Mapping the CN Ratio of the Forest Litters in Europe-Lessons for Global Digital Soil Mapping

被引:5
作者
Carre, F. [1 ]
Jeannee, N. [2 ]
Casalegno, S. [3 ,4 ]
Lemarchand, O. [2 ]
Reuter, H. I. [3 ]
Montanarella, L. [5 ]
机构
[1] INERIS, Div Sci, Parc Technol Alata,BP 7, F-60550 Verneuil En Halatte, France
[2] Geovariances, F-77212 Avon, France
[3] DG JRC, Land Management & Nat Hazards Unit, I-21020 Ispra, VA, Italy
[4] Predict Models Biomed & Environm Fdn Bruno Kessle, I-38123 Povo, Trento, Italy
[5] European Commiss, Inst Environm & Sustainabil, Land Management & Nat Hazards Unit, DG Joint Res Ctr, I-21027 Ispra, VA, Italy
来源
DIGITAL SOIL MAPPING: BRIDGING RESEARCH, ENVIRONMENTAL APPLICATION, AND OPERATION | 2010年 / 2卷
关键词
CN ratio; Kriging; Neural network;
D O I
10.1007/978-90-481-8863-5_18
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The Carbon/Nitrogen ratio (CN) of forest soils is one of the best predictors for evaluating the soil functions mainly involved in climate change issues. The CN ratio of forest litters depends generally on tree species and forest management which are local factors, but also on broader environmental factors. Thus, the European forest litter CN ratio map is predicted using: (a) punctual CN ratio measurements collected systematically every 16 km in European forests and analyzed according to a common European laboratory method; (b) spatially continuous information on tree species abundance (derived from interpolation) and climate, landform and lithology at 1 km resolution. The spatial modeling of the CN ratio is done according to complementary approaches: first, a classical kriging approach done on the CN ratio measurements; and second, a neural network approach using a set of nonlinear equations on the environmental predictors. Other multivariate geostatistical approaches were tested but not retained for final results due to lack of correlation between environmental factors. Twenty percent of CN ratio measurements are kept for validation purpose. The two approaches are compared using coefficient of determinations and Root Mean Square Errors on the validation dataset. Surprisingly, the best approach is the classical kriging, meaning that the spatial structure and variability of CN ratio cannot be explained by the environmental factors, which show high local variation. This leads to a discussion of the quality of the data and to envisage possible risks for global digital soil maps.
引用
收藏
页码:217 / 225
页数:9
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