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Volume 7
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10.3390/rs71013863
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Communication

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

by
Samuli Junttila
1,2,*,
Sanna Kaasalainen
3,
Mikko Vastaranta
1,2,
Teemu Hakala
3,
Olli Nevalainen
3 and
Markus Holopainen
1,2
1
Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland
2
Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute (FGI), FI-02431 Masala, Finland
3
Finnish Geospatial Research Institute (FGI), FI-02431 Masala, Finland
*
Author to whom correspondence should be addressed.
Remote Sens. 2015, 7(10), 13863-13877; https://doi.org/10.3390/rs71013863
Submission received: 6 July 2015 / Revised: 7 October 2015 / Accepted: 16 October 2015 / Published: 22 October 2015
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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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.
Keywords: Lidar; hyperspectral sensors; forest health; drought; declined trees; laser scanning; forestry Lidar; hyperspectral sensors; forest health; drought; declined trees; laser scanning; forestry
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MDPI and ACS Style

Junttila, S.; Kaasalainen, S.; Vastaranta, M.; Hakala, T.; Nevalainen, O.; Holopainen, M. Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test. Remote Sens. 2015, 7, 13863-13877. https://doi.org/10.3390/rs71013863

AMA Style

Junttila S, Kaasalainen S, Vastaranta M, Hakala T, Nevalainen O, Holopainen M. Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test. Remote Sensing. 2015; 7(10):13863-13877. https://doi.org/10.3390/rs71013863

Chicago/Turabian Style

Junttila, Samuli, Sanna Kaasalainen, Mikko Vastaranta, Teemu Hakala, Olli Nevalainen, and Markus Holopainen. 2015. "Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test" Remote Sensing 7, no. 10: 13863-13877. https://doi.org/10.3390/rs71013863

APA Style

Junttila, S., Kaasalainen, S., Vastaranta, M., Hakala, T., Nevalainen, O., & Holopainen, M. (2015). Investigating Bi-Temporal Hyperspectral Lidar Measurements from Declined Trees—Experiences from Laboratory Test. Remote Sensing, 7(10), 13863-13877. https://doi.org/10.3390/rs71013863

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