Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea

DOI zum Zitieren dieses Dokuments: https://doi.org/10.1371/journal.pone.0183205

Title data

Jeong, Gwan Yong ; Choi, Kwanghun ; Spohn, Marie ; Park, Soo Jin ; Huwe, Bernd ; Ließ, Mareike:
Environmental drivers of spatial patterns of topsoil nitrogen and phosphorus under monsoon conditions in a complex terrain of South Korea.
In: PLOS ONE. Vol. 12 (2017) Issue 8 . - e0183205.
ISSN 1932-6203
DOI: https://doi.org/10.1371/journal.pone.0183205

[img] PDF
journal.pone.0183205.pdf - Published Version
Available under License Creative Commons BY 4.0: Attribution .

Download (9MB)

Project information

Project title:
Project's official titleProject's id
Open Access PublizierenNo information

Abstract

Nitrogen (N) and phosphorus (P) in topsoils are critical for plant nutrition. Relatively little isknown about the spatial patterns of N and P in the organic layer of mountainous landscapes. Therefore, the spatial distributions of N and P in both the organic layer and the A horizon were analyzed using a light detection and ranging (LiDAR) digital elevation model and vegetation metrics. The objective of the study was to analyze the effect of vegetation and topographyon the spatial patterns of N and P in a small watershed covered by forest in South Korea. Soil samples were collected using the conditioned latin hypercube method. LiDAR vegetation metrics, the normalized difference vegetation index (NDVI), and terrain parameters were derived as predictors. Spatial explicit predictions of N/P ratios were obtained using a random forest with uncertainty analysis. We tested different strategies of model validation (repeated 2-fold to 20-fold and leave-one-out cross validation). Repeated 10-fold cross validation was selected for model validation due to the comparatively high accuracy and low variance of prediction. Surface curvature was the best predictor of P contents in the organic layer and in the A horizon, while LiDAR vegetation metrics and NDVI were important predictors of N in the organic layer. N/P ratios increased with surface curvature and were higher on the convex upper slope than on the concave lower slope. This was due to P enrichment of the soil on the lower slope and a more even spatial distribution of N. Our digital soil maps showed that the topsoils on the upper slopes contained relatively little P. These findings are critical for understanding N and P dynamics in mountainous ecosystems.

Further data

Item Type: Article in a journal
Additional notes (visible to public): BAYCEER141880
DDC Subjects: 500 Science
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology > Chair Plant Ecology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Soil Ecology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Chair Soil Physics
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Former Professors > Professor Soil Physics - Univ.-Prof. Dr. Bernd Huwe
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Biogeographical Modelling
Research Institutions
Research Institutions > Research Centres
Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER
Faculties
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Biology
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Former Professors
Language: English
Originates at UBT: Yes
Date Deposited: 20 Aug 2018 09:37
Last Modified: 26 Feb 2019 11:02
URI: https://epub.uni-bayreuth.de/id/eprint/3788

Downloads

Downloads per month over past year