Estimating carbon stocks at a regional level using soil information and easily accessible auxiliary variables

One of the most important challenges of digital soil mapping is the development of methods that allow the characterisation of large areas with a high-resolution. Surface soils, forming the largest pool Of terrestrial organic carbon, may be able to sequester atmospheric carbon and thus mitigate climate change. However, this remains controversial, largely due to insufficient information on SOC stocks worldwide. One reason for this is the generally limited number of available data points, especially when large areas are considered, while another reason lies on the accuracy of interpolation techniques used for SOC mapping. The study was performed in Laos, a 230,566 km(2) area mostly forested and with steep slopes, and where soil data from 2806 pits is available. Our objective was to estimate SOC stocks to a depth of 1 m over the whole country while improving regional digital soil mapping (RDSM). SOC mapping by using purely spatial approaches of ordinary kriging (OK), inverse distance weighting (IDW) and regularized spline with tension (RST) was compared with the use of additional information on relief, climate and soils through co-kriging (OCK). The generation and validation data sets were composed of 2665 and 141 data points respectively. Overall, OCK using a multiple correlation with elevation above sea level, compound topographic index, mean slope gradient, average annual rainfall, and soil clay content (R-2 = 0.42; P level<0.001) as covariate, yielded the most accurate predictions (19.7 kg C m(-2) with standard error of +/- 3.2 kg C m(-2); and 4.54 +/- 0.74 billion tons of SOC for Laos). The pure interpolation techniques were less accurate with 4.51 +/- 1.02 billion tons of SOC for OK and 4.88 +/- 0.94 billion tons of SOC for RST. Besides providing nationwide estimates of SOC stocks these results indicate that using collectively soil punctual information on SOC stocks and their interrelationships with controlling factors which are easy to gather might be an efficient way to improve RDSM

Soil erosion impact on soil organic carbon spatial variability on steep tropical slopes

The main objectives of this study were to evaluate soil organic C (SOC) variability in a representative hillslope of Laos and to quantify the impact of some environmental factors. We collected 2348 soil samples from 581 georeferenced soil pits within a hillslope of northern Laos under traditional shifting cultivation at 0- to 0.05-m depth and then every 0.1 m to 0.35-m depth. The SOC stocks at 0- to 0.05-m depth varied between 0.4 kg C m(-2) (standard error of 0.046 kg C m(-2)) and 1.9 (+/- 0.22) kg C m(-2) and stocks in the 0- to 0.35-m depth were between 2.6 (+/- 0.29) and 11.4 (+/- 1.31) kg C m(-2). About 85% of SOC spatial variability occurred at a distance less than 20 m. As expected, SOC content and stocks at 0- to 0.05-m depth were significantly greater with higher soil clay content and shorter durations Of cultivation (P < 0.001). Bur at 0- to 0.35-m depth, the significance of clay content was only P = 0.04 and stocks surprisingly increased with increasing slope gradient (P < 0.001). Thus, it seems that sloping lands under shifting cultivation act as a conveyor that stores atmospheric inorganic C in soils during the regeneration of natural fallows and ultimately transfers it by water erosion to the steepest areas of hillslopes, where it accumulates, probably due to greater infiltration by water. These results on SOC spatial variations under steep slope conditions of the tropics give a better picture of SOC dynamics that may allow development of optimal strategies of land management to foster main soil functions and offset the current rise in atmospheric CO2

Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density

One of the most important scientific challenges of digital elevation modeling is the development of numerical representations of large areas with a high resolution. Although there have been many studies on the accuracy of interpolation techniques for the generation of digital elevation models (DEMs) in relation to landform types and data quantity or density, there is still a need to evaluate the performance of these techniques on natural landscapes of differing morphologies and over a large range of scales. To perform such an evaluation, we investigated a total of six sites, three in the mountainous region of northern Laos and three in the more gentle landscape of western France, with various surface areas from micro-plots, hillslopes, and catchments. The techniques used for the interpolation of point height data with density values from 4 to 109 points/km(2) include: inverse distance weighting (IDW), ordinary kriging (OK), universal kriging (UK), multiquadratic radial basis function (MRBF), and regularized spline with tension (RST). The study sites exhibited coefficients of variation (CV) of altitude between 12% and 78%, and isotropic to anisotropic spatial structures with strengths from weak (with a nugget/sill ratio of 0.8) to strong (0.01). Irrespective of the spatial scales or the variability and spatial structure of altitude, few differences existed between the interpolation methods if the sampling density was high, although MRBF performed slightly better. However, at lower sampling densities, kriging yielded the best estimations for landscapes with strong spatial structure, low CV and low anisotropy, while RST yielded the best estimations for landscapes with low CV and weak spatial structure. Under conditions of high CV, strong spatial structure and strong anisotropy, IDW performed slightly better than the other method. The prediction errors in height estimation are discussed in relation to the possible interactions with spatial scale, landform types, and data density. These results indicate that the accuracy of interpolation techniques for DEM generation should be tested not only in relation to landform types and data density but also to their applicability to multi-scales. (c) 2006 Elsevier B.V. All rights reserved