Mette B. 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.

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%.

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.

Comparing Kriging and Regression Approaches for Mapping Soil Clay Content in a Diverse Danish Landscape

Abstract Information on the spatial variability of soil texture including soil clay content in a landscape is very important for agricultural and environmental use. Different prediction techniques are available to assess and map spatial variability of soil properties, but selecting the most suitable technique at a given site has always been a major issue in all soil mapping applications. We studied the prediction performance of ordinary kriging (OK), stratified OK (OKst), regression trees (RT), and rule-based regression kriging (RKrr) for digital mapping of soil clay content at 30.4-m grid size using 6,919 topsoil (0–20 cm) samples in an approximately 7,100 km2 representative area in Denmark. Eighty percent of the data were used for model calibration and the rest for validation. Twelve derivatives extracted from the digital elevation model, together with the information derived from the maps of landscape types, land use, geology, soil types, and georegions, were used as predictors in RT and RKrr modeling. Existing landscape classes were considered for stratification in OKst, and variograms were used to study the spatial autocorrelation. Predicting ability of the models was assessed with R2, RMSE, and residual prediction deviation (RPD) for comparison. Among all the prediction methods, the highest R2 (i.e., 0.74) and lowest RMSE (i.e., 0.28) were associated with the RKrr model, which also had an RPD value of 2.2, confirming RKrr as the best prediction method. Stratification of samples slightly improved the prediction in OKst compared with that in OK, whereas RT showed the lowest performance of all (R2 = 0.52; RMSE = 0.52; and RPD = 1.17). We found RKrr to be an effective prediction method and recommend this method for any future soil mapping activities in Denmark.

Digital Mapping of Toxic Metals in Qatari Soils Using Remote Sensing and Ancillary Data

After decades of mining and industrialization in Qatar, it is important to estimate their impact on soil pollution with toxic metals. The study utilized 300 topsoil (0–30 cm) samples, multi-spectral images (Landsat 8), spectral indices and environmental variables to model and map the spatial distribution of arsenic (As), chromium (Cr), nickel (Ni), copper (Cu), lead (Pb) and zinc (Zn) in Qatari soils. The prediction model used condition-based rules generated in the Cubist tool. In terms of R2 and the ratio of performance to interquartile distance (RPIQ), the models showed good predictive capabilities for all elements. Of all of the prediction results, Cu had the highest R2 = 0.74, followed by As > Pb > Cr > Zn > Ni. This study found that all of the models only chose images from January and February as predictors, which indicates that images from these two months are important for soil toxic metals’ monitoring in arid soils, due to the climate and the vegetation cover during this season. Topsoil maps of the six toxic metals were generated. The maps can be used to prioritize the choice of remediation measures and can be applied to other arid areas of similar environmental/socio-economic conditions and pollution causes.