饶昕, 舒清态, 王继雄, 等. 基于ICESat-2/ATLAS的景东彝族自治县森林生物量估测研究[J]. 西南林业大学学报(自然科学), 2025, 45(1): 1–10. DOI: 10.11929/j.swfu.202401017
引用本文: 饶昕, 舒清态, 王继雄, 等. 基于ICESat-2/ATLAS的景东彝族自治县森林生物量估测研究[J]. 西南林业大学学报(自然科学), 2025, 45(1): 1–10. DOI: 10.11929/j.swfu.202401017
Rao Xin, Shu Qingtai, Wang Jixiong, Luo Shaolong, Yang Zhengdao. Estimation of Forest Biomass based on ICESat-2/ATLAS Data in Jingdong[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202401017
Citation: Rao Xin, Shu Qingtai, Wang Jixiong, Luo Shaolong, Yang Zhengdao. Estimation of Forest Biomass based on ICESat-2/ATLAS Data in Jingdong[J]. Journal of Southwest Forestry University. DOI: 10.11929/j.swfu.202401017

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基于ICESat-2/ATLAS的景东彝族自治县森林生物量估测研究

Estimation of Forest Biomass based on ICESat-2/ATLAS Data in Jingdong

  • 摘要: 以云南省景东彝族自治县为研究区,星载激光雷达ICESAT-2/ATLAS为主要信息源,在对ATLAS数据进行去噪和分类的基础上,基于地统计学的克里格插值实现ATLAS光子参数指标点由“点”到“面”的空间扩展,结合地面265块生物量调查样地,建立研究区森林生物量估测模型。结果表明:基于随机森林重要性排序,ATLAS光子与森林生物量具有较强相关性的5个参数为最大冠层高度、平均冠层高度、光子关联参数、太阳高度角、太阳方向角。对5个参数进行变异函数分析,根据决定系数和空间自相关性选择最优变异函数模型,最大冠层高度、太阳高度角、太阳方向角3种参数以球状模型进行空间插值效果最优,平均冠层高度、光子关联参数2种参数以指数模型效果最优。以地面265块样地地上生物量为被解释变量,对应的5种参数为解释变量,基于随机森林回归,建立了研究区森林生物量遥感估测模型,建模精度R2=0.7941,RMSE=23.0047 t/hm2,可作为研究区森林地上生物量估测模型。基于验证后的RF模型估测研究区森林生物总量,估计值为31269874.76 t,估测精度为85.3%,与实际计算结果空间分布基本一致,表明基于ICESAT-2/ATLAS数据进行森林生物量估测有较好效果。

     

    Abstract: In this study, Jingdong Yi Autonomous County of Yunnan Province was selected as the research area, and space-borne LiDAR ICESAT-2/ATLAS was used as the main information source. On the basis of denoising and classifying ATLAS data, Kriging interpolation based on geostatistics realized the spatial expansion of the index points of ATLAS spot parameters from "point" to "surface". A forest biomass estimation model was established based on 265 biomass survey plots. The results showed as follows: Based on random forest importance ranking, the 5 parameters of ATLAS light spot with strong correlation with forest biomass are as follows: h_max_canopy_abs,h_mean_canopy, ph_ndx_beg, solar_elevation, and solar_azimuth. Variance function analysis was performed on the 5 parameters, and the optimal variation function model is selected according to the determination coefficient and spatial autocorrelation The spatial interpolation effect of the three parameters h_max_canopy_abs, solar_elevation and solar_azimuth was the best using the spherical model , h_mean_canopy and ph_ndx_beg had better effects with the exponential model. Based on random forest regression, a remote sensing estimation model of forest biomass in the study area was established, with modeling accuracy R2=0.7941 and RMSE=23.0047 t/hm2, taking the above-ground biomass of 265 plots as explained variables and corresponding 5 parameters as explanatory variables. The model can be used as an estimation model of forest above-ground biomass in the study area. The forest biomass in the study area was estimated based on the verified RF model, and the estimated value was 31269874.76 t. The forest biomass calculated by the 2019 ground survey group in the study area was 26674465.55 t as the reference truth value, with an estimated accuracy of 85.3%,The spatial distribution is basically consistent with the actual calculation results.The results showed that the forest biomass estimation based on ICESAT-2/ATLAS data had a good effect.

     

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