Harnessing terrestrial laser scanning to predict understory biomass in temperate mixed forests. (February 2021)

• Record Type: Journal Article
• Authors: Li, Shun; Wang, Tianming; Hou, Zhengyang; Gong, Yinan; Feng, Limin; Ge, Jianping
• Is Part Of: Ecological indicators. Volume 121(2021)
• Publication Date: 2021-02
• Subjects: Terrestrial Laser Scanning (TLS) -- Forest understory -- Biomass mapping -- Herbivory -- Conservation management; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2020.107011
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Efficient TLS procedure was proposed for predicting understory biomass at fine scales. Understory biomass in complex mixed forests was categorized for herb and shrub layers. TLS outperforms manual mensuration on key variables, canopy cover and canopy volume. Parametric TLS models constructed outcompete counterparts requiring manual variables. Promising prospects on investigating the process of herbivore-vegetation interactions. Abstract: Forest understory vegetation plays an important role in providing food, nutrition and habitat for wildlife. The impact of wildlife foraging on forest understory biomass are often subtle processes that are difficult to capture using traditional field measurements. Field measurements are labor-intensive and impractical to cover and detect understory biomass changes at an extensive range with fine spatial resolution, thereby affecting the accuracy of habitat quality and food availability assessment for wildlife. Terrestrial Laser Scanning (TLS) is considered to have potential to improve the accuracy of understory biomass prediction, allowing for detailed monitoring of biomass changes under the influence of wildlife at fine scale. In this study, we first developed an efficient method to predict forest understory biomass by using variables from TLS with regression models, and second, we compared the prediction accuracy between TLS-based variables and field-measured variables. Third, the optimal models with TLS-based variables were applied to map and quantify the effect of herbivores density on understory biomass in temperate forests in northeastern China. Our results demonstrated that TLS-derived data were more accurate than field measurements in predicting understory biomass, that TLS-derived canopy cover yielded the highest herb layer biomass estimation accuracy (R 2 = 0.72, RMSE = 12.73 g/m 2 ), and that TLS-derived vegetation volume obtained the highest accuracy assessment for the shrub layer biomass prediction (R 2 = 0.69, RMSE = 43.64 g/m 2 ). There was a significant difference in the understory herb layer biomass in different deer-density plots, but no significant difference in shrub layer biomass. To quantify biomass changes in different plots, consistent monitoring method is needed, TLS data demonstrated the potential to capture and accurately quantify the biomass variation from forest understory. As tool for monitoring understory, TLS can be used as a supplementary to traditional understory monitoring methods to guide forest management policies and wildlife conservation strategies.