|DOI / URL||link|
|Title (Primary)||Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain|
|Author||Jeong, G.; Oeverdieck, H.; Park, S.J.; Huwe, B.; Ließ, M.;|
|POF III (all)||T31;|
|Keywords||Mountain soil; Soil nutrients; Digital soil mapping; Land potential assessment|
|UFZ wide themes||RU1|
Mountain soils play an essential role in ecosystem management. Assessment of land potentials can provide detailed spatial information particularly concerning nutrient availability. Spatial distributions of topsoil carbon, nitrogen and available phosphorus in mountain regions were identified using supervised learning methods, and a functional landscape analysis was performed in order to determine the spatial soil fertility pattern for the Soyang Lake watershed in South Korea. Specific research aims were (1) to identify important predictors; (2) to develop digital soil maps; (3) to assess land potentials using digital soil maps.
Soil profiles and samples were collected by conditioned Latin Hypercube Sampling considering operational field constraints such as accessibility and no-go areas contaminated by landmines as well as budget limitations. Terrain parameters and different vegetation indices were derived for the covariates. We compared a generalized additive model (GAM) to random forest (RF) and support vector regression (SVR). For the predictor selection, we used the recursive feature elimination (RFE). A land potential assessment for soil nutrients was conducted using trimmed k-mean cluster analysis.
Results suggested that vegetation indices have powerful abilities to predict soil nutrients. Using selected predictors via RFE improved prediction results. RF showed the best performance. Cluster analysis identified four land potential classes: fertile, medium and low fertile with an additional class dominated by high phosphorus and low carbon and nitrogen contents due to human impact. This study provides an effective approach to map land potentials for mountain ecosystem management.
|Persistent UFZ Identifier||http://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=18776|
|Jeong, G., Oeverdieck, H., Park, S.J., Huwe, B., Ließ, M. (2017):
Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain
Catena 154 , 73 - 84