Organisation

May

PhD Defence Ms Elnaz Neinavaz

Department of Natural Resources

Neinavaz

Title of defence

Sensing Vegetation Canopies in the Thermal Domain

Summary

There is an increasing demand for understanding and evaluating the impact of climate change and global warming on the Earth system. Remote sensing as a non-destructive and cost-effective approach is one of the most valuable tools to understanding the ecosystems changes.

This dissertation aims to contribute to understanding the potential of thermal (TIR, 8-14 µm), hyperspectral, and multispectral remote sensing data to quantify two vegetation variables at canopy level: leaf area index (LAI) and vegetation water content in terms of fuel moisture content (FMCC) and equivalent water thickness (EWTC). The research investigated the relation between LAI and emissivity spectra, as well as the applicability of emissivity spectra to retrieve the LAI, FMCC, and EWTC benefiting from empirical models under laboratory controlled conditions over TIR region.

The laboratory experiments demonstrated that there exists a positive correlation between emissivity spectra and LAI. It was concluded that LAI could be successfully predicted at a moderate accuracy using empirical models. The findings revealed that multivariate techniques are more reliable to retrieve LAI in comparison with univariate statistical approaches using TIR data. The results of the study showed that FMCC and EWTC could be successfully estimated through artificial neural networks, and FMCC was more accurately predicted than EWTC. The findings of the research confirmed that TIR remotely sensed data are species-specific regarding prediction of vegetation biophysical as well as biochemical variables at canopy level.

Furthermore, research has moved on the spaceborne level to predict LAI coupled with univariate and machine learning approaches, which have benefitted from TIR multispectral data. The results revealed that LAI could be predicted using land surface emissivity (LSE) with reasonable accuracy. The LSE was found to be correlated with the plot vegetation type. The findings of this thesis confirmed that different vegetation indices achieved similar results for retrieval of LAI using TIR hyperspectral and multispectral data. Lastly, the results showed that the combination of reflectance and emissivity data could improve the prediction accuracy of LAI. This thesis extends the existing knowledge of applied research in vegetation remote sensing studies from the TIR perspective. The findings and approaches of this thesis have confirmed the potential of TIR hyperspectral remote sensing data to generate valuable information regarding vegetation characteristics at canopy level under laboratory conditions. The results further revealed the potential of TIR remote sensing data to retrieve LAI as a primary vegetation biophysical variable over the mixed temperate forest at satellite level. These achievements will improve our knowledge, and enhance our understanding of biodiversity through monitoring and assessment of changes in essential biodiversity variables such as LAI.


Biography

Elnaz Neinavaz was born on 12 May 1982 in Tehran, Iran. She received a B.Sc. Natural Resources Engineering- Environment at the Azad University- North Tehran Branch and obtained her M.Sc. in Natural Resources Engineering- Habitats and Biodiversity from the Azad University- Science and Research Tehran Branch (SRBIAU) . After graduation, she started to collaborate with the United Nations Development Program (UNDP), Global Environment Facility (GEF)/ Small Grant Program (SGP) and Plan for the Land Society (NGO) on the number of the national and international environmental projects for several years. In 2013, she was awarded the European Commission, Erasmus Mundus Scholarship to pursue her doctoral research at the Faculty of Geo- Information Science and Earth Observation (ITC), University of Twente.


 

Timesheet
Event starts: Wednesday 31 May 2017 at 16:30
Venue: UT, Waaier 4
City where event takes place: Enschede

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