Fine-resolution mapping of microforms of a boreal bog using aerial images and waveform-recording LiDAR

Show full item record



Permalink

http://hdl.handle.net/10138/313788

Citation

Korpela , I , Haapanen , R , Korrensalo , A , Tuittila , E-S & Vesala , T 2020 , ' Fine-resolution mapping of microforms of a boreal bog using aerial images and waveform-recording LiDAR ' , Mires and Peat , vol. 26 , 03 . https://doi.org/10.19189/MaP.2018.OMB.388

Title: Fine-resolution mapping of microforms of a boreal bog using aerial images and waveform-recording LiDAR
Author: Korpela, Ilkka; Haapanen, R.; Korrensalo, A.; Tuittila, E-S; Vesala, T.
Contributor: University of Helsinki, Ilkka Korpela / Principal Investigator
University of Helsinki, Department of Forest Sciences
University of Helsinki, University of Eastern Finland
University of Helsinki, Institute for Atmospheric and Earth System Research (INAR)
Date: 2020
Language: eng
Number of pages: 24
Belongs to series: Mires and Peat
ISSN: 1819-754X
URI: http://hdl.handle.net/10138/313788
Abstract: Boreal bogs are important stores and sinks of atmospheric carbon whose surfaces are characterised by vegetation microforms. Efficient methods for monitoring their vegetation are needed because changes in vegetation composition lead to alteration in their function such as carbon gas exchange with the atmosphere. We investigated how airborne image and waveform-recording LiDAR data can be used for 3D mapping of microforms in an open bog which is a mosaic of pools, hummocks with a few stunted pines, hollows, intermediate surfaces and mud-bottom hollows. The proposed method operates on the bog surface, which is reconstructed using LiDAR. The vegetation was classified at 20 cm resolution. We hypothesised that LiDAR data describe surface topography, moisture and the presence and depth of field-layer vegetation and surface roughness; while multiple images capture the colours and texture of the vegetation, which are influenced by directional reflectance effects. We conclude that geometric LiDAR features are efficient predictors of microforms. LiDAR intensity and echo width were specific to moisture and surface roughness, respectively. Directional reflectance constituted 4-34 % of the variance in images and its form was linked to the presence of the field layer. Microform-specific directional reflectance patterns were deemed to be of marginal value in enhancing the classification, and RGB image features were inferior to LiDAR variables. Sensor fusion is an attractive option for fine-scale mapping of these habitats. We discuss the task and propose options for improving the methodology.
Subject: airborne laser scanning
bi-directional reflectance
multi-image
peatland remote sensing
spatial variability
vegetation
3D sensor fusion
INTENSITY DATA
AIRBORNE
DYNAMICS
REFLECTANCE
CLASSIFICATION
NORMALIZATION
VEGETATION
HOLOCENE
RANGE
4112 Forestry
119 Other natural sciences
Rights:


Files in this item

Total number of downloads: Loading...

Files Size Format View
map_26_03.pdf 3.658Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record