the width of transition zones in an unbiased manner. By calculating the spatial rate of change in the interpolated EM38 map across the crisp map unit delineations from a classical soil mapping, a measure of transition zone width can be extracted. The map unit delineations are represented as transition zones in a GIS through a concept of multiple grid layers, a MultiGrid. Each layer corresponds to a soil type and the values in a layer represent the percentage of that soil type in each cell. As a test, the subsoil texture was mapped at the Vindum field in Denmark using both the classical mapping method with crisp representation of the boundaries and the new map with MultiGrid and continuous boundaries. These maps were then compared to an independent reference map of subsoil texture. The improvement of the prediction of subsoil texture, using continuous boundaries instead of crisp, was in the case of the Vindum field, 15%.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l’Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the ‘upland model’ was able to more accurately predict SOC compared with the ‘upland & wetland model’. However, the separately calibrated ‘upland and wetland model’ did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Change in Peat Coverage in Danish Cultivated Soils During the Past 35 Years

Abstract Mapping the spatial and temporal changes of peatland in farming systems is crucial to the study of soil quality and productivity and the modeling of the global carbon cycle (in relation to climate change). This study compiles a contemporary map (2010) of peatland coverage (according to Kyoto protocol) across the cultivated wetlands of Denmark and compares this actual map to a historical 1975 peat coverage map using simple indicator kriging. For the contemporary peatland mapping, extensive soil sampling databases consisting of 42,568 points with 32,817 historical samples and 9,751 contemporary samples were used. These databases contain partly categorical information on parent material (organic [peat, gytje] and mineral [sand, silt and clay]) and partly continuous data (soil organic carbon, in %) reclassified into organic and mineral soils (using 12% soil organic carbon as a cutoff value). In the simple indicator kriging procedure, the values 0 and 1 were assigned to the stationary means of the indicator codes to represent two hypotheses, that is, mineral and organic (peat) soils, respectively. The collected and analyzed contemporary unbiased organic samples (measured on different rectangular grid scales of 250, 275, and 500 m) in addition to some transformed historical organic samples (according to certain decision rules) were used to estimate the recent areal coverage of peat (2010) that was equivalent to 70,176 ha, and this estimate corresponds to an indicator kriging probability of 0.35. Results revealed there has been a total areal coverage loss of 35% (37,786 ha) of the Danish organic cultivated wetlands during a period of 35 years (map 1975 had 107,962-ha coverage of peat). The peat depletion is related to peat mining and agricultural drainage/tillage activities, rather than natural geological processes.

Predicting soil organic carbon at field scale using a national soil spectral library

Visible and near infrared diffuse reflectance (vis-NIR) spectroscopy is a low-cost, efficient and accurate soil analysis technique and is thus becoming increasingly popular. Soil spectral libraries are commonly constructed as the basis for estimating soil texture and properties. In this study, partial least squares regression was used to develop models to predict the soil organic carbon (SOC) content of 35 soil samples from one field using (i) the Danish soil spectral library (2688 samples), (ii) a spiked spectral library (a combination of 30 samples selected from the local area and the spectral library, 2718 samples) and (iii) three sub-sets selected from the spectral library. In an attempt to improve prediction accuracy, sub-sets of the soil spectral library were made using three different sample selection methods: those geographically closest (84 samples), those with the same landscape and parent material (96 samples) and those with the most alike spectra to spectra from the field investigation (100 samples). These sub-sets were used to develop three calibration models and in predictions of SOC content. The results showed that the geographically closest model, which used the fewest number of samples, gave the lowest root mean square error of prediction (RMSEP) of 0.19% and the highest ratio of performance to deviation (RPD) of 3.7, followed by the spiked library, same parent material, the spectral library and the most alike spectra. The spiked library model also gave a low RMSEP value of 0.19% and high RPD value of 3.7% and performed markedly better than the model without spiking, despite using 30 samples for library spiking. The accuracy of the model developed using a sub-set from a spectral library was highly dependent on geographical location, soil parent material and landscape.