The Potential of Forest Biomass Inversion Based on Canopy-Independent Structure Metrics Tested by Airborne LiDAR Data

Forest biomass is an important for evaluating forest resources and optimizing efficiency in the forest industry. To improve our ability to estimate the structure parameter in the forest based on canopy-independent structure metrics, we used a suite of structural metrics that relate to three aspects of the forest biomass: DBH、tree height、forest density, and analyzed the relationships between structural metrics derived from airborne lidar scanner data and field measure data. The regression relationship between each structural metrics and mean diameter at breast height (DBH) was calculated for sites located at New York central park. The tree height had the weak correlations with mean DBH (R2=0.482), and the two canopy-independent structure metrics (rumple index, canopy volume) had the stronger correlations with mean DBH than tree height, R2 values were 0.516, 0.532 respectively. However, the correlations were significantly improved when the two canopy-independent metrics were introduced into regression. The canopy and trunk volume had the strongest correlations with mean DBH (R2=0.898), which included information such as tree height, canopy structure and forest density. Our results demonstrate that canopy-independent variables are useful explanatory variables for predicting forest biomass even if tree height can not be obtained.

• Publication year

  2019

• Venue

  IEEE International Geoscience and Remote Sensing Symposium

• Publication date

  2019-07-01

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/IGARSS.2019.8898393
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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