兰州大学机构库 >资源环境学院
基于GEDI的高寒山地森林生物量估算:以祁连山国家公园为例
Alternative TitleForest biomass estimation in alpine mountain based on GEDI: the case of Qilian Mountain National Park
陈思文
Subtype硕士
Thesis Advisor年雁云
2023-05-26
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name理学硕士
Degree Discipline地图学与地理信息系统
KeywordGEDI GEDI UAV-LiDAR UAV-LiDAR Sentinel-1和Landsat 8 OLI Sentinel-1 and Landsat 8 OLI 随机森林 Random Forest 地理加权回归 Geographically Weighted Regression
Abstract

准确估算森林地上生物量对区域乃至全球碳循环,减缓气候变化等方面至关重要。山地森林因为其地形复杂、难以展开人力调查,森林结构参数和生物量的估算均存在较大的不确定性。为了在大尺度的山地森林反演得到连续的生物量制图,往往需要借助主动或被动遥感数据,利用合适的模型实现将样地生物量外推。星载激光雷达GEDI作为可以提取森林三维结构信息的大光斑主动遥感技术,能够提高大尺度森林生物量的估算精度。本文以祁连山国家公园为研究区,使用了地面调查数据、UAV-LiDAR数据、GEDI数据、Sentinel-1和Landsat 8 OLI影像数据,分别利用了地理加权回归和随机森林模型外推估算了研究区内的森林生物量并评估了精度。本文的主要结论有:

(1)在样方尺度上,UAV-LiDAR点云中提取的森林结构参数和点云特征参数可作为地面实测数据的替代,准确预测森生物量(R2=0.84,RMSE=44.95Mg/ha,rRMSE=26.97%)。GEDI数据对于提高大尺度的森林生物量预测精度是有效的(R2=0.75>0.69,RMSE=28.75<29.44Mg/ha)。区域尺度上,外推森林生物量精度为(R2=0.66,RMSE=19.08 Mg/ha,rRMSE=11.04%),研究区总森林生物量为3.07×107Mg。

(2)在利用随机森林模型估算森林生物量的过程中,本文使用了一种尺度上升的方法,实现了生物量从样方尺度扩大到区域尺度。首先提取了UAV-LiDAR点云中的参数并用随机森林模型来估算样方尺度的森林生物量;将UAV-LiDAR的森林生物量估算结果作为下一步随机森林模型的输入,利用GEDI数据的指标估算得到其足迹点上的森林生物量;离散的GEDI足迹点森林生物量作为外推时随机森林模型的输入,利用Sentinel-1和Landsat 8 OLI影像的多种特征变量估算得到研究区连续的森林生物量数据。

(3)本文基于GEDI L2A高度产品,地形数据、气象数据等辅助数据,利用地理加权回归模型估算得到研究区内的森林树高(R2=0.43,RMSE=6.39m)。在森林树高的基础上,建立不同森林类型的异速生长方程得到森林生物量值。结果发现利用地理加权回归模型和异速生长方程得到的森林生物量比随机森林模型结果偏小,这是由于异速生长方程只关注了GEDI足迹点上最大树高的单棵树而忽略了估算单棵树生物量时造成的累积误差。

Other Abstract

Accurate estimation of forest above ground biomass is essential for regional and global carbon cycling and climate change mitigation. Mountain forests are difficult to conduct human surveys because of the complex terrain. There are large uncertainties in the estimation of both forest structural parameters and biomass. In order to obtain continuous biomass mapping in large scale mountain forest inversion, it is necessary to extrapolate the biomass of sample plots using suitable models with the help of active or passive remote sensing data. The satellite-based LiDAR GEDI, as a large spot active remote sensing technology that can extract 3D structural information of forests, can improve the accuracy of large scale forest biomass estimation. In this paper, we estimated forest biomass in the study area using ground survey data, UAV-LiDAR data, GEDI data, Sentinel-1 and Landsat 8 OLI image data, and evaluated the accuracy by extrapolating the geographically weighted regression and random forest models, respectively, using Qilian Mountains National Park as the study area. The main findings of this study are as follows:

(1) At the sample scale, forest structure parameters and point cloud feature parameters extracted from UAV-LiDAR point clouds can be used as a proxy for ground measurement data to accurately predict forest biomass (R2=0.84, RMSE=44.95Mg/ha, rRMSE=26.97%). GEDI data are effective for improving the accuracy of forest biomass prediction at large scales (R2=0.75>0.69, RMSE=28.75<29.44Mg/ha). At the regional scale, the extrapolated forest biomass accuracy was (R2=0.66, RMSE=19.08 Mg/ha, rRMSE=11.04%), and the total forest biomass in the study area was 3.07×107 Mg.

(2) In estimating forest biomass using the random forest model, an upscaling approach is used to scale up the biomass from the sample plot scale to the regional scale. The parameters in the UAV-LiDAR point cloud were first extracted and used in the random forest model to estimate the forest biomass at the sample plot scale; the forest biomass estimation results of UAV-LiDAR were used as the input of the random forest model in the next step, and the metrics of GEDI data were used to estimate forest biomass at its footprint points was obtained; the forest biomass at discrete GEDI footprint points was used as input to the random forest model at extrapolation, and the continuous forest biomass data of the study area was estimated using multiple characteristic variables from Sentinel-1 and Landsat 8 OLI images.

(3)In this study, based on GEDI L2A height products, topographic data, meteorological data and other auxiliary data, forest tree height in the study area was estimated using a geographically weighted regression model (R2=0.43, RMSE=6.39m). Based on the forest tree height, the forest biomass values were obtained by establishing the allometric equations for different forest types. It was found that the forest biomass obtained using the geographically weighted regression model and the allometric equations were smaller than the random forest model (0.78×107<3.07× 107Mg), which was due to the cumulative error caused by the fact that the anisotropic growth equation only focused on the maximum height of a single tree at the GEDI footprint point and ignored the cumulative error in estimating the biomass of a single tree.

MOST Discipline Catalogue理学 - 地理学 - 地图学与地理信息系统
URL查看原文
Language中文
Other Code262010_220200944570
Document Type学位论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/538544
Collection资源环境学院
Affiliation
兰州大学资源环境学院
Recommended Citation
GB/T 7714
陈思文. 基于GEDI的高寒山地森林生物量估算:以祁连山国家公园为例[D]. 兰州. 兰州大学,2023.
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