Extrapolation modeling of aboveground biomass for different forest types based on ICESat-2 footprint scale

doi:10.13474/j.cnki.11-2246.2024.1006.

Abstract

Abstract: Forest above ground biomass is an important index to measure the carbon sequestration capacity and productivity of forest ecosystem. Rapid, continuous and large-scale inversion and mapping of forest above ground biomass could be accomplished by remote sensing. How to establish an extrapolation model to estimate forest biomass more accurately is the key to remote sensing inversion of forest biomass. Taking Chongyang county, Hubei province as the research object, the above ground biomass mapping of forest growing season from 2013 to 2023 with 30m resolution time series was completed by using spaceborne LiDAR ICESat-2 data and different types of forest biomass modeling, combined with multi-spectral Landsat 8 satellite images, and using multiple linear regression and random forest algorithm. The results show that ICESat-2 ATL08 data could estimate the spot biomass well, and the R² of coniferous forest is 0.872, and the R² of broadleaf forest is 0.806. The experiment verifies the ability of space-borne LiDAR to estimate the biomass of different forest types, and provides a method and basis for the future study of the spatio-temporal change pattern of forest biomass and carbon sink at large regional scales.

Key words: above ground biomass, ICESat-2, remote sensing estimation, forest type

CLC Number:

P237

DU Jie, SHI Shuo, LIU Chenxi. Extrapolation modeling of aboveground biomass for different forest types based on ICESat-2 footprint scale[J]. Bulletin of Surveying and Mapping, 2024, 0(10): 32-38.

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