A multi-scale based method for estimating coniferous forest aboveground biomass using low density airborne LiDAR data
(2016) In Lund University GEM thesis series NGEM01 20161Dept 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)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8886910
- author
- Duanmu, Jialong LU
- supervisor
- organization
- course
- NGEM01 20161
- year
- 2016
- 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}}, 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}}, }