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Open AccessCommunication
Remote Sens. 2015, 7(10), 13863-13877; doi:10.3390/rs71013863

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

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.
Academic Editors: Angela Lausch, Randolph H. Wynne and Prasad S. Thenkabail
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)
View Full-Text   |   Download PDF [669 KB, uploaded 22 October 2015]   |  

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. View Full-Text
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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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