Comparison of Leaf Area Index Prediction Accuracy using thermal Infrared Data from Unmanned Aerial Vehicle and Satellite Images
Field work or other specific aspects of the research
Haagse Bos, Enschede, the Netherlands
LAI is one of the most critical vegetation biophysical variables as well as remote sensing- enabled essential biodiversity variables due to the controlling role of green leaves in biological and physical processes such as photosynthesis, and evapotranspiration. Moreover, monitoring of the LAI is indispensable as LAI is required as an input for many climates and large-scale ecosystem models. LAI measurement on the ground is not a difficult task but requires a significant amount of labour and hence cost as well as time-consuming, while the UAV platform is considered as a rapid approach for monitoring and estimation of the vegetation parameters as well as cost-effective. Recently, it has been revealed that the LAI can be retrieved using thermal infrared data (i.e., land surface emissivity and land surface temperature) using satellite imagery. However, the potential for of thermal data obtained from the UAV platform for predicting LAI has rarely addressed. Therefore, in this study, we aim to investigate on the prediction accuracy of the LAI and the thermal data retrieved by UAV and compare it with the results obtained by satellite imagery. The use of this new approach allows further investigation for other parameters that impact LAI prediction accuracy.
The objective of research topic is to investigate the difference between prediction accuracy of the impact of thermal infrared data using UAV platform on the retrieval accuracy of leaf area index (LAI) and further compare with satellite imagery. In this respect, the student would carry out the field data collection. The candidate will work with multispectral drone imagery as well as Landsat-8 images. The student is expected to collect in situ data (LAI) using the plant canopy analyzer LAI-2200 (LICOR Inc., Lincoln, NE, USA) and other vegetation structural parameters if it would be necessary. Further, the student will become familiar with the pre-processing and calculating land surface temperature and land surface emissivity data obtained by UAV and Landsat-8. According to the field measurements, multivariate or univariate approaches would be considered, and the results would be validated and analyzed. Eventually, the prediction accuracy of the LAI obtained from UAV and Landsat-8 will be compared.
Neinavaz, E., Darvishzadeh, R., Skidmore, A. K., & Abdullah, H. (2019). Integration of Landsat-8 thermal and visible-short wave infrared data for improving the prediction accuracy of forest leaf area index. Remote sensing, 11(4), 390.