Mapping aboveground biomass and its prediction uncertainty using LiDAR and field data, accounting for tree-level allometric and LiDAR model errors

Abstract

Background The increasing availability of remotely sensed data has recently challenged the traditional way of performing forest inventories, and induced an interest in model-based inference. Like traditional design-based inference, model-based inference allows for regional estimates of totals and means, but in addition for wall-to-wall mapping of forest characteristics. Recently Light Detection and Ranging (LiDAR)-based maps of forest attributes have been developed in many countries and been well received by users due to their accurate spatial representation of forest resources. However, the correspondence between such mapping and model-based inference is seldom appreciated. In this study we applied hierarchical model-based inference to produce aboveground biomass maps as well as maps of the corresponding prediction uncertainties with the same spatial resolution. Further, an estimator of mean biomass at regional level, and its uncertainty, was developed to demonstrate how mapping and regional level assessment can be combined within the framework of model-based inference. Results Through a new version of hierarchical model-based estimation, allowing models to be nonlinear, we accounted for uncertainties in both the individual tree-level biomass models and the models linking plot level biomass predictions with LiDAR metrics. In a 5005 km2 large study area in south-central Sweden the predicted aboveground biomass at the level of 18 m ×18 m map units was found to range between 9 and 447 Mg ·ha−1. The corresponding root mean square errors ranged between 10 and 162 Mg ·ha−1. For the entire study region, the mean aboveground biomass was 55 Mg ·ha−1 and the corresponding relative root mean square error 8%. At this level 75% of the mean square error was due to the uncertainty associated with tree-level models. Conclusions Through the proposed method it is possible to link mapping and estimation within the framework of model-based inference. Uncertainties in both tree-level biomass models and models linking plot level biomass with LiDAR data are accounted for, both for the uncertainty maps and the overall estimates. The development of hierarchical model-based inference to handle nonlinear models was an important prerequisite for the study.

A century of national forest inventories – informing past, present and future decisions

“…The hierarchical model based approach (HMB) is a method for propagating uncertainties from multiple regression models when combining multiple remotely sensed data layers. Saarela et al (2020) advanced an analytical HMB method for the important class of nonlinear models. In an ALS-based application, they show the close connection between fine resolution mapping and model-based inference for estimators for areas that aggregate arbitrary numbers of mapped pixels.…”

The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

“…Stand structural variables such as crown canopy, species composition, stem density and basal area can be estimated on the forest stand level using remote sensing data. Therefore, the remote sensing data have great potential to reduce inventory cost and improve estimation accuracy (Steinmann et al 2013;Saarela et al 2020).…”

A Model-Based Volume Estimator that Accounts for Both Land Cover Misclassification and Model Prediction Uncertainty

“…In light of the significance of these roles, accurate and updated information suitable for assessing Remote Sens. 2020, 12 forest resources is needed. National forest inventories (NFIs) were originally motivated by a need for information on forest area, volume, and increment of growing stock and the amount of timber [2].…”

“…Traditional forest inventories have the disadvantage of being expensive, especially in areas with poor road infrastructure [12]. In the last decades, remote sensing (RS) technologies have emerged as an auxiliary data source that alleviates some of this limitation by increasing the precision of inventory estimates and reducing the cost of forest resources assessment [13].…”

“…In this context, reliable methods for propagating the main sources of uncertainty associated with forest predictions have been developed [12,20,21]. As noted previously, forest maps can be used as masks in the design phase to restrict the establishment of field plots and in the estimation phase to restrict the application of the models.…”