Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees - Experiences from Laboratory Test

Samuli Junttila, Sanna Kaasalainen, Mikko Vastaranta, Teemu Hakala, Olli Nevalainen, Markus Holopainen

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)

Abstract

Global warming is posing a threat to the health and condition of forests as the amount and length of biotic and abiotic disturbances increase. Most methods for detecting disturbances and measuring forest health are based on multi- and hyperspectral imaging. We conducted a test with spruce and pine trees using a hyperspectral Lidar instrument in a laboratory to determine the capability of combined range and reflectance measurements to investigate forest health. A simple drought treatment was conducted by leaving the harvested trees outdoors without a water supply for 12 days. The results showed statistically significant variation in reflectance after the drought treatment for both species. However, the changes differed between the species, indicating that drought-induced alterations in spectral characteristics may be species-dependent. Based on our results, hyperspectral Lidar has the potential to detect drought in spruce and pine trees.

Original languageEnglish
JournalRemote Sensing
Volume7
Issue number10
DOIs
Publication statusPublished - Oct 2015
MoE publication typeA1 Journal article-refereed

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