Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Abstract

Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

“…Numerous scholars have substantiated the impact of herbaceous cover on AGB estimation, which is consistent with the findings of this study (Figure 6) (Carreno‐Luengo et al., 2020; Chaparro et al., 2019; Shi et al., 2021). The accuracy of estimating high AGB in forest ecosystems can be impacted by high‐density herbaceous vegetation, which influences vegetation height, vertical structure, and apparent reflectance derived from remote‐sensing data.…”

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth

“…In many studies, there were three typical methods to estimate vegetation cover using remote sensing data, such as the Empirical method, the Spectral Mixture Analysis method and the DPM ( Jiapaer et al., 2011 ). The DPM is a fast and effective method for calculating FVC because it is easy to operate and has performed well ( Shi et al., 2021b ; Hill and Guerschman, 2022 ). The principle of the DPM involves assuming that the reflectance (R) of a pixel consists of two components: the part with vegetation cover (R V ) and the part without vegetation cover (R S ) .…”

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

“…GF series data sets are important component of China High-Resolution Earth Observation System (CHEOS) project sponsored by China National Space Administration (CNSA) [64]. We obtained GF series data sets from the China Centre for Resources Satellite Data and Application (http://www.cresda.com/CN/, last accessed on 2 February 2022) [65]. The GF 1 satellite, launched on 26 April 2013, is the first of China's series of high resolution earth observation satellites operated by China [66].…”