Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data

Kaja Kandare,Lorenzo Frizzera,Hans Ørka,Erik Næsset,Michele Dalponte

doi:10.3390/rs9050400

Kaja Kandare, Lorenzo Frizzera + Show 3 more

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https://doi.org/10.3390/rs9050400

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Abstract

Fusion of ALS and hyperspectral data can offer a powerful basis for the discrimination of tree species and enables an accurate prediction of species-specific attributes. In this study, the fused airborne laser scanning (ALS) data and hyperspectral images were used to model and predict the total and species-specific volumes based on three forest inventory approaches, namely the individual tree crown (ITC) approach, the semi-ITC approach, and the area-based approach (ABA). The performances of these inventory approaches were analyzed and compared at the plot level in a complex Alpine forest in Italy. For the ITC and semi-ITC approaches, an ITC delineation algorithm was applied. With the ITC approach, the species-specific volumes were predicted with allometric models for each crown segment and aggregated to the total volume. For the semi-ITC and ABA, a multivariate k-most similar neighbor method was applied to simultaneously predict the total and species-specific volumes using leave-one-out cross-validation at the plot level. In both methods, the ALS and hyperspectral variables were important for volume modeling. The total volume of the ITC, semi-ITC, and ABA resulted in relative root mean square errors (RMSEs) of 25.31%, 17.41%, 30.95% of the mean and systematic errors (mean differences) of 21.59%, −0.27%, and −2.69% of the mean, respectively. The ITC approach achieved high accuracies but large systematic errors for minority species. For majority species, the semi-ITC performed slightly better compared to the ABA, resulting in higher accuracies and smaller systematic errors. The results indicated that the semi-ITC outperformed the two other inventory approaches. To conclude, we suggest that the semi-ITC method is further tested and assessed with attention to its potential in operational forestry applications, especially in cases for which accurate species-specific forest biophysical attributes are needed.

Highlights

Stem volume is one of the most relevant resource attributes of forest inventories
The individual tree crown (ITC) approach reached high accuracies for the volumes of minority species but in general large systematic errors and the area-based approach (ABA) approach resulted in small systematic errors and relatively high accuracies for the dominant species
The ABA is recommended where the dominant species are a key value for management purposes, due to a less demanding collection of field data, relatively high accuracy, and minor systematic error for the dominant species

Summary

Introduction

Stem volume is one of the most relevant resource attributes of forest inventories. In the Nordic countries, conventional forest inventories at various geographical scales have over the past few decades been enhanced by using remotely sensed data such as airborne laser scanning (ALS) and stereo aerial photography [2,3]. It has been shown that for local management planning, data from ALS may reduce. One of the biggest challenges in remote sensing-assisted inventories is the discrimination among tree species [5]. The tree species information is needed for many forest applications, especially to retrieve species-specific forest biophysical properties, such as volume and diameter at breast height (DBH), to derive biodiversity indicators and to plan silvicultural activities and cutting regimes

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