An allometric area-based approach—a cost-effective method for stand volume estimation based on ALS and NFI data

Abstract

Reliable information concerning stand volume is fundamental to making strategic decisions in sustainable forest management. A variety of remotely sensed data and different inventory methods have been used for the estimation of forest biometric parameters. Particularly, airborne laser scanning (ALS) point clouds are widely used for the estimation of stand volume and forest biomass using an area-based approach (ABA) framework. This method relies on the reference measurements of field plots with the necessary prerequisite of a precise co-registration between ground reference plots and the corresponding ALS samples. In this research, the allometric area-based approach (AABA) is proposed in the context of stand volume estimation of Scots pine (Pinus sylvestris L.) stands. The proposed method does not require detailed information about the coordinates of the field plots. We applied Polish National Forest Inventory data from 9400 circular field plots (400 m2) to develop a plot level stand volume allometric model using two independent variables: top height (TH) and relative spacing index (RSI). The model was developed using the multiple linear regression method with a log–log transformation of variables. The hypothesis was that, the field measurements of TH and RSI could be replaced with corresponding ALS-derived metrics. It was assumed that TH could be represented by the maximum height of the ALS point cloud, while RSI can be calculated based on the number of tree crowns delineated within the ALS-derived canopy height model. Performance of the developed AABA model was compared with the semi-empirical ABASE (with two predictors: TH and RSI) and empirical ABAE (several point cloud metrics as predictors). The models were validated at the plot level using 315 forest management inventory plots (400 m2) and at the stand level using the complete field measurements from 42 Scots pine dominated forest stands in the Milicz forest district (Poland). The AABA model showed a comparable accuracy to the traditional ABA models with relatively high accuracy at the plot (relative root mean square error (RMSE) = 22.8 per cent; R2 = 0.63) and stand levels (RMSE = 17.8 per cent, R2 = 0.65). The proposed novel approach reduces time- and cost-consuming field work required for the classic ABA method, without a significant reduction in the accuracy of stand volume estimations. The AABA is potentially applicable in the context of forest management inventory without the necessity for field measurements at local scale. The transportability of the approach to other species and more complex stands needs to be explored in future studies.

Above-Ground Forest Biomass Estimation using Multispectral LiDAR Data in a Multilayered Coniferous Forest

“…While a variety of remote sensors have been employed for forest biomass estimation (e.g. Multispectral and Hyperspectral Optical sensors, Synthetic Aperture Radar sensors), Light Detection and Ranging (LiDAR) has been identified as the most accurate data source for forest parameters and biomass estimation (Clark, 2011;Socha et al, 2020). LiDAR instruments have become the dominant sensors for accurate AGB estimation, mostly due to their ability to penetrate the forest canopy, providing 3D information for all forest layers (Jiang, Li, Lu, Chen & Wei, 2020;Silva Carlos A. et al, 2016).…”

Above-ground biomass allocation and potential carbon sink of black pine – a case study from southern Poland

“…The advantage of local models is not only to provide unbiased estimation of biomass allocation for specific growth conditions, but they are also suitable to compare biomass allocation in different locations and can be incorporated into more general models designed for the estimation of biomass on regional or national scale. One of the most promising ways to achieve this goal seems to be area base approach (ABA) (Socha et al 2020) for biomass estimation, where local allometric models are combined with data stand features acquired by using remote sensing techniques.…”

PlanetScope Imageries and LiDAR Point Clouds Processing for Automation Land Cover Mapping and Vegetation Assessment of a Reclaimed Sulfur Mine

“…It is sensible to explore the possibility of applying the newest technologies and geodata for this aim. The remote sensing approach is required to obtain a multidisciplinary view of the environmental research and policies related to climate changes due to the forest carbon dynamics information about the vegetation structure, which provides support for studies at degraded lands [2][3][4].…”