Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics

ABSTRACT

Airborne LiDAR has been widely used to map forest inventory attributes at various scales. However, most of the developed models on airborne LiDAR-based forest attribute estimations are specific to a study site and forest type (or species), so it is essential to develop predictive models with excellent generalization capabilities across study sites and forest types for the consistent estimation of forest attributes. In this study, 13 LiDAR-derived metrics, which depicted the three-dimensional structural aspects of stand canopy and had clear forest mensuration and ecology significance, were categorized into three groups (height, density, and vertical structure). A rule-based exhaustive combination was then used to construct 86 multiplicative power formulations consisting of 2–5 predictors for estimating the stand volume and basal area. By calibrating and validating these formulations using data from four forest types in the three study regions, we obtained the 24 best local models. Based on these models we proposed a set of accuracy criteria to determine generalized formulations and models. By applying two selection methods (the mean and mixed data methods), we finally archived the eight best region-generalized models, which could be used for estimating the stand volume and basal area of four forest types across study sites on a province scale. This study highlights the accuracy criteria and procedures for developing generalized formulations and models for consistent estimations of forest inventory attributes using airborne LiDAR data.

KEYWORDS:

• Forest structure
• model generalization
• accuracy criteria
• multiplicative power model
• remote sensing

Acknowledgement

This project is a part of the Fifth Forest Management Inventory Project of Guangxi Zhuang Autonomous Region (5^th FMI-GX, 2017-2020), China. The airborne LiDAR data acquisition and prepossessing and field data measurement were founded by the finance department of the Guangxi Zhuang Autonomous Region. The authors would like to express their sincere gratitude to Chengling Yang and Yao Liang from the Guangxi Forest Inventory and Planning Institute (FIPI-GX) and 120 field crews who worked on the field measurements. The authors also thank Guangxi 3D Remote Sensing Engineering Technology Co., Ltd., Feiyan Aero Remote Sensing Technology Co., Ltd., Zhongke Remote Sensing Technology Group Co., Ltd., and Guangzhou Jiantong Surveying and Mapping Geographic Information Technology Co., Ltd., which were responsible for the acquisition and preprocessing of the airborne LiDAR data utilized herein. In addition, the authors are grateful to Professor Guofan Shao from the Department of Forestry and Natural Resources of Purdue University in West Laffayette, IN, USA, and Dr. Marc Bouvier from UMR TETIS Irstea-Cirad-AgroParisTech/ENGREF, Maison de la Télédétection en Languedoc-Roussillon, 500, rue J.F. Breton BP 5095, 34196 Montpellier Cedex 05, France, for their advice on the name of the generalization model. We acknowledge the anonymous reviewers and the editor for their insightful suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available at https://doi.org/10.57760/sciencedb.j00001.00467

CRediT authorship contribution statement

Chungan Li: Conceptualization, Methodology, Calculation, Data collection and analysis, Writing – original draft and – Review and editing, Project administration, Funding acquisition. Zhongchao Chen:and Xiangbei Zhou Calculation, Writing – original draft. Mei Zhou and Zhen Li: Data collection and analysis, Calculation.

Additional information

Funding

This work received financial support from the Forest Department of Guangxi Zhuang Autonomous Region, China [GXLYKJ2016-001].