14:30 〜 14:45
[ACG37-04] 18-year variation of forest aboveground biomass estimated by multiple periods of airborne lidar in a cool-temperate forest in Northern Japan
キーワード：carbon cycle, aboveground forest biomass
Forests act as a net sink for atmospheric carbon and provide ecosystem services, crucial in climate change. Living woody biomass is the largest biomass carbon pool, and its variations are significant in the global carbon cycle. Previous studies have extensively examined the distribution of aboveground forest biomass. However, studies on the temporal changes in aboveground biomass over multiple years are relatively scarce. Various methods exist for estimating aboveground biomass, among which lidar offers relatively high-resolution and high-accuracy estimates. This paper utilizes lidar data to estimate the aboveground biomass of forests in northern Japan over four distinct periods within 18 years. It also employs regression analysis to investigate the relationships between factors such as topography, forest structure, species, forest density, and the increase in aboveground biomass. Our research aims to quantify the dynamics of forest biomass and to gain a deeper understanding of the impact of various factors on the carbon cycling of forest systems in northern Japan.
The research area is located in the Tomakomai Experimental Forest, a forest in Hokkaido, northern Japan, covering approximately 2,700 hectares. This study collected airborne lidar data from the Tomakomai region for four years: 2004, 2008, 2014, and 2022. The LiDAR data from 2004 had an average resolution of approximately 0.25 points per square meter, collected in September and sourced from the National Institute for Environmental Studies. The 2008 LiDAR dataset exhibited an average resolution of about 5.21 points per square meter, with data acquisition occurring in October and November, provided by Pasco Co., Ltd. Sapporo Branch. The LiDAR data from 2014, gathered in October, featured an average resolution of approximately 23.25 points per square meter and was sourced from Aero Asahi Corporation. The 2022 LiDAR dataset, acquired by UAV in August, showed an average resolution of about 208.91 points per square meter, with the data obtained from the Field Science Center for Northern Biosphere, Hokkaido University. Additionally, visible light photographs corresponding to the times of the lidar data collection were obtained.
The methodology for calculating the dynamic changes in aboveground biomass across multiple years is divided into the following key steps: (1) Calculation of the Digital Surface Model (DSM), (2) Calculation of the Digital Terrain Model (DTM), (3) Generation of the Digital Canopy Height Model (DCHM) for each period, and (4) Estimation of aboveground biomass for each period and the quantification of changes between periods using allometric growth functions.
The key steps for analyzing regional attribute influences on forest aboveground biomass growth are: (1) A digital elevation model was generated using lidar data, which further facilitated the creation of maps depicting slope and maximum solar radiation. Distribution maps for coniferous and deciduous forests were produced using deep learning algorithms applied to visible light imagery. Forest structural parameters and individual tree identification were conducted using lidar data. Furthermore, tree density was calculated based on the results of individual tree identification. (2) Conduct a regression analysis to examine the influence of variables such as topography, forest structure, species composition, and density on aboveground biomass growth. (3) Apply multivariate linear correlation with these variables and biomass growth data to develop a forest aboveground biomass growth model.
Preliminary results indicate that from 2004 to 2022, the average growth rate of trees was 0.21 meters per year, with an average annual aboveground biomass growth rate of 2.19 tons per hectare. The multivariate linear regression analysis identified the proportion of coniferous and broadleaf forests, forest structure parameters, and maximum solar radiation as the three most significant variables for forest growth.
The research area is located in the Tomakomai Experimental Forest, a forest in Hokkaido, northern Japan, covering approximately 2,700 hectares. This study collected airborne lidar data from the Tomakomai region for four years: 2004, 2008, 2014, and 2022. The LiDAR data from 2004 had an average resolution of approximately 0.25 points per square meter, collected in September and sourced from the National Institute for Environmental Studies. The 2008 LiDAR dataset exhibited an average resolution of about 5.21 points per square meter, with data acquisition occurring in October and November, provided by Pasco Co., Ltd. Sapporo Branch. The LiDAR data from 2014, gathered in October, featured an average resolution of approximately 23.25 points per square meter and was sourced from Aero Asahi Corporation. The 2022 LiDAR dataset, acquired by UAV in August, showed an average resolution of about 208.91 points per square meter, with the data obtained from the Field Science Center for Northern Biosphere, Hokkaido University. Additionally, visible light photographs corresponding to the times of the lidar data collection were obtained.
The methodology for calculating the dynamic changes in aboveground biomass across multiple years is divided into the following key steps: (1) Calculation of the Digital Surface Model (DSM), (2) Calculation of the Digital Terrain Model (DTM), (3) Generation of the Digital Canopy Height Model (DCHM) for each period, and (4) Estimation of aboveground biomass for each period and the quantification of changes between periods using allometric growth functions.
The key steps for analyzing regional attribute influences on forest aboveground biomass growth are: (1) A digital elevation model was generated using lidar data, which further facilitated the creation of maps depicting slope and maximum solar radiation. Distribution maps for coniferous and deciduous forests were produced using deep learning algorithms applied to visible light imagery. Forest structural parameters and individual tree identification were conducted using lidar data. Furthermore, tree density was calculated based on the results of individual tree identification. (2) Conduct a regression analysis to examine the influence of variables such as topography, forest structure, species composition, and density on aboveground biomass growth. (3) Apply multivariate linear correlation with these variables and biomass growth data to develop a forest aboveground biomass growth model.
Preliminary results indicate that from 2004 to 2022, the average growth rate of trees was 0.21 meters per year, with an average annual aboveground biomass growth rate of 2.19 tons per hectare. The multivariate linear regression analysis identified the proportion of coniferous and broadleaf forests, forest structure parameters, and maximum solar radiation as the three most significant variables for forest growth.