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A multi-scale based method for estimating coniferous forest aboveground biomass using low density airborne LiDAR data

Duanmu, Jialong LU (2016) In Lund University GEM thesis series NGEM01 20161
Dept of Physical Geography and Ecosystem Science
Abstract
Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. LiDAR (Light Detection and Ranging) remote sensing is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale.
This study is a new attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately... (More)
Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. LiDAR (Light Detection and Ranging) remote sensing is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale.
This study is a new attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately 50 km northwest of Umeå, Sweden. A method based on local maximum height point identification and downscaling calibration is provided. By implementing local maximum elevation extraction and visualization of aerial images, an algorithm directly based on point cloud data is designed. This algorithm retains more details of the LiDAR data and therefore provides better results. Two calibration look-up tables are provided to approximate the forest AGB derived from low density LiDAR data to the forest AGB derived from high density LiDAR data (more than 1 point/m2). The error of downscaling calibration in the test sample plot is of 0.28%, which proved validity of the method applied. (Less)
Popular Abstract
Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. Recently, LiDAR (Light Detection and Ranging) remote sensing, as a surveying technology measuring distance by illuminating a target with laser light, is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale.
Briefly, scale describes the resolution and extent in which the data is shown. Multi-scale studies aim at analyzing the relationship between data... (More)
Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. Recently, LiDAR (Light Detection and Ranging) remote sensing, as a surveying technology measuring distance by illuminating a target with laser light, is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale.
Briefly, scale describes the resolution and extent in which the data is shown. Multi-scale studies aim at analyzing the relationship between data in different resolution and/or different extent. Multi-scale transform includes downscaling and upscaling. Downscaling is to push down the scale from coarser spatial and temporal resolution into more detailed information with finer spatial and temporal resolution while upscaling is just the opposite. By implementing downscaling and upscaling, multi-scale studies provide possibilities to understand the behavior of variables while changing scale. For forest biomass estimation based on LiDAR data, most of the researches from a viewpoint of multi-scale are about extent. However, the multi-scale based research about resolution is rarely attempted.
This study is an attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately 50 km northwest of Umeå, Sweden. A method based on local maximum height point identification and downscaling calibration is provided. By implementing local maximum elevation extraction and visualization of aerial images, an algorithm directly based on point cloud data is designed. This algorithm retains more details of the LiDAR data and therefore provides better results. Two calibration look-up tables are founded from a viewpoint of downscaling which is widely applied in geomorphology study. By inferring the result extracted from high density LiDAR data (more than 1 point/m2) with low density LiDAR data, the forest parameter estimation accuracy based on low density LiDAR data is improved.
The error of downscaling calibration in the test sample plot is of 0.28%, which proved validity of the method applied. Furthermore, the calibration look-up tables can be used directly in the further researches of the study area in the same situation. (Less)
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author
Duanmu, Jialong LU
supervisor
organization
course
NGEM01 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
individual tree identification, multi-scale, aboveground biomass (AGB), LiDAR, remote sensing, GEM
publication/series
Lund University GEM thesis series
report number
15
language
English
id
8886910
date added to LUP
2016-08-23 10:43:19
date last changed
2016-08-23 10:43:19
@misc{8886910,
  abstract     = {Forest biomass acts as an important indicator of carbon resources in terrestrial system. Estimation of forest biomass enables a straightforward measurement of carbon storage and provides initial values for process-based carbon cycle models to simulate carbon dynamics. LiDAR (Light Detection and Ranging) remote sensing is increasingly used to estimate forest biomass because of its ability to detect the structure of forest. However, it is still not adequately studied from a viewpoint of multi-scale.
 This study is a new attempt for the application of multi-scale theory in forest aboveground biomass (AGB) estimation based on low density LiDAR data (less than 1 point/m2). The study area is located in Krycklan catchment which is approximately 50 km northwest of Umeå, Sweden. A method based on local maximum height point identification and downscaling calibration is provided. By implementing local maximum elevation extraction and visualization of aerial images, an algorithm directly based on point cloud data is designed. This algorithm retains more details of the LiDAR data and therefore provides better results. Two calibration look-up tables are provided to approximate the forest AGB derived from low density LiDAR data to the forest AGB derived from high density LiDAR data (more than 1 point/m2). The error of downscaling calibration in the test sample plot is of 0.28%, which proved validity of the method applied.},
  author       = {Duanmu, Jialong},
  keyword      = {individual tree identification,multi-scale,aboveground biomass (AGB),LiDAR,remote sensing,GEM},
  language     = {eng},
  note         = {Student Paper},
  series       = {Lund University GEM thesis series},
  title        = {A multi-scale based method for estimating coniferous forest aboveground biomass using low density airborne LiDAR data},
  year         = {2016},
}