标题: Incorporating of spatial effects in forest canopy height mapping using airborne, spaceborne lidar and spatial continuous remote sensing data

作者: Min, WK (Min, Wankun); Chen, YM (Chen, Yumin); Huang, WL (Huang, Wenli); Wilson, JP (Wilson, John P.); Tang, H (Tang, Hao); Guo, MY (Guo, Meiyu); Xu, R (Xu, Rui)

来源出版物: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION  卷: 133  文献号: 104123  DOI: 10.1016/j.jag.2024.104123  Early Access Date: SEP 2024  Published Date: 2024 SEP  

摘要: Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level. However, ALS data has limited spatial coverage, while SLS data has relatively lower estimation accuracy. To this end, we proposed a two-step FCH mapping framework by combining ALS, SLS and auxiliary data. Firstly, using the ALS-derived FCH as reference, the SLS-derived relative height metrics were calibrated at the footprint-level using a regression method. Secondly, to further address the spatial discontinuities in SLS-derived FCH maps, a site-level FCH model was built using a weighted ensemble multi-machine learning model incorporating spatial effects (WEML_SE). The calibrated footprint-level calibration FCH model was used as a reference, and multiple remote sensing data metrics were selected and subjected to important variable selection. Specifically, a spatial adjacency matrix was established based on the spatial locations of SLS footprints, and spatial feature vectors were extracted. The result indicated that the correlation coefficient between the SLS-derived FCH and the ALS-derived FCH (r r = 0.39-0.73, MRE =10.6-25.9 %, and RMSE =2.58-9.37 m) improved at footprint-level (r r = 0.71-0.84, MRE =7.7-18.7 %, RMSE =1.96-7.68 m). Moreover, the WEML_SE exhibited better performance (r r = 0.59-0.75, MRE =8.8-14.8 %, RMSE =2.12-5.4 m) compared to the model without incorporating spatial effects (r r = 0.45-0.71, MRE =9.4-15.8 %, RMSE =2.28-5.89 m). This study emphasizes the advantages of integrating spaceborne and airborne LiDAR data to construct footprint-level estimation of FCH. The proposed WEML_SE model provides new possibilities for accurately generating wall-to-wall estimates of forest biomass.

作者关键词: Forest canopy height; Eigenvector spatial filtering; Global Ecosystem Dynamics Investigation; (GEDI); LiDAR; Weighted ensemble machine learning model

地址: [Min, Wankun; Chen, Yumin; Huang, Wenli; Xu, Rui] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

[Tang, Hao] Natl Univ Singapore, Fac Arts & Social Sci, Dept Geog, Singapore, Singapore.

[Guo, Meiyu] Hong Kong Baptist Univ, Dept Geog, Hong Kong, Peoples R China.

通讯作者地址: Chen, YM; Huang, WL (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

影响因子：7.6