Abstract
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.
Highlights
Forests play an essential role in climate change, ecological balance, and the carbon cycle [1,2,3]
Assuming that we can improve the performance of generalized hierarchical modelbased (GHMB) by replacing the regression model with the Regression kriging (RK) model in the second estimation stage, one general forest parameter estimation and uncertainty estimation framework named RK-GHMB has been developed in this paper
As in many previous studies, Light direction and ranging (LiDAR) data were used as a bridge, combining plot data and optical remote sensing data
Summary
Forests play an essential role in climate change, ecological balance, and the carbon cycle [1,2,3]. Forest canopy height is the main structural parameter of forests. The accurate estimation of canopy height can be beneficial for the modeling of ecosystem services, forest biomass, or other forest parameters [4,5,6]. Ground plot-based measurements can provide limited information about forest resources in terms of spatial coverage, because large areas cannot be surveyed due to topographic factors, the climate, or other reasons [9,10]. In the last few years, more and more studies have been conducted on estimating forest canopy height with remote sensing data [11,12,13,14]. Space-borne remote sensors can obtain data over spatially continuous large areas with a low cost.
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