基于ICESat-2光斑尺度外推建模的不同森林类型地上生物量反演

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

摘要/Abstract

摘要： 森林地上生物量是衡量森林生态系统固碳能力和生产力的重要指标,利用遥感手段可以快速完成连续、大规模的森林地上生物量反演与制图,建立外推模型且更加精准地估算是遥感反演森林生物量的关键。本文以湖北省崇阳县为研究对象,通过星载激光雷达ICESat-2数据与不同类型森林地上生物量建模,结合多光谱Landsat8卫星影像,利用多元线性回归和随机森林算法,完成该地区2013—2023年森林生长季30m分辨率时间序列地上生物量制图。结果显示,ICESat-2 ATL08数据可以较好地估算光斑点生物量,针叶林R²=0.872,阔叶林R²=0.806。试验验证了星载激光雷达对不同森林类型生物量的估算能力,对未来大区域尺度森林生物量及碳汇时空变化格局研究提供了方法和依据。

关键词: 地上生物量, ICESat-2, 遥感估测, 森林类型

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

中图分类号:

P237

杜洁, 史硕, 刘晨曦. 基于ICESat-2光斑尺度外推建模的不同森林类型地上生物量反演[J]. 测绘通报, 2024, 0(10): 32-38.

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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