Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory

Jay R Malcolm,Trevor Jones,Sean C Thomas,Braiden Brousseau

doi:10.3390/rs13214297

Abstract

Aerial-photo interpreted inventories of forest resources, including tree species composition, are valuable in forest resource management, but are expensive to create and can be relatively inaccurate. Because of differences among tree species in their spectral properties and seasonal phenologies, it might be possible to improve such forest resource inventory information (FRI) by using it in concert with multispectral satellite information from multiple time periods. We used Sentinel-2 information from nine spectral bands and 12 dates within a two-year period to model multivariate percent tree species composition in >51,000 forest stands in the FRI of south-central Ontario, Canada. Accuracy of random forest (RF) and convolutional neural network (CNN) predictions were tested using species-specific basal area information from 155 0.25-ha field plots. Additionally, we created models using the Sentinel-2 information in concert with the field data and compared the accuracy of these models and the FRI-based models by use of basal areas from a second (13.7-ha) field data set. Based on average R2 values across species in the two field data sets, the Sentinel-FRI models outperformed the FRI, showing 1.5- and 1.7-fold improvements relative to the FRI for RF and 2.1- and 2.2-fold improvements for CNN (mean R2: 0.141–0.169 (FRI); 0.217–0.295 (RF); 0.307–0.352 (CNN)). Models created with the field data performed even better: improvements relative to the FRI were 2.1-fold for RF and 2.8-fold for CNN (mean R2: 0.169 (FRI); 0.356 (RF); 0.469 (CNN)). As predicted, R2 values between FRI- and field-trained predictions were higher than R2 values with the FRI. Of the 21 tree species evaluated, 8 relatively rare species had poor models in all cases. Our multivariate approach allowed us to use more FRI stands in model creation than if we had been restricted to stands dominated by single species and allowed us to map species abundances at higher resolution. It might be possible to improve models further by use of tree stem maps and incorporation of the effects of canopy disturbances.

Highlights

forest resource inventory information (FRI) and model predictions plotted against field observations for eastern hemlock and sugar maple illustrated the tighter relationships obtained for the Sentinel models compared to the FRI, and showed that both models tended to underestimate relatively high abundances (Figure 4)
When we looked at plot-level R2 values among the various data sets, as predicted we observed that R2 values among the various model predictions were higher than R2 values between the model predictions and the FRI
Our multivariate approach allowed us to use a much larger set of FRI stands for model training than if we had relied upon stands heavily dominated by single tree species

Summary

Introduction

Extensive and up-to-date information on forest attributes, including tree species composition, play vital roles in forest management and in many other environmental fields. In Canada, for example, forest resource inventories (FRIs) created from interpreted aerial photos provide stand-level estimates of tree species composition, tree height, tree density, and site quality [1]. These attributes in turn play key roles in tactical and strategic management of wood fibre resources. Species composition is important because it is used to define major forest types, which in turn are used to operationalize many aspects of forest management, including growth-and-yield calculations and forecasts of timber supply [2,3].

Methods

Results

Discussion

Conclusion