Visiting lecture by prof. Arturo Sanchez-Azofeifa

2017-06-21 14:00:00 2017-06-21 15:00:00 Europe/Helsinki Visiting lecture by prof. Arturo Sanchez-Azofeifa Dr. Arturo Sanchez-Azofeifa -The role of hyperspectral remote sensing to estimate the extend of Lianas in tropical environments http://eng.aalto.fi/en/midcom-permalink-1e75192c4fb0fcc519211e7b217016ef060abd8abd8 Rakentajanaukio 4 A, 02150, Espoo

Dr. Arturo Sanchez-Azofeifa -The role of hyperspectral remote sensing to estimate the extend of Lianas in tropical environments

21.06.2017 / 14:00 - 15:00
Lecture hall R2, Rakentajanaukio 4 A, 02150, Espoo, FI

Visting lecture at the Department of Civil Engineering by professor Arturo Sanchez-Azofeifa, Director, Alberta Centre for Earth Observation Sciences (CEOS) University of Alberta, Canada https://www.researchgate.net/profile/GA_Sanchez-Azofeifa

Title: The role of hyperspectral remote sensing to estimate the extend of Lianas in tropical environments

Host: Professor Jussi Leveinen, p. 040 723 2215

Please forward on to anyone who might be interested.

Abstract:

In the context of the impacts of climate change on tropical regions, the increase on the dominance of lianas is considered one of the top ten dominant fingerprints. This fingerprint is more significant in tropical dry forests than in rainforests since dry ecosystems have higher Liana diversity than any other tropical ecosystem.  In general, efforts to estimate liana dominance are based on field studies that require time and are expensive; therefore new techniques to monitor their extent and increased dominance are necessary.  In this regard, hyperspectral remote sensing can play a role in emerging monitoring efforts. In this presentation, an evaluation of current theories of liana dominance in the tropics is first presented.  Second, a discussion of the mechanisms controlling hyperspectral responses of liana and tree leaves at the cell, leaf and landscape level is discussed.  Hyperspectral information on the visible, near-infrared, short-wave infrared, and thermal infrared is evaluated. Finally, an exploration of the role of multi-spectral remote sensing cameras mounted on drones, and the integration of this technology with machine learning techniques to estimate liana dominance in tropical dry forest ecosystems is discussed