Spatial transitions between slope positions (e.g., shoulder slope, back slope) are often gradual, which reflects the integrative effect and transitional nature of earth surface processes over a slope. Quantification of these transitions (or spatial gradations) is important for terrain-related geographical or ecological modeling (such as modeling of soil erosion and predictive soil mapping at finer scale). Although various methods have been developed to quantify these spatial gradation using fuzzy slope positions, few studies have applied the quantitative information on fuzzy slope positions in geographical or ecological modeling.

In a new study published in the journal Geoderma, Dr. QIN Cheng-Zhi, an associate professor of geographical information science at the State Key Laboratory of Resources & Environmental Information System, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences (LREIS, IGSNRR, CAS), and his colleagues apply such quantitative information directly to predictive soil mapping in small low-relief catchments for the first time.

The new research results are based on Dr. QIN’s 2009 study published in the journal Geomorphology, in which he and his research partners developed an approach to deriving the fuzzy slope positions by using typical locations as prototypes. This approach characterizes slope position gradation in both the parameter space and the geographic space in an easy and practicable way, so the problems in former approaches can be addressed.

The researchers apply fuzzy slope positions of a five slope position system to mapping soil organic matter content (SOM) in a low-relief watershed in north-eastern China within a purposive sampling framework for predictive soil mapping.

First, the fuzzy slope positions were used to direct purposive sampling, which determined the typical SOM value for each slope position type. Secondly, typical SOM values were combined with fuzzy slope positions to predict the spatial distribution of SOM using a weighted-average model (so-called FSPW model).

Quantitative evaluation in the model-development area based on evaluation points show that the performance of the FSPW model with five modeling points from purposive sampling compares favourably with the result from a multiple linear regression (MLR) model with 48 modeling points. Quantitative assessment based on a validation set of 102 sample points in the nearby, larger model-extrapolation area shows that the FSPW model performs better and more portable than the MLR model. The new study suggests that the fuzzy slope positions are useful in aiding predictive soil mapping.

For more details please refer to:

1.　  Qin C-Z, A-X Zhu, W-L Qiu, et al. Mapping soil organic matter in small low-relief catchments using fuzzy slope position information. Geoderma, 2011, doi: 10.1016/j.geoderma.2011.06.006.