Monitoring and process-based modeling of grassland dynamics using UAV imagery and crop modeling
About Clara Bazzo
Clara is a PhD Student at university of Bonn, Bonn, Germany. She is graduated from the University of Kurdistan, Iran in Civil Engineering-Water Resources Management-MSc level in December2018. Her master dissertation entitled “Assessment of Local Scour Hole Dimension around Bridge Piers with Complex Geometry”.
About the project
Her PhD thesis is part of DAKIS-grassland project. The project is mainly led by Dr. Thomas Gaiser and Ms. Clara Bazzo, as the PhD student works on this project. More information on this project is found in the section of PhD supervision.
Given the heterogeneity of grassland fields, continuous changes in height and phenology of plant, together with high variability in soil and weather conditions, the productivity of different sites vary due to local scale responses. This emphasizes the necessity for grassland productivity models that can be run at small spatial and temporal scales (provided soil input data are available at highest spatial resolution possible), thus making long-term grassland productivity predictions as informative as possible. Remote sensing is an important recent and viable tool for measuring and estimating biophysical parameters at an appropriate scale to grassland distribution. However, image-based modelling approach with UAV imagery for estimating forage biomass and quality are a new area of research and there are still no established or validated grassland models for simulating the dynamics of pasture biomass at small-scales. In view of this, the first part of this work aims to fill this gap in the research field by presenting a protocol for
- Evaluate the use of remote sensingRS data (mainly UAV based sensors) to quantify small-scale variability of biomass production and feed quality of grassland vegetation
- Develop a grassland model solution in the framework of SIMPLACE that is able to simulate grassland dynamics (biomass and N-content) under the effects of small-scale variability of soil and groundwater conditions
- Improve the performance of the model and reducing grassland model uncertainty with respect to the simulation of small-scale variability of biomass production by the assimilation of the remotely sensedRS data.
Location of education
University of Bonn
From july 2021 to July 2024
- Primary professor: Dr. Thomas Gaiser
- Advisor: Dr. Bahareh Kamali
- Funded by: Federal Ministry of Education and Research (BMBF) under DAKIS project