Next Article in Journal
Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis
Previous Article in Journal
Object-Based Assessment of Satellite Precipitation Products
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 548; doi:10.3390/rs8070548

Discrimination between Ground Vegetation and Small Pioneer Trees in the Boreal-Alpine Ecotone Using Intensity Metrics Derived from Airborne Laser Scanner Data

Department of Ecology and Natural Resource Management, P.O. Box 5003, N-1432 Ås, Norway
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 16 April 2016 / Revised: 15 June 2016 / Accepted: 22 June 2016 / Published: 28 June 2016
View Full-Text   |   Download PDF [7588 KB, uploaded 28 June 2016]   |  

Abstract

It has been shown that height measurements obtained by airborne laser scanning (ALS) with high point density (>7–8 m−2) can be used to detect small trees in the alpine tree line—an ecotone sensitive to climate change. Because the height measurements do not discriminate between trees and other convex structures with positive height values, this study aimed at assessing the contribution of ALS backscatter intensity to classification of trees and non-trees. The study took place in a boreal-alpine ecotone in southeastern Norway and was based on 500 precisely georeferenced small trees and non-tree objects for which ALS height and intensity were derived from four different ALS acquisitions, representing different sensors, pulse repetition frequencies (PRF), and flying altitudes. The sensors operated at 1064 nm. Based on logistic regression modeling, it was found that classification into three different tree species ((1) spruce; (2) pine; and (3) birch)) and two different non-tree object types (objects with: (1) vegetated surface; and (2) rock) was significantly better (p < 0.001–0.05) than a classification based on models with trees and non-trees as binary response. The cause of the improved classification is mainly diverse reflectivity properties of non-tree objects. No effect of sensor, PRF, and flying altitude was found (p > 0.05). Finally, it was revealed that in a direct comparison of the contribution of intensity backscatter to improve classification models of trees and non-trees beyond what could be obtained by using the ALS height information only, the contribution of intensity turned out to be far from significant (p > 0.05). In conclusion, ALS backscatter intensity seems to be of little help in classification of small trees and non-trees in the boreal-alpine ecotone even when a more detailed discrimination on different species and different non-tree structures is applied. View Full-Text
Keywords: forest monitoring; laser scanning; small trees; alpine tree line; backscatter intensity; sensor effects forest monitoring; laser scanning; small trees; alpine tree line; backscatter intensity; sensor effects
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Næsset, E. Discrimination between Ground Vegetation and Small Pioneer Trees in the Boreal-Alpine Ecotone Using Intensity Metrics Derived from Airborne Laser Scanner Data. Remote Sens. 2016, 8, 548.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top