Next Article in Journal
Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation
Next Article in Special Issue
Multi-Sensor Platform for Indoor Mobile Mapping: System Calibration and Using a Total Station for Indoor Applications
Previous Article in Journal
Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data
Previous Article in Special Issue
Enhanced Algorithms for Estimating Tree Trunk Diameter Using 2D Laser Scanner
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2013, 5(10), 5285-5303; doi:10.3390/rs5105285

Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Department of Geography and Geology, University of Turku, FI-20014 Turku, Finland
Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland
Department of Real Estate, Planning and Geoinformatics, Aalto University, FI-00076 Aalto, Finland
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, FI-02431 Masala, Finland
Helsinki Metropolia University of Applied Sciences, FI-00079 Helsinki, Finland
Author to whom correspondence should be addressed.
Received: 6 September 2013 / Revised: 16 October 2013 / Accepted: 16 October 2013 / Published: 22 October 2013
(This article belongs to the Special Issue Advances in Mobile Laser Scanning and Mobile Mapping)
View Full-Text   |   Download PDF [4924 KB, uploaded 19 June 2014]   |  


Vegetation plays an important role in stabilizing the soil and decreasing fluvial erosion. In certain cases, vegetation increases the accumulation of fine sediments. Efficient and accurate methods are required for mapping and monitoring changes in the fluvial environment. Here, we develop an area-based approach for mapping and monitoring the vegetation structure along a river channel. First, a 2 × 2 m grid was placed over the study area. Metrics describing vegetation density and height were derived from mobile laser-scanning (MLS) data and used to predict the variables in the nearest-neighbor (NN) estimations. The training data were obtained from aerial images. The vegetation cover type was classified into the following four classes: bare ground, field layer, shrub layer, and canopy layer. Multi-temporal MLS data sets were applied to the change detection of riverine vegetation. This approach successfully classified vegetation cover with an overall classification accuracy of 72.6%; classification accuracies for bare ground, field layer, shrub layer, and canopy layer were 79.5%, 35.0%, 45.2% and 100.0%, respectively. Vegetation changes were detected primarily in outer river bends. These results proved that our approach was suitable for mapping riverine vegetation.
Keywords: LiDAR; mobile laser scanning (MLS); riverine environment; river bank; vegetation; change detection LiDAR; mobile laser scanning (MLS); riverine environment; river bank; vegetation; change detection

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Saarinen, N.; Vastaranta, M.; Vaaja, M.; Lotsari, E.; Jaakkola, A.; Kukko, A.; Kaartinen, H.; Holopainen, M.; Hyyppä, H.; Alho, P. Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning. Remote Sens. 2013, 5, 5285-5303.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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