Preprint Article Version 1 This version is not peer-reviewed

Monitoring Effects of Drought on Nitrogen and Phosphorus in Temperate Oak Forests Using Machine Learning Techniques

Version 1 : Received: 9 October 2018 / Approved: 9 October 2018 / Online: 9 October 2018 (15:53:56 CEST)

How to cite: Kotlarz, J.; Kubiak, K.; Spiralski, M. Monitoring Effects of Drought on Nitrogen and Phosphorus in Temperate Oak Forests Using Machine Learning Techniques. Preprints 2018, 2018100191 (doi: 10.20944/preprints201810.0191.v1). Kotlarz, J.; Kubiak, K.; Spiralski, M. Monitoring Effects of Drought on Nitrogen and Phosphorus in Temperate Oak Forests Using Machine Learning Techniques. Preprints 2018, 2018100191 (doi: 10.20944/preprints201810.0191.v1).

Abstract

Oak is a European tree species highly sensitive to drought. If declining symptoms appear they are often detectable at the crown (such as dieback) enabling monitoring using aerial images and remote sensing methods. Here, we analyzed the impact of short and long-term drought on oaks located in central Poland, between the years of 2014 and 2017. We used leaf nitrogen (N) and phosphorus (P) concentrations measured in the laboratory, aerial images collected in the range of 460-880 nm and machine learning techniques to estimate nutrient concentrations on the > 4000 oaks growing on gleysoil in the study area. We determined a negative impact on N and P concentrations during both types of drought stress (-23% and 19% for N concentration in leaves; -27% and -10% for P concentration in leaves) and an inconsiderable impact on N:P values (3% increase of N:P ration during short and 7% decrease of N:P ration during long-term drought stress). We found that the long-term drought impact was spatially diverse, possibly depending on the presence of drainage ditches and competing species.

Subject Areas

drought, oak, nitrogen, phosphorus, remote sensing, machine learning

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