An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height

编号 030033003

推送时间 20220214

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height

来源期刊 REMOTE SENSING

期 第330期

发表时间 20220125

关键词 forest;  up-scaling;  regression kriging;  uncertainty;  LiDAR; 

摘要 Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Considering the high cost of acquiring LiDAR data over large areas, we took a two-stage up-scaling approach in estimating forest canopy height and aimed to develop a method for quantifying the uncertainty of the estimation result. Based on the generalized hierarchical model-based (GHMB) estimation framework, a new estimation framework named RK-GHMB that makes use of a geostatistical method (regression kriging, RK) was developed. In this framework, the wall-to-wall forest canopy height and corresponding uncertainty in map unit scale are generated. This study was carried out by integrating plot data, sampled airborne LiDAR data, and wall-to-wall Ziyuan-3 satellite (ZY3) stereo images. The result shows that RK-GHMB can obtain a similar estimation accuracy (r?= 0.92,?MAE?= 1.50 m) to GHMB (r?= 0.92,?MAE?= 1.52 m) with plot-based reference data. For LiDAR-based reference data, the accuracy of RK-GHMB (r?= 0.78,?MAE?= 1.75 m) is higher than that of GHMB (r?= 0.75,?MAE?= 1.85 m). The uncertainties for all map units range from 1.54 to 3.60 m for the RK-GHMB results. The values change between 1.84 and 3.60 m for GHMB. This study demonstrates that this two-stage up-scaling approach can be used to monitor forest canopy height. The proposed RK-GHMB approach considers the spatial autocorrelation of neighboring data in the second modeling stage and can achieve a higher accuracy.

服务人员 付贺龙

服务院士 唐守正

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