Improving the estimate of forest biomass carbon storage by combining two forest inventory systems

Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present biomass carbon storage in Alberta, Canada, by taking advantage of a spatially explicit dataset derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (LiDAR) canopy height data. Ten climatic variables together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree based modelling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total biomass stock in Alberta forests was estimated to be 3.11 × 109 Mg, with the average biomass density of 77.59 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.91 × 109 Mg biomass, accounting for 61% of total estimated biomass. Spatial distribution of biomass varied with natural regions, land cover types, and species. And the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne LiDAR data, land cover classification, climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

Download Full-text

Estimating aboveground forest biomass carbon and fire consumption in the U.S. Utah High Plateaus using data from the Forest Inventory and Analysis Program, Landsat, and LANDFIRE

Ecological Indicators ◽

10.1016/j.ecolind.2009.03.013 ◽

2011 ◽

Vol 11 (1) ◽

pp. 140-148 ◽

Cited By ~ 17

Author(s):

Xuexia Chen ◽

Shuguang Liu ◽

Zhiliang Zhu ◽

James Vogelmann ◽

Zhengpeng Li ◽

...

Keyword(s):

Forest Inventory ◽

Forest Biomass ◽

Biomass Carbon ◽

Analysis Program ◽

Forest Inventory And Analysis ◽

Using Data ◽

The U.S

Download Full-text

Forest biomass carbon pool dynamics in Tibet Autonomous Region of China: Inventory data 1999-2019

PLoS ONE ◽

10.1371/journal.pone.0250073 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0250073

Author(s):

Liu Shu-Qin ◽

Bian Zhen ◽

Xia Chao-Zong ◽

Bilal Ahmad ◽

Zhang Ming ◽

...

Keyword(s):

Carbon Storage ◽

Forest Biomass ◽

Forest Area ◽

Total Biomass ◽

Biomass Carbon ◽

Inventory Data ◽

Autonomous Region ◽

Pinus Densata ◽

Tibet Autonomous Region ◽

Picea Asperata

According to the forest resources inventory data for different periods and the latest estimation parameters of forest carbon reserves in China, the carbon reserves and carbon density of forest biomass in the Tibet Autonomous Region from 1999 to 2019 were estimated using the IPCC international carbon reserves estimation model. The results showed that, during the past 20 years, the forest area, forest stock, and biomass carbon storage in Tibet have been steadily increasing, with an average annual increase of 1.85×104 hm2, 0.033×107 m3, and 0.22×107 t, respectively. Influenced by geographical conditions and the natural environment, the forest area and biomass carbon storage gradually increased from the northwest to the southeast, particularly in Linzhi and Changdu, where there are many primitive forests, which serve as important carbon sinks in Tibet. In terms of the composition of tree species, coniferous forests are dominant in Tibet, particularly those containing Abies fabri, Picea asperata, and Pinus densata, which comprise approximately 45% of the total forest area in Tibet. The ecological location of Tibet has resulted in the area being dominated by shelter forest, comprising 68.76% of the total area, 64.72% of the total forest stock, and 66.34% of the total biomass carbon reserves. The biomass carbon storage was observed to first increase and then decrease with increasing forest age, which is primarily caused by tree growth characteristics. In over-mature forests, trees’ photosynthesis decreases along with their accumulation of organic matter, and the trees can die. In addition, this study also observed that the proportion of mature and over-mature forest in Tibet is excessively large, which is not conducive to the sustainable development of forestry in the region. This problem should be addressed in future management and utilization activities.

Download Full-text

Spatiotemporal Distribution and Driving Factors of Forest Biomass Carbon Storage in China: 1977–2013

Forests ◽

10.3390/f8070263 ◽

2017 ◽

Vol 8 (7) ◽

pp. 263 ◽

Cited By ~ 5

Author(s):

Jiameng Yang ◽

Xiaoxia Ji ◽

David Deane ◽

Linyu Wu ◽

Shulin Chen

Keyword(s):

Carbon Storage ◽

Forest Biomass ◽

Driving Factors ◽

Spatiotemporal Distribution ◽

Biomass Carbon

Download Full-text

Estimating spatial variation in Alberta forest biomass from a combination of forest inventory and remote sensing data

Biogeosciences ◽

10.5194/bg-11-2793-2014 ◽

2014 ◽

Vol 11 (10) ◽

pp. 2793-2808 ◽

Cited By ~ 30

Author(s):

J. Zhang ◽

S. Huang ◽

E. H. Hogg ◽

V. Lieffers ◽

Y. Qin ◽

...

Keyword(s):

Land Cover ◽

Random Forests ◽

Forest Inventory ◽

Regression Models ◽

Spatial Interpolation ◽

Spatial Regression ◽

Forest Biomass ◽

Biomass Carbon ◽

Inventory Data ◽

Spatial Regression Models

Abstract. Uncertainties in the estimation of tree biomass carbon storage across large areas pose challenges for the study of forest carbon cycling at regional and global scales. In this study, we attempted to estimate the present aboveground biomass (AGB) in Alberta, Canada, by taking advantage of a spatially explicit data set derived from a combination of forest inventory data from 1968 plots and spaceborne light detection and ranging (lidar) canopy height data. Ten climatic variables, together with elevation, were used for model development and assessment. Four approaches, including spatial interpolation, non-spatial and spatial regression models, and decision-tree-based modeling with random forests algorithm (a machine-learning technique), were compared to find the "best" estimates. We found that the random forests approach provided the best accuracy for biomass estimates. Non-spatial and spatial regression models gave estimates similar to random forests, while spatial interpolation greatly overestimated the biomass storage. Using random forests, the total AGB stock in Alberta forests was estimated to be 2.26 × 109 Mg (megagram), with an average AGB density of 56.30 ± 35.94 Mg ha−1. At the species level, three major tree species, lodgepole pine, trembling aspen and white spruce, stocked about 1.39 × 109 Mg biomass, accounting for nearly 62% of total estimated AGB. Spatial distribution of biomass varied with natural regions, land cover types, and species. Furthermore, the relative importance of predictor variables on determining biomass distribution varied with species. This study showed that the combination of ground-based inventory data, spaceborne lidar data, land cover classification, and climatic and environmental variables was an efficient way to estimate the quantity, distribution and variation of forest biomass carbon stocks across large regions.

Download Full-text