Details
| Translated title of the contribution | Spatial modeling of forest soil properties in Northwestern Switzerland |
|---|---|
| Original language | German |
| Pages (from-to) | 10-22 |
| Number of pages | 13 |
| Journal | Schweizerische Zeitschrift fur Forstwesen |
| Volume | 164 |
| Issue number | 1 |
| Publication status | Published - 1 Jan 2013 |
Abstract
Forest soils are an important natural resource. However, up to now almost no area-wide forest soil information was available for Switzerland. Since 2006, model-based, high-resolution maps of forest soil properties in the cantons Basel-Landschaft and Basel-Stadt were generated, depicting soil depth, stone content, soil wetness, acidity and water storage capacity. These maps are based on all available point information on soils, and on 95 recently analyzed pedological forest soil profiles. Two different methods were applied in parallel: 1) the development of decision trees based on frequency statistics combined with expertise and 2) the semi-automated Random Forest modeling approach. Highly branched hierarchical decision trees were used to derive soil properties from 24 predictors (relief forms, parent material, vegetation, forest type, location, climate, etc.). This article describes the approaches and portrays the mapped results of soil depth, top soil acidity and water storage capacity. Our project shows that it is basically feasible to predict soil properties with a high spatial resolution, classifying them into 4-5 categories. However, depending on soil type, for 10-30% of the area no predictions are possible, especially because of high soil heterogeneity, inadequate morphographic slope differentiation in the terrain models and the implausibility of predictor information. Soil property maps provide basic information for set up forestry maps for forest development, forest management and risk assessment.
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Forestry
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In: Schweizerische Zeitschrift fur Forstwesen, Vol. 164, No. 1, 01.01.2013, p. 10-22.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Flächenhafte Modellierung von Waldbodeneigenschaften in der Nordwestschweiz
AU - Mosimann, Thomas
AU - Herbst, Philipp
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Forest soils are an important natural resource. However, up to now almost no area-wide forest soil information was available for Switzerland. Since 2006, model-based, high-resolution maps of forest soil properties in the cantons Basel-Landschaft and Basel-Stadt were generated, depicting soil depth, stone content, soil wetness, acidity and water storage capacity. These maps are based on all available point information on soils, and on 95 recently analyzed pedological forest soil profiles. Two different methods were applied in parallel: 1) the development of decision trees based on frequency statistics combined with expertise and 2) the semi-automated Random Forest modeling approach. Highly branched hierarchical decision trees were used to derive soil properties from 24 predictors (relief forms, parent material, vegetation, forest type, location, climate, etc.). This article describes the approaches and portrays the mapped results of soil depth, top soil acidity and water storage capacity. Our project shows that it is basically feasible to predict soil properties with a high spatial resolution, classifying them into 4-5 categories. However, depending on soil type, for 10-30% of the area no predictions are possible, especially because of high soil heterogeneity, inadequate morphographic slope differentiation in the terrain models and the implausibility of predictor information. Soil property maps provide basic information for set up forestry maps for forest development, forest management and risk assessment.
AB - Forest soils are an important natural resource. However, up to now almost no area-wide forest soil information was available for Switzerland. Since 2006, model-based, high-resolution maps of forest soil properties in the cantons Basel-Landschaft and Basel-Stadt were generated, depicting soil depth, stone content, soil wetness, acidity and water storage capacity. These maps are based on all available point information on soils, and on 95 recently analyzed pedological forest soil profiles. Two different methods were applied in parallel: 1) the development of decision trees based on frequency statistics combined with expertise and 2) the semi-automated Random Forest modeling approach. Highly branched hierarchical decision trees were used to derive soil properties from 24 predictors (relief forms, parent material, vegetation, forest type, location, climate, etc.). This article describes the approaches and portrays the mapped results of soil depth, top soil acidity and water storage capacity. Our project shows that it is basically feasible to predict soil properties with a high spatial resolution, classifying them into 4-5 categories. However, depending on soil type, for 10-30% of the area no predictions are possible, especially because of high soil heterogeneity, inadequate morphographic slope differentiation in the terrain models and the implausibility of predictor information. Soil property maps provide basic information for set up forestry maps for forest development, forest management and risk assessment.
KW - Digital soil mapping
KW - Forest soil properties
KW - Forest soils
KW - Knowledge-based modeling
UR - http://www.scopus.com/inward/record.url?scp=84879346791&partnerID=8YFLogxK
U2 - 10.3188/szf.2013.0010
DO - 10.3188/szf.2013.0010
M3 - Artikel
AN - SCOPUS:84879346791
VL - 164
SP - 10
EP - 22
JO - Schweizerische Zeitschrift fur Forstwesen
JF - Schweizerische Zeitschrift fur Forstwesen
SN - 0036-7818
IS - 1
ER -